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Sensate Focus (SF) forms a core part of psychosexual therapy (PST) but in many respects is under researched. A literature review identified gaps in research including: how practitioners use language when asking for feedback, in particular about sensations and pleasure, and how they help clients deal with specific distractions and blockages arising from SF exercises. Sally Thorne's Interpretative Description methodology was used for this study, using a social constructionist epistemology and interpretive theoretical perspective. Seven therapists were selected using purposive sampling and participated in semi-structured interviews, analysed using Template Analysis. The results identified two general approaches to dealing with feedback, characterised as "touch-centred" feedback and "broad-based" feedback, though the differences are ones of emphasis rather than a strict demarcation. Therapists are also divided about the value of asking clients about their experience of pleasure in SF exercises. Differences were found in the kinds of techniques used to address distractions and blockages and the extent to which Mindfulness is applied. Recommendations made relate to raising the profile of Mindfulness in training courses, giving greater emphasis to client note-taking after sensate focus exercises, and the need for further research into the language used by therapists when dealing with feedback. LAY SUMMARY: This research was designed to help psychosexual therapists develop a greater understanding of how to process feedback from clients about Sensate Focus exercises. Recommendations include a greater emphasis on teaching Mindfulness in training courses and encouraging clients to make written notes after exercises to inform their feedback with the therapist. [ABSTRACT FROM AUTHOR]

Background: The study determined the proportion of patients with pancreatic adenocarcinoma (PDAC) who had margin-positive disease and no other adverse pathologic findings (APF) using institutional and administrative datasets. Methods: Patients with clinical stage I or II PDAC in the National Cancer Database (NCDB 2010–2020) and those who underwent pancreatectomy at the authors' institution (2010–2021) were identified. Isolated margin positivity (IMP) was defined as a positive surgical margin with no APF (negative nodes, no lymphovascular/perineural invasion). Results: The study included 225 patients from the authors' institution and 23,598 patients from the NCDB. The margin-positive rates were 21.8% and 20.3%, and the IMP rates were 0.4% and 0.5%, respectively. In the institutional cohort, 68.4% of the patients had recurrence, and most of the patients (65.6%) had distant recurrences. The median recurrence-free survival (RFS) was 63.3 months for no APF, not reached for IMP, 14.8 months for negative margins & 1 APF, 20.3 months for positive margins & 2 APFs, and 12.9 months with all APF positive. The patients in the NCDB with IMP had a lower median OS than the patients with no APF (20.5 vs 390 months), but a higher median OS than those with margin positivity plus 1 APF (20.5 vs 18.0 months) or all those with APF positivity (20.5 vs 15.4 months). Based on institutional rates of IMP, any margin positivity, neck margin positivity (NMP), and no APF, the fraction of patients who might benefit from neck margin revision was 1 in 100,000, and those likely to benefit from any margin revision was 1 in 18,500. In the NCDB, those estimated to derive potential benefit from margin revision was 1 in 25,000. Conclusions: Isolated margin positivity in resected PDAC is rare, and most patients experience distant recurrence. Revision of IMP appears unlikely to confer benefit to most patients. [ABSTRACT FROM AUTHOR]

Simple Summary: This research aimed to investigate if adding social determinants of health (SDOH) to predictive models improves major adverse cardiovascular events (MACE) predictions in breast cancer patients, as cardiovascular disease is their leading cause of death. ML models, incorporating SDOH, demographics, risk factors, tumor characteristics, and treatments, were developed and compared. The results showed that including SDOH enhanced ML model performance in forecasting MACEs within two years of breast cancer diagnosis, especially for non-Hispanic Black patients. These findings offer more accurate risk assessments and personalized care insights for breast cancer patients, while also guiding efforts toward achieving healthcare equity. Cardiovascular disease is the leading cause of mortality among breast cancer (BC) patients aged 50 and above. Machine Learning (ML) models are increasingly utilized as prediction tools, and recent evidence suggests that incorporating social determinants of health (SDOH) data can enhance its performance. This study included females ≥ 18 years diagnosed with BC at any stage. The outcomes were the diagnosis and time-to-event of major adverse cardiovascular events (MACEs) within two years following a cancer diagnosis. Covariates encompassed demographics, risk factors, individual and neighborhood-level SDOH, tumor characteristics, and BC treatment. Race-specific and race-agnostic Extreme Gradient Boosting ML models with and without SDOH data were developed and compared based on their C-index. Among 4309 patients, 11.4% experienced a 2-year MACE. The race-agnostic models exhibited a C-index of 0.78 (95% CI 0.76–0.79) and 0.81 (95% CI 0.80–0.82) without and with SDOH data, respectively. In non-Hispanic Black women (NHB; n = 765), models without and with SDOH data achieved a C-index of 0.74 (95% CI 0.72–0.76) and 0.75 (95% CI 0.73–0.78), respectively. Among non-Hispanic White women (n = 3321), models without and with SDOH data yielded a C-index of 0.79 (95% CI 0.77–0.80) and 0.79 (95% CI 0.77–0.80), respectively. In summary, including SDOH data improves the predictive performance of ML models in forecasting 2-year MACE among BC females, particularly within NHB. [ABSTRACT FROM AUTHOR]

PURPOSE: Develop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors. METHODS: The initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve. RESULTS: We included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable. CONCLUSION: Key drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission. New cancer-specific ML models accurately predict 30-day unplanned readmissions in patients with solid tumors. [ABSTRACT FROM AUTHOR]

