Managing Nitrogen Soils for Corn Production on Irrigated Sandy Soils PDF Download
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Author: George William Rehm
Publisher:
ISBN:
Category : Corn
Languages : en
Pages : 8
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Author: George William Rehm
Publisher:
ISBN:
Category : Corn
Languages : en
Pages : 8
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Author: Derrel L. Martin
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ISBN:
Category :
Languages : en
Pages : 392
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Author:
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ISBN:
Category : Agricultural chemicals
Languages : en
Pages : 76
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Author: Steven Lyle Oberle
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ISBN:
Category :
Languages : en
Pages : 278
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Author: Michael A. Schmitt
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ISBN:
Category : Nitrogen fertilizers
Languages : en
Pages : 8
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Author: Mingwei Yuan
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ISBN:
Category :
Languages : en
Pages : 0
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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 of N 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 nitrate-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 nitrate-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 nitrate-N loading goals at a regional scale.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 196
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Book Description
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 of N 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 nitrate-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 nitrate-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 nitrate-N loading goals at a regional scale.
Author: Franklin Hiram King
Publisher:
ISBN:
Category : Soils
Languages : en
Pages : 336
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Author: Clair H. Redmond
Publisher:
ISBN:
Category : Agricultural experiment stations
Languages : en
Pages : 464
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Author: Ronald J. Gehl
Publisher:
ISBN:
Category :
Languages : en
Pages : 444
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