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Subsurface drainage is widely used to improve field trafficability and crop growth on poorly drained soils. Although drainage causes or exacerbates some environmental challenges, drained croplands are among the most productive in the world and there may be opportunities to better manage drainage for positive environmental outcomes including improved nitrogen use efficiency. The objective of this study was to determine the effects of drainage on cropping system N use. We hypothesized that drainage reduces the agronomic optimum N fertilizer rate (AONR) while increasing grain yield thereby increasing agronomic efficiency (kg grain kg-1 N at the AONR; AE) and reducing N surplus (i.e., N inputs minus outputs). Using a site in southeast Iowa, USA that included four drainage treatments in both continuous maize and maize-soybean crop rotations with eight N rates (0–392 kg N ha-1), maize grain yield was measured from 2016 to 2020. The four drainage systems included conventional (1.2 m depth x 18 m spacing), shallow (0.76 m x 12.2 m), controlled (1.2 m x 18 m, with a water table control structure), and a no drainage control. In addition, maize N uptake in the no drain and conventional depth systems was measured from 2016 to 2018. No drainage produced the highest grain yields at the AONR (13.3 ± 1.8 Mg ha-1), but also the highest AONR (243 ± 155 kg N ha-1) and lowest AE at the AONR (73 ± 37 kg grain kg-1 N); moreover, no drainage was the most variable system from year-to-year over all metrics (mean CV 44% ± 20%). In contrast, controlled drainage had the greatest average AE at the AONR (85 ± 28 kg grain kg-1 N) and the least variability over nearly all metrics from year-to-year (mean CV 25% ± 14%). Conventional drainage had consistently higher maize grain N concentrations than no drainage across N rates, particularly in the continuous maize rotation (6.9 ± 2.8%) and a lower AONR in four of five years compared to no drainage. Within each rotation at the economic optimum rate, no drainage had higher N surplus than conventional (45 and 19 kg N ha-1 and 137% and 72% higher for the continuous maize and the maize phase of maize-soybean rotations, respectively). This study demonstrates that drainage reduces the amount and interannual variability of N fertilizer requirements while also reducing interannual variability in yields. Better recognition, understanding, and management of these effects can benefit both productivity and the environment. • Drainage increased agronomic efficiency. • Drainage reduced annual variability of grain yield and optimum N fertilizer rate. • Controlled drainage performed the best of the four drainage treatments. • There was a greater effect of crop rotation than drainage on N surplus. • Grain N concentration was higher in drainage vs. no drainage across N rates. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To investigate the interrater reliability of the Capacity Profile (CAP) in children with neurodevelopmental disabilities. Design: Cross-sectional study. Setting: Six rehabilitation centers in the Netherlands. Participants: Children (N=70) with permanent, nonprogressive neurodevelopmental disabilities. Interventions: Not applicable. Main Outcome Measures: The CAP is a method to classify additional care needs of children with nonprogressive neurodevelopmental disabilities in 5 domains of body functions: physical health, motor functions, sensory functions, mental functions, and voice/speech functions. The CAP was scored independently by 2 trained physiatrists during an outpatient visit. Interrater reliability was evaluated using an intraclass correlation coefficient (ICC). Results: Interrater reliability of the CAP is as follows: physical functions, ICC=.74; motor functions, ICC=.85; sensory functions, ICC=.61; mental functions, ICC=.85; and voice/speech functions, ICC=.79. Conclusions: These findings support the interrater reliability of the CAP, when scored during a visit to the rehabilitation center. [Copyright &y& Elsevier]

• Parameter adjustment approach for DNDC calibration is effective under ample data. • DNDC predicted the daily-time scale and cumulative CH 4 emission in good agreement. • DNDC model show discrepancies in soil pH and temperature, urge further advances. • Soil pH identified to control CH 4 flux after biochar application. Methane (CH 4) is a second largest contributor of global warming after carbon dioxide (CO 2), and it is crucial to understand how management practices affect CH 4 emissions. Among field crops, paddy rice alone has accounted for about 10–12% of the total CH 4 emission in 2010. The process-based DeNitrification and DeComposition (DNDC) model can be applied to quantify greenhouse gas (GHG) emissions from agricultural soils. Capturing both the daily time-scale and cumulative growing season CH 4 fluxes by DNDC may help to devise appealing mitigation approaches for better rice management. In this study, DNDC was calibrated with a parameter-adjustment approach under two treatments: 140 kg N ha−1 without biochar and 140 kg N ha−1 with 2% biochar. Simulation results show that the model predicted the daily CH 4 fluxes in good agreement with measurements under both treatments. Next, DNDC was validated with the adjusted parameters against the remaining biochar and N treatments, and the model performed well in prediction of CH 4 fluxes as indicated by several statistical indexes: RMSE ranged from 6.74 to 7.62 g CH 4 ha−1 d −1, the d -index varied between 0.92 to 0.98, and MPD and nRMSE were at 10.94–17.43% and 17.54–24.52%, respectively. In terms of cumulative growing season CH 4 efflux, DNDC under-simulated all treatments except the control. Further DNDC predicted above-ground dry weights and volumetric water contents in good agreement with the measurements. Moreover, model poorly predicted soil temperature, pH, and soil moisture content. The value of the d -index varied from 0.12 to 0.38 for both soil temperature and pH. Subsequently, DNDC successfully identified the significant impact of biochar on CH 4 emission, and model error was strongly correlated with pH of soils with no biochar amendments. In conclusion, the DNDC model can capture the daily time-scale as well as annual-scale CH 4 fluxes, though DNDC must be validated by intensive measurements of additional soil variables, including dissolved organic carbon (DOC) and microbial composition under different biochar types. [Display omitted] [ABSTRACT FROM AUTHOR]