Improving corn (Zea mays L.) nitrogen (N) rate fertilizer recommendation tools can improve farmers' profits and mitigate N pollution. Numerous approaches have been tested to improve these tools, but to date improvements for predicting economically optimum N rate (EONR) have been modest. This work's objective was to use ensemble learning to improve our estimation of EONR (for a single at‐planting and split N application timing) by combining multiple corn N recommendation tools. The evaluation was conducted using 49 corn N response trials from eight states in the US Corn Belt and three growing seasons (2014–2016). Elastic net and decision tree approaches regressed EONR against three unique tools for each N application timing. Tools used in various combinations included a yield goal method, two soil nitrate tests (pre‐plant and late season), a computer simulation crop model (Maize‐N), and canopy reflectance sensing. Any combination of two or three N recommendation tools improved or maintained performance metrics (R2, root‐mean square error , and number of sites close to EONR). The best results for a single at‐planting recommendation occurred when combining the three at‐planting N recommendation tools (including interactions) with an elastic net regression model. This combined recommendation tool had a significant linear relationship with EONR (R2 = 0.46), an increase of 0.27 over the best tool evaluated alone. Combining multiple tools increased the implementation cost, but it did not reduce profitability and, sometimes, improved profitability. These results show tools can be combined to better match EONR, and thus could aid farmers in improving N management. Core Ideas: Individual corn N recommendation tools poorly estimated EONR (R2 ≤ 0.24) across many US Midwest states.Combining or "ensembling" two or more corn N recommendation tools improved EONR estimation (R2 ≤ 0.46).Both ensembling techniques evaluated proved effective at combining tools. [ABSTRACT FROM AUTHOR]

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is needed (i.e., critical N dilution curve; CNDC) to better understand when plants are N deficient. A few CNDCs have been developed and used for plant N status diagnoses but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y = 34.0x−0.37, where y is critical PNC, and x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y = 36.5x−0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc) based on the French CNDC was significantly related to soil nitrate-N content (R2 = 0.38–0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT but for individual sites ranging from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging to guide specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest. [ABSTRACT FROM AUTHOR]

Background: Patients with certain autoimmune conditions are at a reduced risk of developing breast cancer compared to the general population. Despite this, little is known about outcomes in patients with breast cancer who have a concurrent autoimmune diagnosis. Methods: This study compared differences in outcomes between women with breast cancer who had or did not have an autoimmune diagnosis. The SEER‐Medicare databases (2007–2014) were used to identify patients with breast cancer and diagnosis codes were used to identify those with an autoimmune disorder. Results: The studied autoimmune diseases had a prevalence of 27% among the 137,324 patients with breast cancer. Autoimmune disease was associated with significantly longer overall survival (OS) and significantly lower cancer‐specific mortality (CSM) among stage IV breast cancer patients (p < 0.0001). After controlling for the effects of age, race, chronic kideny disease, chemotherapy, and radiation therapy autoimmune disease was still predictive of improved OS (HR: 1.45, 95% CI: 1.35–1.55, p < 0.0001) and CSM (HR: 1.40, 95% CI: 1.29–1.5, p < 0.0001). By contrast, in patients with stage I–III breast cancer, the presence of an autoimmune diagnosis was associated with a lower OS (p < 0.0001, p < 0.0001, and p = 0.026, respectively), compared to patients without autoimmune disease. Conclusions: We found a higher prevalence of rheumatoid arthritis, Crohn's disease, ulcerative colitis, and systemic lupus erythematosus in patients with breast cancer compared to age matched cohorts in the general population. The presence of an autoimmune diagnosis was associated with a lower OS in stages I–III breast cancer and improved OS and CSM in patients with stage IV disease. These results suggest that anti‐tumor immunity plays an important role in late stage breast cancer and could potentially be exploited to improve the effectiveness of immunotherapy. [ABSTRACT FROM AUTHOR]

Background: There is a male predominance of acute myeloid leukemia (AML) incidence, but survival data are conflicting. The objective of this study is to carry out a comprehensive analysis of sex differences in AML, and to investigate the impact of sex disparities in survival. Methods: The cohort included patients ≥18 years diagnosed with AML (2010–2022). Demographics, treatment patterns, treatment adverse events, and survival were analyzed. The population was described and compared by sex, and sex‐based risks and associations were obtained via Cox proportional‐hazards regression. Results: In total, 1020 AML patients were analyzed (57.4% males), with lower risk of death for females (aHR = 0.41, 95% CI 0.26–0.66). Among females, BMT (aHR = 0.51, 95% CI 0.27–0.97), hospitalization record (aHR = 0.65, 95%CI 0.45–0.93), and higher appointment completion rates (aHR = 0.98, 95% CI 0.98–0.98) were associated with lower risk of death. Overall, and similarly in males, higher age at diagnosis (aHR = 1.03, 95% CI 1.02–1.04) and a TP53 mutation (aHR = 2.24, 95% CI 1.69–2.97) were associated with higher risk of death. Conclusion: Sex differences exist in both AML incidence and overall survival. Treatment and health care factors should be addressed by caregivers and public policies developed to reduce mortality rates and mitigate existing sex differences. [ABSTRACT FROM AUTHOR]

