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Fertilizer management practices that focus on applying N fertilizer at the right rate and time have been proposed as a practical option to reduce NO 3-N losses from subsurface drained agricultural fields. In this study, regression equations were developed to predict NO 3-N losses for a corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] rotation in southern Minnesota, using fertilizer application timing and rate and growing season precipitation as inputs. The equations were developed using the results of the field-scale hydrologic and N simulation model DRAINMOD-NII, first calibrated and validated for three sites in southern Minnesota, and then run with different combinations of N fertilizer application rates and timings. Fertilizer timing treatments included a single application in the fall or spring and a split-spring application (half applied preplant and the remaining applied as sidedress). The predictive regression equations showed that the split fertilizer application timing could reduce regional N loads by 28% compared with spring or fall applications. Greater reductions were predicted when the split timing was combined with lower N fertilizer rates. Utilizing the split application timing and reducing the fertilizer rate by 10 and 30% showed 33 and 41% reductions in N loads, respectively, compared with current fertilizer management practices. Such reductions in fertilizer application rates could be achieved through the use of variable-rate nitrogen (VRN) fertilizer technologies. Results of this modeling study indicate that synchronizing fertilizer application with crop requirements and utilizing VRN technologies could significantly reduce N loads to surface waters in southern Minnesota.

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e18766 Background: Patients (pts) with thoracic cancers have a high rate of hospitalization and death from COVID-19. Smoking has been associated with increased risk for severe COVID-19. However, there is limited data evaluating the impact of smoking recency on COVID-19 severity in pts with cancer. We aimed to characterize the clinical outcomes of COVID-19 based on the recency of smoking in pts with thoracic cancers (TC) and all other cancers (OC). Methods: Adult pts with cancer and lab-confirmed SARS-CoV-2 and smoking history recorded in the CCC19 registry (NCT0435470) were included. Pts were stratified by cancer type (TC or OC) and further stratified into subgroups based on the recency of smoking cessation: current smoker; former smokers who quit < 1 yr. ago; 1-5 yr. ago; 6-10 yr. ago; quit > 10 yr. ago; and never smoker. 30-day all-cause mortality was the primary endpoint. Secondary endpoints were any hospitalization; hospitalization with supplemental O2; ICU admission; and mechanical ventilation. Results: From January 2020 to December 2021, 752 pts from TC group and 8,291 pts from OC group met the inclusion criteria. 78% of patients in TC group ever smoked compared to 36% patients in the OC group. In both groups, the majority of never-smokers were females (70% and 60% in TC and OC respectively). The burden of smoking and the rate of pulmonary comorbidities (PC) was higher in the TC group (PC 22-69%) compared to OC group (PC 12-26%) across all smoking strata. Overall, 30-day all-cause mortality was 21% and 11% in pts with TC and OC respectively. Former smokers who quit < 1 year ago in TC group had the highest rate of mortality and severe COVID-19 outcomes. However, in the OC group, there was no consistent trend of higher mortality or severe COVID-19 outcomes in specific subgroups based on smoking recency. Conclusions: To our knowledge this is the largest study evaluating the effect of granular phenotypes of smoking recency on COVID-19 outcomes in pts with cancer. Recent smokers who quit < 1 year ago in TC group had the highest rate of mortality and severe COVID-19. Further analysis exploring the factors (e.g., smoking pack years) associated with severe outcomes in this subgroup is planned.[Table: see text]