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Dynamic modeling of nitrogen balance...

Yuan, Mingwei.

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Dynamic modeling of nitrogen balance in irrigated sweet corn and snap bean on sandy soils.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic modeling of nitrogen balance in irrigated sweet corn and snap bean on sandy soils./
Author:	Yuan, Mingwei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	209 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
Contained By:	Dissertation Abstracts International78-01B(E).
Subject:	Soil sciences. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10158492
ISBN:	9781369135008

Dynamic modeling of nitrogen balance in irrigated sweet corn and snap bean on sandy soils.
Yuan, Mingwei.

Dynamic modeling of nitrogen balance in irrigated sweet corn and snap bean on sandy soils. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 209 p.

Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.

Snap bean (Phaselous vulgaris L.) and sweet corn (Zea mays L.) grown on irrigated sandy soils are economically important crops in Wisconsin. The sandy soils in which this production is focused permit rapid leaching of N from the rootzone, making it challenging to maintain the high soil solution N concentrations needed for maximum growth during early stages of the crop while minimizing groundwater pollution. The ability to add small amounts ofN throughout the crop's life with irrigation water provides an opportunity to synchronize N availability in the root zone with crop demand. Dynamic (time-dependent) modeling can identify when N additions are required for maximum production. I developed dynamic simulation models for sweet corn and snap bean that will support dynamic, adaptive N management of these crops on irrigated sandy soils. For sweet corn I adapted the simple process-based growth model AmaizeN to the Wisconsin production. For snap bean a phenological model was developed and combined with the N and soil water models used in sweet corn. Additionally, I evaluated the suitability of narrowband spectroscopy for estimating leaf N concentrations (%N) and leaf mass per area (LMA) and found that this technique could be applied in sweet corn and snap bean trials to provide important inputs for model development and calibration. Both crop models performed well in prediction of leaf area index, above-ground biomass, cumulative crop N uptake and yield (R2 , 0.82-0.95; RMSE, 6.00-13.34% of the measured range). The models also simulated seasonal NO3-N loading with acceptable levels of accuracy (1.7-19.8% of relative absolute errors between prediction and measurement from lysimeter experiments). Using the calibrated dynamic simulation models in sweet corn and snap bean, the adaptive N management strategy was assessed in a probabilistic perspective under weather uncertainty. The results showed that the dynamic N management strategy could significantly reduce seasonal NO3-N loading, while maintain high crop productivity, compared with conventional N management and controlled-released fertilizers. I also speculated that the probabilistic estimates of groundwater N loading provides the basis for a stakeholder-driven processes to meet specific groundwater NO3-N loading goals at a regional scale.

ISBN: 9781369135008Subjects--Topical Terms:

2122699
Soil sciences.

Dynamic modeling of nitrogen balance in irrigated sweet corn and snap bean on sandy soils.
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245 1 0 $a Dynamic modeling of nitrogen balance in irrigated sweet corn and snap bean on sandy soils.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 209 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
500 $a Advisers: William L. Bland; Matthew D. Ruark.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.
520 $a Snap bean (Phaselous vulgaris L.) and sweet corn (Zea mays L.) grown on irrigated sandy soils are economically important crops in Wisconsin. The sandy soils in which this production is focused permit rapid leaching of N from the rootzone, making it challenging to maintain the high soil solution N concentrations needed for maximum growth during early stages of the crop while minimizing groundwater pollution. The ability to add small amounts ofN throughout the crop's life with irrigation water provides an opportunity to synchronize N availability in the root zone with crop demand. Dynamic (time-dependent) modeling can identify when N additions are required for maximum production. I developed dynamic simulation models for sweet corn and snap bean that will support dynamic, adaptive N management of these crops on irrigated sandy soils. For sweet corn I adapted the simple process-based growth model AmaizeN to the Wisconsin production. For snap bean a phenological model was developed and combined with the N and soil water models used in sweet corn. Additionally, I evaluated the suitability of narrowband spectroscopy for estimating leaf N concentrations (%N) and leaf mass per area (LMA) and found that this technique could be applied in sweet corn and snap bean trials to provide important inputs for model development and calibration. Both crop models performed well in prediction of leaf area index, above-ground biomass, cumulative crop N uptake and yield (R2 , 0.82-0.95; RMSE, 6.00-13.34% of the measured range). The models also simulated seasonal NO3-N loading with acceptable levels of accuracy (1.7-19.8% of relative absolute errors between prediction and measurement from lysimeter experiments). Using the calibrated dynamic simulation models in sweet corn and snap bean, the adaptive N management strategy was assessed in a probabilistic perspective under weather uncertainty. The results showed that the dynamic N management strategy could significantly reduce seasonal NO3-N loading, while maintain high crop productivity, compared with conventional N management and controlled-released fertilizers. I also speculated that the probabilistic estimates of groundwater N loading provides the basis for a stakeholder-driven processes to meet specific groundwater NO3-N loading goals at a regional scale.
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