Objective: To determine the frequency of depression or anxiety preceding a diagnosis of pancreatic cancer (PC). Further, to examine the association of PC‐associated depression or anxiety with treatment compliance and survival. Methods: 856 patients with PC from a single institution were identified using International Classification of Diseases (ICD) codes. For each case, two non‐cancer age‐ and sex‐matched controls were included. Dates of depression or anxiety diagnosis identified using ICD codes were compared to the date of PC diagnosis. The medical record was queried to further explore psychiatric symptoms. Multivariable analyses were performed to examine if prediagnosis depression or anxiety was associated with receipt of treatment or survival. Results: A greater proportion of patients with PC experienced depression or anxiety in the year preceding diagnosis than the overall frequency in controls (4.6% vs. 2.6%, p = 0.005) based on ICD codes. Patients with PC exhibited signs of prodromal depression or anxiety based on ICD codes, clinical documentation of psychiatric symptoms, or initiation of new psychiatric medications more often than controls (20.7% vs. 6.7%, p < 0.001). Prediagnosis depression or anxiety was associated with a reduced likelihood of receiving chemotherapy (OR = 0.58, p = 0.04). There was an associated decrease in overall survival among patients with metastatic disease who experienced depression or anxiety before PC diagnosis (HR = 1.32, p = 0.04). Conclusions: The frequency of depression or anxiety among patients with PC was higher than the general population. Prediagnosis psychiatric symptoms were associated with reduced chemotherapy utilization and worse overall survival. Thus, timely identification and treatment of these symptoms may improve outcomes. [ABSTRACT FROM AUTHOR]

413 Background: Current health information systems lack a robust framework for longitudinal integration of internal experiences and external evidence into existing workflows. The result is labor intensive, error-prone and fragmented systems plagued with process gaps. Learning health systems (LHS) can be a potential solution to address these gaps. We created and implemented a novel LHS framework in radiation oncology (RO). We hypothesize that this framework can be leveraged to identify and implement opportunities for improvement using a patient-oriented endpoint of time from radiation simulation to treatment start (RSTS). Methods: An RO-LHS was developed through a collaboration between an academic, multi-facility radiation oncology department and an industry partner (Medlever). The end-to-end clinical workflow was embedded into the Medlever platform following an interdisciplinary, iterative analysis with representatives from key teams (clerical, finance and clinical). A novel nomenclature was created to label standardized, customizable templates, which choreograph logic-driven tasks that cover all sub-steps of RSTS. The primary endpoint of RSTS was compared pre- and post-implementation, stratified by key variables, such as facility, staff member, and radiation treatment modality. Results: The iterative workflow analysis identified >700 variables and decision points necessary for >200 cancer and benign conditions routinely managed in RO. >450 treatment protocols were templated.180 logic-driven tasks were created and linked to key parameters, resulting in a choregraphed precision workflow for each patient. Using the first iteration of the build, a total of 512 patients were managed during a 50-day period (03/01/2024-04/20/2024) and were compared to 2875 patients treated prior to implementation. Post-implementation, RSTS was shortened by 36.4% (p<0.001). We identified significant differences in RSTS by radiation modalities (protons vs all others), across facilities, and by providers (p<0.01 for all). Analysis of fractional domain of RSTS, 56% of the time was driven by physician-directed tasks, while 44% was comprised of physics and dosimetry-driven work. Physician-directed tasks had the greatest heterogeneity in time to completion. The biggest variation was noted for the contouring completion task. Significant differences were identified by provider (p<0.001) in nearly each step of the process, including steps not directly tied to physician's work. Conclusions: Through an inter-disciplinary implementation of the first iteration of a LHS in our RO department, we have significantly shortened RSTS. The implementation and analysis has also enabled us to better understand underlying heterogeneities and identify further opportunities for optimization of RSTS. An LHS framework provides a generalizable solution that could be adapted in other RO clinics. [ABSTRACT FROM AUTHOR]

Background: Objective measures of post-pancreatectomy weight change for pancreatic ductal adenocarcinoma (PDAC) have not been extensively studied for long-term outcomes. We used weight measurements in our institutional medical record to analyze trends in post-pancreatectomy weight and determine the association with disease status. Methods: Pancreatectomies for PDAC (n = 315) and benign indications (n = 111) were identified. Preoperative baseline, minimum postoperative (Min #1), and subsequent postoperative maximum (Max) weights were abstracted. Multivariable Cox hazards regression was conducted to analyze the association between weight change and survival. Results: Median weight loss postoperatively in each group was > 20 lbs. PDAC patients gained 10 lbs after Min #1 compared to 15 lbs in the benign cohort (p < 0.001). Few patients returned to their preoperative weight (29.8% PDAC vs. 40.5% benign, p = 0.04). Patients with early PDAC recurrence (< 13 months) lost more weight (18.0% vs. 13.3% vs. 10.9%, p < 0.001) and gained less weight (2.1% vs. 12.0% vs. 7.9%, p < 0.001) compared with those with late cancer recurrence (≥ 13 months) or no evidence of active disease, respectively. PDAC patients lost 11.2 lbs in the year preceding recurrence diagnosis. Weight loss was not associated with survival; however, weight gain was associated with improved survival. Conclusions: Resections for PDAC are complicated by a similar degree of weight loss as patients with benign disease, and there is no association with survival. However, failure to gain weight is especially ominous. Weight loss after weight recovery foreshadows disease recurrence. These data suggest that rigorous weight tracking is an untapped surveillance strategy in patients with PDAC. [ABSTRACT FROM AUTHOR]

