Corn Stalk Nitrate Concentration Profile

The end-of-season nitrate test provides a method of assessing the N available to the corn (Zea mays L.) crop during the latter part of the season. This study was conducted to determine how stalk nitrate test results and interpretations are affected by sample composition. Stalks were collected from three filed sites and separated into phytomers (node plus internode above), which were subdivided into three or five segments after length was measured. Nitrate-N concentration of phytomers decreased linearly from the soil to the ear. Within a phytomer, segments also decreased acropetally (from base to apex). Node tissue NO3-N concentration did not differ from that of the internode segment immediately above the node. Weighted means were used to compute NO3-N concentration of stalk samples collected 5 cm higher (from 20 to 40 cm above the soil) or lower (from 10 to 30 cm above the soil). Although the three samples (10-30, 15-35, and 20-40 cm) differed in NO3-N concentration, the difference was only about 15% compared with the 25% difference in sampling position (± 5 cm of 20-cm sample length). The phytomer nearest the soil had 35 to 40% greater NO3-N concentrations than the section of stalk 15 to 35 cm above the soil. Critical values delineating yield-limiting adequate, and excessive N availability should be modified if stalk sections other than the standard 15 to 35 cm section are used. However, the qualitative nature of the stalk nitrate test and the range of NO3-N concentrations observed with reasonable corn cultural practices (1000x) make this test quite robust and precise definition of sample composition and critical values less necessary

Latest results from the EU project AVATAR: aerodynamic modelling of 10 MW wind turbines

This paper presents the most recent results from the EU project AVATAR in which aerodynamic models are improved and validated for wind turbines on a scale of 10 MW and more. Measurements on a DU 00-W-212 airfoil are presented which have been taken in the pressurized DNW-HDG wind tunnel up to a Reynolds number of 15 Million. These measurements are compared with measurements in the LM wind tunnel for Reynolds numbers of 3 and 6 Million and with calculational results. In the analysis of results special attention is paid to high Reynolds numbers effects. CFD calculations on airfoil performance showed an unexpected large scatter which eventually was reduced by paying even more attention to grid independency and domain size in relation to grid topology. Moreover calculations are presented on flow devices (leading and trailing edge flaps and vortex generators). Finally results are shown between results from 3D rotor models where a comparison is made between results from vortex wake methods and BEM methods at yawed conditions

Characterization of maize physiological responses to nitrogen stress by using chlorophyll fluorescence and gas exchange measurements.

Suplemento. Edição dos resumos do IX Congresso Brasileiro de Fisiologia Vegetal, 2003, Atibaia, SP

Model and Sensor-Based Recommendation Approaches for In-Season Nitrogen Management in Corn

Nitrogen management for corn (Zea mays L.) may be improved by applying a portion of N in-season. This investigation was conducted to evaluate crop modeling (Maize-N) and active crop canopy sensing approaches for recommending in-season N fertilizer rates. These approaches were evaluated during 2012–2013 on 11 field sites, in Missouri, Nebraska, and North Dakota. Nitrogen management also included a no-N treatment (check) and a non-limiting N reference (all at planting). Nitrogen management treatments were assessed for two hybrids and at low and high seeding rates, arranged in a randomized complete block design. In 9 of 11 site-years, the sensor-based approach recommended lower in-season N rates than the model (collectively 59% less N), resulting in trends of higher partial factor productivity of nitrogen (PFPN) and higher agronomic efficiency (AE) than the model. However, yield was better protected by the model-based approach. In some situations, canopy sensing excelled at optimizing the N rate for localized conditions. With abnormally warm and moist soil conditions for the 2012 Nebraska sites and presumed high levels of inorganic N from mineralization, N application was appropriately reduced, resulting in no yield decrease and N savings compared to the non-limiting N reference. Depending on the site, both recommendation approaches were successful; a combination of model and sensor information may optimize in-season decision support for N recommendation

Toward accurate CO_2 and CH_4 observations from GOSAT

The column-average dry air mole fractions of atmospheric carbon dioxide and methane (X_(CO_2) and X_(CH_4)) are inferred from observations of backscattered sunlight conducted by the Greenhouse gases Observing SATellite (GOSAT). Comparing the first year of GOSAT retrievals over land with colocated ground-based observations of the Total Carbon Column Observing Network (TCCON), we find an average difference (bias) of −0.05% and −0.30% for X_(CO_2) and X_(CH_4) with a station-to-station variability (standard deviation of the bias) of 0.37% and 0.26% among the 6 considered TCCON sites. The root-mean square deviation of the bias-corrected satellite retrievals from colocated TCCON observations amounts to 2.8 ppm for X_(CO_2) and 0.015 ppm for X_(CH_4). Without any data averaging, the GOSAT records reproduce general source/sink patterns such as the seasonal cycle of X_(CO_2) suggesting the use of the satellite retrievals for constraining surface fluxes

