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Quantifying the carbon (C) uptake of Miscanthus × giganteus (M × g) in both aboveground and belowground structures (e.g., net primary productivity (NPP)) and differences among methodological approaches is crucial. Our objectives were to directly measure Mxg NPP and evaluate the effects of nitrogen application, location, and belowground biomass sampling methods. We hypothesize that increased nitrogen application increases the overall NPP of M × g and that quantifying rhizome biomass using excavations will produce the lowest variability between replicates. We collected biomass from mature M × g stands from three locations in Iowa with three nitrogen application rates and one site in Illinois. We destructively sampled at two time points, when rhizome mass is anticipated to be at a minimum (initial) and anticipated to be at its maximum (peak). Biomass was collected from 1 × 1 m quadrats in which one in‐clump and one beside‐clump cores were collected and then excavated to 30 cm depth to extract all rhizomes. We found that aboveground M × g NPP ranged from 15.4 Mg Da ha–1 year–1 to 36.4 Mg Da ha–1 year–1 and belowground M × g NPP ranged from 4.4 Mg Da ha–1 year–1 to 19.6 Mg Da ha–1 year–1. M × g NPP varied across sites, fertilization, and calculation assumptions. Aboveground NPP (yield) was on average 68.7% of the total NPP. Root‐to‐shoot ratios at peak biomass decreased with nitrogen application rate, from an average of 1.9 for 0 N plots to 0.89 for 224 N fertilized plots. There was more variation in core data than from excavations; however, when in‐clump and beside‐clump cores were averaged together, core and excavation averages were not different. Overall, these results show that the range of mature M × g NPP is driven by aboveground productivity, influenced by nitrogen application and site. Our results provide useful data to constrain agro‐ecosystem models and provide crucial insights for future perennial belowground sampling. Many estimates of Miscanthus × giganteus (M × g) productivity focus on aboveground harvestable yields and do not directly address belowground biomass in this perennial crop. To more accurately constrain the amount of carbon taken up by M × g in both aboveground and belowground plant parts, we calculated the net primary productivity (NPP) of mature M × g at three sites with three nitrogen application rates using collections of aboveground and belowground biomass at two time points during the growing season. These estimates help improve our understanding of M × g carbon sequestration potential and will improve the representation of M × g in agro‐ecosystem models.

Biomass sorghum (Sorghum bicolor L. Moench) is an annual C4 grass that has emerged as a candidate bioenergy crop but has not been widely grown in the United States. Corn (Zea mays L.), another annual C4 grass, has been produced on a large scale in the United States for biofuels. Iowa leads the nation in both corn and ethanol production. The high productivity of corn in Iowa creates the research question: could biomass sorghum be as or more productive than corn in the state in terms of bioenergy? Efforts to use crop models to fill the gaps left by field experimentation on biomass sorghum have also been limited thus far. To address our research question, we collected biophysical data on biomass sorghum grown in Iowa for two growing seasons, used them to develop a biomass sorghum module in an agroecosystem model (Agro‐IBIS), and predict the potential performance of biomass sorghum across Iowa compared to maize. Despite dry conditions in 2019 and 2020, average biomass sorghum yields were 17.20 Mg ha−1. By comparison, average corn aboveground biomass was higher in 2020 (22.02 Mg ha−1). Soil cores indicated average belowground biomass of 1.46 Mg ha−1, with roots concentrated near the surface (73% of biomass above 50 cm in a 1 m core). When biomass sorghum model parameters were calibrated with measured values, model output was in close agreement with measured biomass (slope = 0.932, R2 = 0.91) and evapotranspiration (slope = 0.757, R2 = 0.64). Subsequent regional simulations revealed a notable latitudinal gradient in biomass sorghum yield, with a strong linear relationship between yield and seasonal growing degree‐days (R2 = 0.89). When these yields were compared to simulated corn aboveground biomass yields, only 3.4% of the state had biomass sorghum yields that were significantly higher than corn. This number was reduced to 0.3% when comparing the crops in terms of energy ethanol yield. Thus, we conclude biomass sorghum must be improved to be competitive with corn as an annual biofuel crop in Iowa. This study provides a baseline against which to compare advancements in sorghum breeding for biomass and stress tolerance in Iowa. Biomass sorghum has emerged as a candidate bioenergy crop but to date has not been widely produced on a large scale in the United States. In this study, we collected biophysical data on biomass sorghum grown in Iowa and used them to develop a biophysical biomass sorghum module within the Agro‐IBIS model. Corn (or maize) is the dominant crop produced in Iowa for biofuel and Agro‐IBIS model simulations reveal that biomass sorghum energy ethanol yield (EEY) is below that of corn for 96.6% of Iowa. Thus, current cultivars must be improved to be competitive with corn.