The focus of this paper is the relationship between soil health and global food security. We discuss the role of climate change on food security and the role of farmer profitability in promoting soil health. Specific objectives were to (a) consider the role that U.S. agricultural production plays in global food security, (b) consider key mechanisms by which soil health impacts U.S. agricultural production, and (c) offer a comprehensive strategy to scale adoption of soil health systems. Based on the Food and Agriculture Organization's (FAO) Food Price Index, U.S. commodity exports influence global food prices, linking U.S. crop production to food security. Soil health affects food security through crop yield, crop yield resilience, and farmer profitability. Results relating soil health and crop yield are complicated by biophysical complexity, lack of standardized research practices, and the scale of research trials. Studies of the relationship between yield resilience and soil health are similarly inconclusive. Despite mixed research results, interviews with 100 U.S. farmers found over half reported increased crop yield due to soil health management systems, and most reported more resilient yields. Partial budget analysis of production information from 100 interviews found using soil health management systems increased net farm income (in 2020 U.S. dollars) by US$127.61 ha–1 ($51.66 acre–1) for maize (Zea mays L.) and $110.84 ha–1 ($44.87 acre–1) for soybean [Glycine max (L.) Merr.]. We detail a path to increase adoption of soil health systems so healthy soils can serve as the foundation for producing food, mitigating climate change, and increasing farmer profitability. Core Ideas: Commodity exports influence food prices, linking U.S. crop production to food security.U.S. agricultural production and exports must not occur at the expense of soil health.Soil health benefits for food security require adoption of soil health systems at scale.A comprehensive strategy to achieve adoption of soil health systems at scale is offered. [ABSTRACT FROM AUTHOR]

Background: Pancreatic ductal adenocarcinoma (PDAC) has the worst survival of common cancers, partly because there are no reliable early detection tests. Unintentional weight loss (≥ 5% decrease from baseline) has been linked to PDAC, but the frequency and severity of weight loss using objective measures, and its relationship to prognosis, have not been well characterized. Methods: We identified 390 patients with PDAC (all stages) and two or more prediagnosis weights in the electronic medical record. Percentage weight loss in the 365 and 180 days preceding diagnosis was calculated. Results were compared with raw weights of age- and sex-matched non-cancer controls (n = 780). Odds ratios for PDAC were calculated using conditional logistic regression. Cox proportional hazards models were used for survival. Results: Within 1 year of diagnosis, more PDAC patients lost ≥ 5% weight relative to controls (74.9% vs. 11.2%; p < 0.001), with a median weight loss of 14.2 versus 2.9 lbs. The odds ratio for PDAC comparing weight loss within 1 year of 5 to < 10% was 10.30 (p < 0.001) and 77.82 for ≥ 10% (p < 0.001), compared with stable weight. Weight loss prior to diagnosis was also associated with early-stage PDAC. PDAC cases with ≥ 10% prediagnosis weight loss had worse survival compared with stable weights (hazard ratio [HR] 1.60; p = 0.01). Greater prediagnosis weight loss was associated with poor survival after pancreatectomy (5 to < 10% vs. < 5%, HR 2.40, p = 0.03; ≥ 10% vs. < 5%, HR 2.59, p = 0.03). Conclusions: Diagnosis of PDAC is preceded by unintentional weight loss in the majority of patients, even at an early stage. Greater prediagnosis weight loss severity is also associated with poor postoperative survival. [ABSTRACT FROM AUTHOR]

Improving corn (Zeamays L.) N managementis pertinent to economic and environmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public-industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site-years across eight states over the 2014-2016 growing seasons with many soil, plant, and weather related measurements. This note provides the data (found in supplemental files), outlines the data, summarizes key findings, and highlights the strengths and weakness for those who wish to use this dataset. [ABSTRACT FROM AUTHOR]

OBJECTIVES: COVID-19 created unexpected delays in oncologic treatment. This study sought to assess the volume of missed cancer-related services due to the pandemic. METHODS: This case-controlled trial evaluated more than 345,000 oncologic clinic, lab, and radiation appointments from January 1, 2019, through December 31, 2020, and surgery appointments from January 1, 2019, through October 31, 2020. All patients at the Seidman Cancer Center with a cancer diagnosis based on a comprehensive list of 2178 International Classification of Diseases, Ninth Edition (ICD-9) and ICD-10 codes were included in the analysis. Subgroup analyses based on age, race, and sex were also performed. RESULTS: Clinic, lab, and surgical visit cancellations increased by 4.20% (P <.001), 4.84% (P <.001), and 5.22% (P <.001), respectively. In the first 10 months of 2020, there were 703 (9.2%) fewer surgeries compared with the same time period in 2019. The following cancellation rates peaked in March 2020: clinic visits (26.53%), labs (43.66%), surgery (34.00%). Radiation oncology (12.53%) cancellations peaked in April 2020. Prior to the emergence of COVID-19, the group aged 0 to 39 years had the highest clinic cancellation rate (17.85%) compared with patients aged 40 to 64 years (15.95%) and 65 years and older (14.52%; P <.001). Men cancelled (15.63%) significantly more often than women (14.93%; P <.001) in 2019. This reversed during the pandemic: Women (19.56%) cancelled more frequently than men (19.20%; P <.036). CONCLUSIONS: There was a large increase in cancelled oncologic care in 2020, which has implications for delayed diagnosis and treatment. This was especially true for patients older than 65 years and for women. These delays could result in patients presenting with more advanced disease, complicating morbidities, and ultimately worse long-term outcomes. [ABSTRACT FROM AUTHOR]

