東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

An integrated crop- and soil-based s...

Roberts, Darrin F.

Linked to FindBook

Google Book

Amazon

博客來

An integrated crop- and soil-based strategy for variable-rate nitrogen management in corn.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An integrated crop- and soil-based strategy for variable-rate nitrogen management in corn./
Author:	Roberts, Darrin F.
Description:	249 p.
Notes:	Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3231.
Contained By:	Dissertation Abstracts International70-06B.
Subject:	Agriculture, Agronomy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3360163
ISBN:	9781109244021

An integrated crop- and soil-based strategy for variable-rate nitrogen management in corn.
Roberts, Darrin F.

An integrated crop- and soil-based strategy for variable-rate nitrogen management in corn. - 249 p.

Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3231.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2009.

Nitrogen (N) management in cereal crops has been the subject of considerable research and debate for several decades. Historic N management practices have contributed to low nitrogen use efficiency (NUE). Low NUE can be caused by such things as poor synchronization between soil N supply and crop demand, uniform application rates of fertilizer N to spatially variable landscapes, and failure to account for temporally variable influences on soil N supply and crop N need. Active canopy reflectance sensors and management zones (MZ) have been studied separately as possible plant- and soil-based N management tools to increase NUE. Recently, some have suggested that the integration of these two approaches would provide a more robust N management strategy that could more effectively account for soil and plant effects on crop N need. For this reason, the goal of this research was to develop an N application strategy that would account for spatial variability in soil properties and use active canopy reflectance sensors to determine in-season, on-the-go N fertilizer rates, thereby increasing NUE and economic return for producers over current N management practices. To address this overall goal, a series of studies were conducted to better understand active canopy sensor use and explore the possibility of integrating spatial soil data with active canopy sensors. Sensor placement to assess crop N status was first examined. It was found that the greatest reduction in error over sensing each individual row for a hypothetical 24-row applicator was obtained with 2-3 sensors estimating an average chlorophyll index for the entire boom width. Next, use of active sensor-based soil organic matter (OM) estimation was compared to more conventional aerial image-based soil OM estimation. By adjusting regression intercept values for each field, OM could be predicted using either a single sensor or image data layer. The final study consisted of validation of the active sensor algorithm developed by Solari (2006), identification of soil variables for MZ delineation, and the possible integration of MZ and active sensors for N application. Crop response (sensor measured sufficiency index and yield) had the highest correlation with soil optical reflectance readings in sandy fields and with apparent soil electrical conductivity in silt loam fields with eroded slopes. Therefore, using these soil variables to delineate MZ allowed characterization of spatial patterns in both in-season crop response (sufficiency index) and yield. Compared to uniform N application, integrating MZ and sensor-based N application resulted in substantial N savings (&sim;40--120 kg ha-1) and increases in partial factor productivity (&sim;13--75 kg grain (kg N applied)-1) for fine-textured soils with eroded slopes. However, for coarser texture soils the current sensor-based N application algorithm may require further calibration, and for fields with no spatial variability there appears to be no benefit to using the algorithm. Collectively, results from these studies show promise for integrating active sensor-based N application and static soil-based MZ to increase NUE and economic return for producers over current N management strategies, but further research is needed to explore how best to integrate these two N management strategies.

ISBN: 9781109244021Subjects--Topical Terms:

1018679
Agriculture, Agronomy.

An integrated crop- and soil-based strategy for variable-rate nitrogen management in corn.
LDR:04528nam 2200349 4500 001 1397856
005 20110907152205.5
008 130515s2009 ||||||||||||||||| ||eng d
020 $a 9781109244021
035 $a (UMI)AAI3360163
035 $a AAI3360163
040 $a UMI $c UMI
100 1 $a Roberts, Darrin F. $3 1676715
245 1 3 $a An integrated crop- and soil-based strategy for variable-rate nitrogen management in corn.
300 $a 249 p.
500 $a Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3231.
500 $a Advisers: Richard B. Ferguson; John F. Shanahan.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2009.
520 $a Nitrogen (N) management in cereal crops has been the subject of considerable research and debate for several decades. Historic N management practices have contributed to low nitrogen use efficiency (NUE). Low NUE can be caused by such things as poor synchronization between soil N supply and crop demand, uniform application rates of fertilizer N to spatially variable landscapes, and failure to account for temporally variable influences on soil N supply and crop N need. Active canopy reflectance sensors and management zones (MZ) have been studied separately as possible plant- and soil-based N management tools to increase NUE. Recently, some have suggested that the integration of these two approaches would provide a more robust N management strategy that could more effectively account for soil and plant effects on crop N need. For this reason, the goal of this research was to develop an N application strategy that would account for spatial variability in soil properties and use active canopy reflectance sensors to determine in-season, on-the-go N fertilizer rates, thereby increasing NUE and economic return for producers over current N management practices. To address this overall goal, a series of studies were conducted to better understand active canopy sensor use and explore the possibility of integrating spatial soil data with active canopy sensors. Sensor placement to assess crop N status was first examined. It was found that the greatest reduction in error over sensing each individual row for a hypothetical 24-row applicator was obtained with 2-3 sensors estimating an average chlorophyll index for the entire boom width. Next, use of active sensor-based soil organic matter (OM) estimation was compared to more conventional aerial image-based soil OM estimation. By adjusting regression intercept values for each field, OM could be predicted using either a single sensor or image data layer. The final study consisted of validation of the active sensor algorithm developed by Solari (2006), identification of soil variables for MZ delineation, and the possible integration of MZ and active sensors for N application. Crop response (sensor measured sufficiency index and yield) had the highest correlation with soil optical reflectance readings in sandy fields and with apparent soil electrical conductivity in silt loam fields with eroded slopes. Therefore, using these soil variables to delineate MZ allowed characterization of spatial patterns in both in-season crop response (sufficiency index) and yield. Compared to uniform N application, integrating MZ and sensor-based N application resulted in substantial N savings (&sim;40--120 kg ha-1) and increases in partial factor productivity (&sim;13--75 kg grain (kg N applied)-1) for fine-textured soils with eroded slopes. However, for coarser texture soils the current sensor-based N application algorithm may require further calibration, and for fields with no spatial variability there appears to be no benefit to using the algorithm. Collectively, results from these studies show promise for integrating active sensor-based N application and static soil-based MZ to increase NUE and economic return for producers over current N management strategies, but further research is needed to explore how best to integrate these two N management strategies.
590 $a School code: 0138.
650 4 $a Agriculture, Agronomy. $3 1018679
650 4 $a Agriculture, Soil Science. $3 1017824
650 4 $a Engineering, Agricultural. $3 1019504
650 4 $a Remote Sensing. $3 1018559
690 $a 0285
690 $a 0481
690 $a 0539
690 $a 0799
710 2 $a The University of Nebraska - Lincoln. $b Agronomy. $3 1029981
773 0 $t Dissertation Abstracts International $g 70-06B.
790 1 0 $a Ferguson, Richard B., $e advisor
790 1 0 $a Shanahan, John F., $e advisor
790 1 0 $a Adamchuk, Viacheslav I. $e committee member
790 1 0 $a Kitchen, Newell R. $e committee member
790 1 0 $a Schepers, James S. $e committee member
790 $a 0138
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3360163