The growth in the world population is driving increased demand for agricultural products. To meet this demand, with the consideration of global climate change, agricultural production needs innovative solutions that not only increase production, but also maintain or even improve the health of our ecosystems. Both agro-ecosystem models and remote sensing are useful tools for the prediction and observation of agricultural production. Agro-ecosystem models have great potential to predict agricultural production and ecosystem services while incorporating factors of change like global climate changes and agricultural innovations in management and genetics, but these models have two main requirements to accomplish this. The first is that agro-ecosystem models must accurately represent the systems they predict. The second is that models need to make predictions at increasingly small scales, incorporating methods to quantify field scale differences in agricultural production driven by increasing adoption of precision agriculture. Remote sensing of agricultural systems has the potential to provide the data needed to meet the needs of agro-ecosystem models. However, the accurate prediction of agricultural properties requires that the retrieval of remotely sensed variables be validated. At satellite scales, a well validated agro-ecosystem model can provide data for the validation of remote sensing products. This mutually beneficial relationship can provide data for the validation, analysis, and application of both remote sensing of agriculture and agro-ecosystem models, increasing the usefulness of these tools to progress agricultural understanding and innovation together. First, I use a well validated agro-ecosystem model, Agro-IBIS, to validate the Soil Moisture Active Passive (SMAP) satellite b-parameter across the U.S. Corn Belt. Crop water describes the water contained within crops and has great potential for uses in tracking vegetation development, water status, and for use in weather and climate models. Crop water is linearly related to SMAP VOD in the U.S. Corn belt and the b-parameter is the constant of proportionality that relates these two properties. I show that the current SMAP value of 0.11 is acceptable for use during the first half of the growing season (vegetative growth). However, the b-parameter value increases during the second half of the growing season (reproductive growth). I hypothesize that the changes in the b-parameter in the second half of the growing season are due to the concentration of water in reproductive structures, but more work needs to be done to explain and quantify this change.Second, I develop a sensitivity analysis and calibration for the agro-ecosystem model, Agro-IBIS, and demonstrate the usefulness of the platform through an analysis of soybean cultivars representing approximately 80 years worth of soybean breeding. The sensitivity analysis and calibration platform provides users the ability to make parameter changes with significant time savings and also minimize error between simulations and observed data with compatibility with the R function “optim”. Using the case study data, I analyze the role that stomatal sensitivity may have had in increasing the yield of more recent soybean cultivars. With successful calibration of three groups of historical soybean cultivars, I find that stomatal sensitivity indeed may have increased through breeding efforts, but definitive statements will require fitting the model parameters with higher confidence. Finally, I show the usefulness of remotely sensed crop physiological parameters as dynamic, daily parameter inputs into the Agro-IBIS model. Currently, the Agro-IBIS model treats physiological parameters like the CO2 saturated rate of photosynthesis (Vmax) and specific leaf area (SLA) as static parameters throughout the growing season. However, these parameters are known to change throughout the growing season in response to mechanisms of plant age and also environmental conditions. As the improvement of remote sensing progresses, there is potential to measure these physiological properties at sub-seasonal time scales and use dynamic parameters in Agro-IBIS. Through this study, I derive dynamic, daily, growing-season-long parameter datasets of Vmax and SLA from high frequency, field-scale hyperspectral remote sensing data. I integrate these daily parameter datasets into Agro-IBIS to test model performance with and without daily parameterization. I find that model performance does not significantly increase when incorporating daily parameters of Vmax and SLA. However, through this analysis, I find that the Agro-IBIS representation of leaf area accumulation may be mis-specified and the model over-parameterized to give the right results for the wrong reasons. From this analysis I make recommendations for the improvement of the leaf area accumulation for the improved performance of future field-scale simulations using Agro-IBIS. This study provides the first steps in improving Agro-IBIS field-scale simulations with the assimilation of remotely sensed parameters.Through these three studies, I show the mutual benefits that data from both remote sensing and agro-ecosystem models can provide. With an improved ability to understand the parameter space of the model and to calibrate the model to observations, future studies using the Agro-IBIS model can make better predictions of existing and future agricultural production. Data provided through agro-ecosystem models can serve to validate satellite remote sensing products efficiently, improving observations of agro-ecosystems. Likewise, remote sensing data can provide measurements of crops that enable the improvement of agro-ecosystem model predictions. These studies show that the development, utilization, and improvement of these tools together can progress our understanding of agricultural production and continue agricultural innovation.