PURPOSE Human papilloma virus–positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC), diagnosed with p16 immunohistochemistry, is associated with favorable prognosis; however, this connection was established using European American (EA)–skewed populations. The impact of p16/human papillomavirus status on outcomes in African American (AA) OPSCC patients remains to be settled. In this study, we determine the association between cancer disparity and p16 status in an OPSCC cohort controlling for time to treatment initiation (TTI), a surrogate for medical care access. MATERIALS AND METHODS We analyzed data from all patients diagnosed with OPSCC (N = 440) between 2010 and 2017, who received treatment at our academic medical center. Associations between age, disease stage, sex, p16 status, race, TTI, and overall survival (OS) were investigated. RESULTS TTI was similar between AA and EA OPSCC patients in our p161 (P= .291) or p162 (P = .715) cohorts. Among p16+ OPSCC patients, the median OS was . 8.65 years for EA patients compared with 5.038 years (95% CI, 2.019 to 5.30; P = .003, log-rank) for AA patients. For p162 patients, the median OS was >.74 years (95% CI, 3.32 to 6.99) for EA patients and 1.85 years (95% CI, 0.978 to 4.50; P= .03, log-rank) for AA patients. Multivariate Cox regression analysis showed that race was an independent prognostic biomarker and the most impactful co-variate for OS (hazard ratio, 0.40; 95% CI, 0.00 to 0.69; P = .001). CONCLUSION Our work showed that AAs with p16+OPSCC have surprisingly poor clinical outcomes and are thus poor candidates for treatment de-escalation regimens. Caution should be exercised when extending clinical guidelines based on EA-majority studies to non-EA populations. [ABSTRACT FROM AUTHOR]

Improving corn (Zea mays L.) N fertilizer rate recommendation tools is necessary for improving farmers' profits and minimizing N pollution. Research has repeatedly shown that weather and soil factors influence available N and crop N need. Adjusting available corn N recommendation tools with soil and weather measurements could improve farmers' ability to manage N. The aim of this research was to improve publicly available N recommendation tools with site-specific soil and weather measurements. Information from 49 site-years of N response trials in the U.S. Midwest was used to evaluate 21 rate recommendation tools for a single (at-planting) and split (at-planting + sidedress) N application. Using elastic net and decision tree algorithms, the difference between each tool's N recommendation and the economically optimum nitrogen rate (EONR) was modeled against soil and weather measurements. The model's predicted values were used to adjust the tools. Unadjusted the best performing tool had r² = .24; after adjustment, the best performing tool had r² = .57. Overall tool improvement was modest and sometimes required many additional inputs. Using weather measurements (e.g., evenness of rainfall or abundant and well-distributed rainfall) helped increase N recommendations by accounting for N loss while soil measurements (e.g., pH and total C) helped decrease N recommendations when there was sufficient available soil N. This investigation showed that incorporating soil and weather measurements is a viable approach for improving corn N recommendation tools regionally; but even with adjustments, tools still have room for additional improvement. [ABSTRACT FROM AUTHOR]

Splitting the N application into two or more timings may improve corn (Zea mays L.) grain yield and N recovery relative to a single‐N application. A 49 site‐year study across eight U.S. Midwestern states compared the effect of an at‐planting (single‐N application) and two split‐N applications [45 (45+SD) or 90 kg N ha−1 (90+SD) at planting with the remainder of the total rate (180 or 270 kg N ha−1) applied at V9]. For split‐N applications, soil and plant responses were similar between 45+SD and 90+SD 93–98% of the time, indicating the at‐planting N rate of 45 kg N ha−1 may be all that is needed in most cropping scenarios. Splitting the N application compared to a single‐N application changed soil NO3–N at VT and post‐harvest <35% of the time and plant N uptake and grain yield <15% of the time. Split‐N applications had greater grain yield in areas with uniform precipitation around the sidedress timing (Shannon Diversity Index >0.56–0.59) to incorporate N in the root zone, and in coarse‐textured soil (sand content >4–10%) that had greater potential for N loss. Single‐N applications produced greater grain yield in soils with more total N (>2.1–2.4 g kg−1) to support N mineralization and greater cation exchange capacity (CEC) (> 27–31 cmolc kg−1), silt content (>66–74%), or clay content (>24–37%) to improve nutrient and water retention. Decisions on nitrogen application timing should be made based on soil parameters and typical weather conditions around the sidedress timing. [ABSTRACT FROM AUTHOR]

Anaerobic potentially mineralizable nitrogen (PMN) combined with preplant nitrate test (PPNT) or pre‐sidedress nitrate test (PSNT) may improve corn (Zea mays L.) N management. Forty‐nine corn N response studies were conducted across the U.S. Midwest to evaluate the capacity of PPNT and PSNT to predict grain yield, N uptake, and economic optimal N rate (EONR) when adjusted by soil sampling depth, soil texture, temperature, PMN, and initial NH4–N from PMN analysis. Pre‐plant soil samples were obtained for PPNT (0‐ to 30‐, 30‐ to 60‐, 60‐ to 90‐cm depths) and PMN (0‐ to 30‐cm depth) before corn planting and N fertilization. In‐season soil samples were obtained at the V5 corn development stage for PSNT (0‐ to 30‐, 30‐ to 60‐cm depths) at 0 kg N ha−1 at‐planting rate and for PMN when 0 and 180 kg N ha−1 was applied at planting. Grain yield, N uptake, and EONR were best predicted when separating soils by texture or sites by annual growing degree‐days and including PMN and initial NH4–N with either NO3–N test. Using PSNT (mean R2 =.30)‐instead of PPNT (mean R2 =.19)‐based models normally increased predictability of corn agronomic variables by a mean of 11%. Including PMN and initial NH4–N with PPNT or PSNT only marginally improved predictability of grain yield, N uptake, and EONR (R2 increase ≤.33; mean R2 =.35). Therefore, including PMN with PPNT or PSNT is not suggested as a tool to improve N fertilizer management in the U.S. Midwest. [ABSTRACT FROM AUTHOR]