A new perspective when examining maize fertilizer nitrogen use efficiency, incrementally

For maize (Zea mays L.), nitrogen (N) fertilizer use is often summarized from field to global scales using average N use efficiency (NUE). But expressing NUE as averages is misleading because grain increase to added N diminishes near optimal yield. Thus, environmental risks increase as economic benefits decrease. Here, we use empirical datasets obtained in North America of maize grain yield response to N fertilizer (n = 189) to create and interpret incremental NUE (iNUE), or the change in NUE with change in N fertilization. We show for those last units of N applied to reach economic optimal N rate (EONR) iNUE for N removed with the grain is only about 6%. Conversely stated, for those last units of N applied over 90% is either lost to the environment during the growing season, remains as inorganic soil N that too may be lost after the growing season, or has been captured within maize stover and roots or soil organic matter pools. Results also showed iNUE decrease averaged 0.63% for medium-textured soils and 0.37% for fine-textured soils, attributable to fine-textured soils being more predisposed to denitrification and/or lower mineralization. Further analysis demonstrated the critical nature growing season water amount and distribution has on iNUE. Conditions with too much rainfall and/or uneven rainfall produced low iNUE. Producers realize this from experience, and it is uncertain weather that largely drives insurance fertilizer additions. Nitrogen fertilization creating low iNUE is environmentally problematic. Our results show that with modest sub-EONR fertilization and minor forgone profit, average NUE improvements of ~10% can be realized. Further, examining iNUE creates unique perspective and ideas for how to improve N fertilizer management tools, educational programs, and public policies and regulations

CFD modelling of wind farms in complex terrain

Modelling of entire wind farms in flat and complex terrain using a full 3D Navier–Stokes solver for incompressible flow is presented in this paper. Numerical integration of the governing equations is performed using an implicit pressure correction scheme, where the wind turbines (W/Ts) are modelled as momentum absorbers through their thrust coefficient. The k–ω turbulence model, suitably modified for atmospheric flows, is employed for closure. A correction is introduced to account for the underestimation of the near wake deficit, in which the turbulence time scale is bounded using a general “realizability” constraint for the fluctuating velocities. The second modelling issue that is discussed in this paper is related to the determination of the reference wind speed for the thrust calculation of the machines. Dealing with large wind farms and wind farms in complex terrain, determining the reference wind speed is not obvious when a W/T operates in the wake of another WT and/or in complex terrain. Two alternatives are compared: using the wind speed value at hub height one diameter upstream of the W/T and adopting an induction factor-based concept to overcome the utilization of a wind speed at a certain distance upwind of the rotor. Application is made in two wind farms, a five-machine one located in flat terrain and a 43-machine one located in complex terrain

Simulation of wind farms in flat and complex terrain using CFD

Use of computational fluid dynamic (CFD) methods to predict the power production from wind entire wind farms in flat and complex terrain is presented in this paper. Two full 3D Navier–Stokes solvers for incompressible flow are employed that incorporate the k–ε and k–ω turbulence models respectively. The wind turbines (W/Ts) are modelled as momentum absorbers by means of their thrust coefficient using the actuator disk approach. The WT thrust is estimated using the wind speed one diameter upstream of the rotor at hub height. An alternative method that employs an induction-factor based concept is also tested. This method features the advantage of not utilizing the wind speed at a specific distance from the rotor disk, which is a doubtful approximation when a W/T is located in the wake of another and/or the terrain is complex. To account for the underestimation of the near wake deficit, a correction is introduced to the turbulence model. The turbulence time scale is bounded using the general “realizability” constraint for the turbulent velocities. Application is made on two wind farms, a five-machine one located in flat terrain and another 43-machine one located in complex terrain. In the flat terrain case, the combination of the induction factor method along with the turbulence correction provides satisfactory results. In the complex terrain case, there are some significant discrepancies with the measurements, which are discussed. In this case, the induction factor method does not provide satisfactory results