I was pleasantly surprised by the article and accompanying picture of the original 1917 \"living flag\" which appeared in THE TRIBUNE [July 2]. The original \"living flag\" was engineered and photographed by my father, Robert...

INTRODUCTION:Colonoscopy surveillance guidelines categorize individuals as high or low risk for future colorectal cancer (CRC) based primarily on their prior polyp characteristics, but this approach is imprecise, and consideration of other risk factors may improve postpolypectomy risk stratification.METHODS:Among patients who underwent a baseline colonoscopy with removal of a conventional adenoma in 2004-2016, we compared the performance for postpolypectomy CRC risk prediction (through 2020) of a comprehensive model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and prior polyp findings (i.e., adenoma with advanced histology, polyp size ≥10 mm, and sessile serrated adenoma or traditional serrated adenoma) with a polyp model featuring only polyp findings. Models were developed using Cox regression. Performance was assessed using area under the receiver operating characteristic curve (AUC) and calibration by the Hosmer-Lemeshow goodness-of-fit test.RESULTS:Among 95,001 patients randomly divided 70:30 into model development (n = 66,500) and internal validation cohorts (n = 28,501), 495 CRC were subsequently diagnosed; 354 in the development cohort and 141 in the validation cohort. Models demonstrated adequate calibration, and the comprehensive model demonstrated superior predictive performance to the polyp model in the development cohort (AUC 0.71, 95% confidence interval [CI] 0.68-0.74 vs AUC 0.61, 95% CI 0.58-0.64, respectively) and validation cohort (AUC 0.70, 95% CI 0.65-0.75 vs AUC 0.62, 95% CI 0.57-0.67, respectively).DISCUSSION:A comprehensive CRC risk prediction model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and polyp findings was more accurate at predicting postpolypectomy CRC diagnosis than a model based on polyp findings alone.

Postpolypectomy surveillance is a common colonoscopy indication in older adults; however, guidelines provide little direction on when to stop surveillance in this population. To estimate surveillance colonoscopy yields in older adults. This population-based cross-sectional study included individuals 70 to 85 years of age who received surveillance colonoscopy at a large, community-based US health care system between January 1, 2017, and December 31, 2019; had an adenoma detected 12 or more months previously; and had at least 1 year of health plan enrollment before surveillance. Individuals were excluded due to prior colorectal cancer (CRC), hereditary CRC syndrome, inflammatory bowel disease, or prior colectomy or if the surveillance colonoscopy had an inadequate bowel preparation or was incomplete. Data were analyzed from September 1, 2022, to February 22, 2024. Age (70-74, 75-79, or 80-85 years) at surveillance colonoscopy and prior adenoma finding (ie, advanced adenoma vs nonadvanced adenoma). The main outcomes were yields of CRC, advanced adenoma, and advanced neoplasia overall (all ages) by age group and by both age group and prior adenoma finding. Multivariable logistic regression was used to identify factors associated with advanced neoplasia detection at surveillance. Of 9740 surveillance colonoscopies among 9601 patients, 5895 (60.5%) were in men, and 5738 (58.9%), 3225 (33.1%), and 777 (8.0%) were performed in those aged 70-74, 75-79, and 80-85 years, respectively. Overall, CRC yields were found in 28 procedures (0.3%), advanced adenoma in 1141 (11.7%), and advanced neoplasia in 1169 (12.0%); yields did not differ significantly across age groups. Overall, CRC yields were higher for colonoscopies among patients with a prior advanced adenoma vs nonadvanced adenoma (12 of 2305 [0.5%] vs 16 of 7435 [0.2%]; P = .02), and the same was observed for advanced neoplasia (380 of 2305 [16.5%] vs 789 of 7435 [10.6%]; P < .001). Factors associated with advanced neoplasia at surveillance were prior advanced adenoma (adjusted odds ratio [AOR], 1.65; 95% CI, 1.44-1.88), body mass index of 30 or greater vs less than 25 (AOR, 1.21; 95% CI, 1.03-1.44), and having ever smoked tobacco (AOR, 1.14; 95% CI, 1.01-1.30). Asian or Pacific Islander race was inversely associated with advanced neoplasia (AOR, 0.81; 95% CI, 0.67-0.99). In this cross-sectional study of surveillance colonoscopy yield in older adults, CRC detection was rare regardless of prior adenoma finding, whereas the advanced neoplasia yield was 12.0% overall. Yields were higher among those with a prior advanced adenoma than among those with prior nonadvanced adenoma and did not increase significantly with age. These findings can help inform whether to continue surveillance colonoscopy in older adults.