The anaerobic potentially mineralizable N (PMN) test combined with the preplant (PPNT) and presidedress (PSNT) nitrate tests may improve corn (Zea mays L.) N fertilization predictions. Forty‐nine corn N response experiments (mostly corn following soybean [Glycine max (L.) Merr.]) were conducted in the U.S. Midwest from 2014–2016 to evaluate the ability of the PPNT and PSNT to predict corn relative yield (RY) and N fertilizer over‐ and under‐application rates when adjusted by PMN. Before planting and N fertilization, PPNT (0–30, 30–60, and 60–90 cm) and PMN (0–30 cm) samples were obtained. In‐season soil samples were obtained at the V5 development stage for PSNT (0–30, 30–60 cm) in all N rate treatments and PMN (0–30 cm) in only the 0 and 180 kg N ha−1 preplant N treatments. Increasing NO3–N sampling depths beyond 30 cm with or without PMN improved RY predictability marginally (R2 increase up to 0.20) and reduced over‐ and under‐application frequencies up to 14%. Including PMN (preplant only) with PPNT or PSNT improved RY predictability minimally (R2 increase up to 0.10) only for coarse‐ and medium‐textured soils, but N fertilizer over‐ and under‐application frequencies were not substantially reduced (≤12%). These marginal improvements in RY predictability and N fertilizer over‐ and under‐application frequencies, regardless of the variables used (e.g., fertilization, sampling depth, soil texture, and growing degree‐day categories), demonstrate that including PMN with soil NO3–N alone does not improve corn N fertilization need predictions enough to recommend their use. [ABSTRACT FROM AUTHOR]

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received < 183 mm of precipitation or < 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios > 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content > 9.5%, total C < 24.2 g kg−1, soil organic matter (SOM) < 3.9 g kg−1, or C to N ratios < 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management. [ABSTRACT FROM AUTHOR]

Determining which corn (Zea mays L.) N fertilizer rate recommendation tools best predict crop N need would be valuable for maximizing profits and minimizing environmental consequences. Simultaneous comparisons of multiple tools across various environmental conditions have been limited. The objectives of this research were to evaluate the performance of publicly‐available N fertilizer recommendation tools across diverse soil and weather conditions for: (i) prescribing N rates for planting and split‐fertilizer applications, and (ii) economic and environmental effects. Corn N‐response trials using standardized methods were conducted at 49 sites, spanning eight US Midwest states and three growing seasons. Nitrogen applications included eight rates in 45 kg N ha−1 increments all at‐planting and matching rates with 45 kg N ha−1 at‐planting plus at the V9 development stage. Tool performances were compared to the economically optimal N rate (EONR). Over this large geographic region, only 10 of 31 recommendation tools (mainly soil nitrate tests) produced N rate recommendations that weakly correlated to EONR (P ≤.10; r2 ≤.20). With other metrics of performance, the Maximum Return to N (MRTN) soil nitrate tests, and canopy reflectance sensing came close to matching EONR. Economically, all tools but the Maize‐N crop growth model had similar returns compared to EONR. Environmentally, yield goal based tools resulted in the highest environmental costs. Results show that no tool was universally reliable over this study's diverse growing environments, suggesting that additional tool development is needed to better represent N inputs and crop utilization at a larger regional level. [ABSTRACT FROM AUTHOR]

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha-1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4--N included with PMNan. Soil was sampled (0-30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha-1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R² ≤ 0.08; RMSE, ≥67 kg N ha-1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4--N as separate explanatory variables improved EONR predictability (11-20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R² increased ≤0.18, and RMSE was reduced ≤7 kg N ha-1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4--N were relatively low (R² ≤ 0.33; RMSE ≥ 68 kg N ha-1) compared with other tools for N fertilizer recommendations. [ABSTRACT FROM AUTHOR]

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0.30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha-1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 < 0.40), followed by temperature (R2 < 0.20) and precipitation (R2 < 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by .10%. Including soil and weather in the model greatly increased PMNan predictability (R2 < 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied.

Determination of in-season N requirement for corn (Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWCwt) and a ratio of in-season rainfall to AWCwt (RAWCwt), were created to capture the impact of soil hydrology on N dynamics. Four ML models--linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)--were assessed and validated using "leave-one-location-out" (LOLO) and "leave-one-year-out" (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is ~70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha-1; R² = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in-season soil hydrological status seems essential for success in modeling N demand. [ABSTRACT FROM AUTHOR]

Theoretically, N losses are reduced by synchronizing fertilizer additions with plant uptake requirements. We investigated the impacts of supplemental, late-season N applications on nitrogen fertilizer recovery efficiency (NRE), and N accumulation and partitioning in maize (Zea mays L.) at silking (R1) and physiological maturity (R6). Also tested was whether modern hybrids responded differently to split-N applications compared to hybrids released 20 yr ago. We compared 3 to 4 N rates ranging from 0 to 245 kg N ha-1 applied either in a single application at V3, or split with the last 45 kg N ha-1 delayed until V12, over 3 yr. Two newer hybrids (2012 and 2014) and two 1990 era hybrids (1991 and 1995) were compared at all N treatment combinations. Additional plant N accumulation following latesplit N applications was already apparent at R1, particularly in stems. Late-split N application increased both whole-plant R6 N accumulation and NRE through higher post-silking N uptake. However, these benefits were rarely accompanied by increased grain yields. We found little evidence of differential hybrid responses to N rate or timing treatments. Principal component analysis revealed that the most consistent predictor of high postsilking N uptake was lower N remobilization during grain-fill; these had a strong inverse relationship. Therefore, gains from N management programs aiming to increase post-silking N uptake are most likely in environments where whole-plant N accumulation at R1 (and, consequently, potential N remobilization) is reduced. Further studies of late-split N approaches are most warranted at lower total-season N rates. [ABSTRACT FROM AUTHOR]

Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public-domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project's anticipated value. The project was initiated in a partnership between eight U.S. Midwest land-grant universities, USDA-ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools. [ABSTRACT FROM AUTHOR]

Capturing the variability in soil-landscape properties is a challenge for grain producers attempting to integrate spatial information into the decision process of precision agriculture (PA). Digital soil maps (DSMs) use traditional soil survey information and can be the basis for PA subfield delineation (e.g., management zones). However, public soil survey maps provide only general descriptions of soil-landscape features. Therefore, improved DSMs are needed that use high-resolution data that more precisely model soil-landscape characteristics. Additionally, reliable methods are needed to validate DSM products for PA. The objective of this study was to validate with corn (Zea mays L.) yield data the performance of a new DSM product, termed Environmental Response Unit (ERU), compared with the USDA Soil Survey Geographic (SSURGO) soil map. The ERU was developed by integrating SSURGO information with high-resolution elevation data. For validation, corn yield maps were collected and corrected for common data collection errors from 409 fields across Indiana, Iowa, Minnesota, and Nebraska in 2010 to 2012. Reductions in the area-weighted variance (Rv) of corn yield for ERU and SSURGO were calculated relative to the whole-field variance. The average Rv across all site-years for SSURGO and ERU was 16 and 25%, respectively, which equated to a 57% higher median yield variance reduction with ERU over SSURGO. This variance reduction technique showed the potential of ERU as an improved model better representing soil-landscape properties that impact corn yield. This research also has application potential for determining the success of a DSM for identifying management zones in PA. [ABSTRACT FROM AUTHOR]

Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is too coarse to fully understand the spatial dynamics of crop status. The objective of this study was to downscale Landsat 7 (L7) reflectance from the native resolution of 30 × 30 m to that typical of yield maps (ca. 5 × 5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat ( Triticum aestivum L.) in the 2002-2003 growing season. Geospatial yield measurements were available (1 per ca. 20 m). Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data were acquired (resolution <5 × 5 m) to characterize soil spatial variability. Geographically-weighted regressions were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30 × 30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 < R < 0.94). Maps for regression parameters and intercept were obtained at 30 × 30 m and used to estimate the L7 reflectance at 5 × 5 m resolution. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5 × 5 m estimated reflectance showed stronger correlations (average increase in explained variance = 3.2 %) with yield than at the 30 × 30 m resolution. Land resource managers, producers, agriculture consultants, extension specialists and Natural Resource Conservation Service field staff would be the beneficiaries of downscaled L7 reflectance data. [ABSTRACT FROM AUTHOR]

The article discusses the 17th-century stage adaptation by John Dryden and William Davenant of the play "The Tempest" by William Shakespeare. Particular focus is given to the adaptation's reception history. According to the author, its popularity was due in part to the way in which it demonstrated natural philosophical ideas associated with the Royal Society of London, both in form and content. Details on language use and political obligation are also presented.

Integrating soil-based management zones (MZ) with crop-based active canopy sensors to direct spatially variable N applications has been proposed for improving N fertilizer management of corn (Zea mays L.). Analyses are needed to evaluate relationships between canopy sensing and soil-based MZ and their combined potential to improve N management. The objectives of this study were to: (i) identify soil variables related to in-season crop canopy reflectance and yield and use these variables to delineate MZ for N fertilizer management; and (ii) compare corn yield response to different N fertilizer treatments for different MZ. Eight N rates (0-274 kg N ha-1 in 39 kg ha-1 increments) were applied in replicated small plots across six irrigated fields in 2007 to 2008 throughout central Nebraska. Soil variables evaluated for MZ delineation included: apparent soil electrical conductivity (ECa), soil optical reflectance, and landscape topography. Crop response to N was determined via active sensor assessments of in-season canopy reflectance (chlorophyll index, CI590) and grain yield. Relationships between soil and topography data and crop performance were evaluated, with selected soil variables used to delineate two MZ within four of the six fields. Economic benefits to N application according to soil-based MZ were observed in fields with silty clay loam and silt loam soils with substantial relief and eroded slopes. Sensor-based algorithms may need to be adjusted according to MZ to account for differences in crop N response. [ABSTRACT FROM AUTHOR]

Much of the previous evaluation of active crop canopy sensors for in-season assessment of crop N status has occurred in environments without water stress. The impact of concurrent water and N stress on the use of active crop canopy sensors for in-season N management is unknown. The objective of this study was to evaluate the performance of various spectral indices for sensing N status of corn (Zea mays L.), where spectral variability might be confounded by water-induced variations in crop reflectance. The study was conducted in 2009 and 2010 with experimental treatments of irrigation level (100 and 70% evapotranspiration [ET]), previous crop {corn-corn or soybean [Glycine max (L.) Merr.]-corn} and N fertilizer rate (0, 75, 150, and 225 kg N ha-1). Crop canopy reflectance was measured from V11 to R4 stage using two active sensors-a two band (880 and 590 nm) and a three band (760, 720, and 670 nm). Among the indices, the vegetation index described by near infrared minus red edge divided by near infrared minus red (DATT) and Meris terrestrial chlorophyll index (MTCI) were the least affected by water stress, with good ability to differentiate N rate with both previous crops. The chlorophyll index using amber band (CI), normalized difference vegetation index using red edge band (NDVI_RE) and the normalized vegetationi using the red band (NDVI_Red) showed more variation due to water supply, and had only moderate ability to differentiate N rates. [ABSTRACT FROM AUTHOR]

This essay combines scientific history and the history of theater to explain how the early experimental community tried to find ways to distinguish its work from inherited models of theatricality and knowledge, and how playwright Thomas Shadwell worked to thwart these goals. He tried to thwart these goals with a satiric analysis of scientific modernity. Shadwell's play "The Virtuoso" is examined.

The article reviews a revival of Margaret Cavendish's play "Bell In Campo," adapted and directed by Ian Gledhill and starring John Skevington, performed at the Seventh Biennial International Conference of the Margaret Cavendish Society at Bolsover Castle in Derbyshire, England, on July 1, 2007.

This essay argues that The Alchemist played an important but largely unrecognized part in the formation of early modern science. It shows how Jonson's innovative combination of alchemical content and neoclassical form produced a model of space, time, and dexterity useful for the development of laboratory experience. At the same time, the play demonstrates how new ideas about what a stage was and what it could do created a legacy of ambivalence in the development of the (semi-)public laboratory. Reading Jonson's play as exemplary, this essay will also indicate why we ought to consider early modern drama more generally as an important conceptual source of the protocols of experimental natural philosophy. [ABSTRACT FROM AUTHOR]

Reviews the theatrical production "The Convent of Pleasure," directed by Gweno Williams and performed at Convocation Hall of the McMaster University in Hamilton, Ontario on July 9, 2005.

The purpose of this study was to examine the effects of different nutrient (carbon, nitrogen, oxygen) concentrations on the microbial activity and community structure in membrane-aerated biofilms (MABs). MABs were grown under well-defined conditions of fluid flow, substrate concentration, and membrane oxygen partial pressure. Biofilms were then removed and thin-sliced using a cryostat/microtome parallel to the membrane. Individual slices were analyzed for changes with depth in biomass density, respiratory activity, and the population densities of ammonia-oxidizing and denitrifying bacteria populations. Oxygen-sensing microelectrodes were used to determine the depth of oxygen penetration into each biofilm. Our results demonstrated that ammonia-oxidizing bacteria grow near the membrane, while denitrifying bacteria grow a substantial distance from the membrane. However, nitrifying and denitrifying bacteria did not grow simultaneously when organic concentrations became too high or ammonia concentrations became too low. In conclusion, membrane-aerated biofilms exhibit substantial stratification with respect to community structure and activity. A fundamental understanding of the factors that control this stratification will help optimize the performance of full-scale membrane-aerated biofilm reactors for wastewater treatment. [ABSTRACT FROM AUTHOR]

Development of corn (Zea mays L.) germplasm tolerant to water stress will be vital to sustaining corn-based farming in the U.S. Great Plains. In a companion 4-yr field study near Shelton, NE, we found that 12 hybrids displayed differential agronomic responses to varying water levels, with tolerant hybrids yielding from 27 to 42% more than susceptible hybrids under stress while yielding similarly under no stress. The objective of this study was to determine if chlorophyll fluorescence (CF) measurements could be used to distinguish tolerant from susceptible hybrids. Leaf temperature (LT) and two CF parameters (ΦPSII, photosystem II quantum efficiency, and ETR, electron transport rate) were measured on three postflowering dates in 2001 using a fluorometer on a subset of original treatments involving two tolerant and susceptible hybrids grown under deficit and adequate water. Water effects were observed on only one date; LT was 2.5°C warmer and ΦPSII and ETR values were 25% lower for deficit vs. adequate water just after silking, signifying increased water stress and decreased photosynthesis during reproductive growth. Under stress, LTs were 2.8°C cooler and ΦPSII and ETR values 50% higher for tolerant vs. susceptible hybrids, while all hybrids produced similar CF values under no stress. Thus, grain yield and photosynthetic responses of hybrids to stress were similar, indicating that CF measurements can be used to distinguish tolerant from susceptible hybrids. [ABSTRACT FROM AUTHOR]

Evaluates the field-scale apparent electrical conductivity (EC) mapping for delineating soil properties in Sterling, Colorado. Impact of management on soil condition; Measurement of soil EC; Determination of the soil and residue sampling.

Proso millet, Panicum miliaceum (L.), is a warm‐season annual grass well adapted for grain production in the western Great Plains of the United States, where risk of hail injury is greater than any other region of the United States. Because adjustment procedures and loss equations are not available, proso millet producers in this region have had limited access to crop hail insurance as a risk management tool. Our research was conducted to assess impact of shoot reduction treatments imposed at different crop growth stages on grain yield loss of proso millet grown under several environments. Our goal was to provide information for development of crop insurance adjustment procedures. We also wanted to determine the impact of shoot reduction on various grain yield components. Treatments consisted of a control and three levels of shoot reduction (33,66, and 100% of full stand) applied at four growth stages (emergence, 4‐leaf, boot, and heading stages). The experiments were conducted at two locations (Akron, CO and Carrington, ND) during 1996 and 1997 to assess treatment impact on relative grain yield (RGY), expressed as percent of control. A significant shoot reduction χ growth stage interaction was observed for RGY, indicating yield loss from increasing shoot reduction varied with growth stage. A linear reduction in RGY to increasing levels of shoot reduction was observed for the 4‐leaf, boot and heading growth stages, while RGY displayed a segmented linear response to increasing shoot reduction at emergence. Variation in grain yield, induced by shoot reduction treatments, was more consistently correlated with variation in seed number than seed weight. [ABSTRACT FROM PUBLISHER]

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions. [ABSTRACT FROM AUTHOR]