語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Ground based active remote sensors f...
~
Shaver, Timothy Michael.
FindBook
Google Book
Amazon
博客來
Ground based active remote sensors for precision nitrogen management in irrigated maize production.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Ground based active remote sensors for precision nitrogen management in irrigated maize production./
作者:
Shaver, Timothy Michael.
面頁冊數:
185 p.
附註:
Source: Dissertation Abstracts International, Volume: 70-08, Section: B, page: 4734.
Contained By:
Dissertation Abstracts International70-08B.
標題:
Agriculture, Agronomy. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3374621
ISBN:
9781109353327
Ground based active remote sensors for precision nitrogen management in irrigated maize production.
Shaver, Timothy Michael.
Ground based active remote sensors for precision nitrogen management in irrigated maize production.
- 185 p.
Source: Dissertation Abstracts International, Volume: 70-08, Section: B, page: 4734.
Thesis (Ph.D.)--Colorado State University, 2009.
Precision agriculture can increase farm input efficiency by accurately quantifying variability within a field. Remotely sensed normalized difference vegetation index (NDVI) has been shown to quantify maize (Zea mays ) N variability. Ground-based active remote sensors that can determine NDVI are commercially available and have been shown to accurately distinguish N variability in maize. There are several active sensors available but no studies directly comparing active sensors have been reported. Therefore, a study was conducted to evaluate active sensor performance and develop an in-season maize N recommendation algorithm for use in Colorado using NDVI. Previous studies have demonstrated an association of active sensor NDVI with maize N content and height. However, the NDVI from a GreenSeeker(TM) green NDVI prototype active sensor had not yet been tested when our study began. Therefore, the green sensor was evaluated to determine if differences in plant growth across MZ could be determined by the active sensor. Results show that the prototype active sensor did not record NDVI values that were associated with MZ. The NDVI from two different sensors (Crop Circle(TM) amber NDVI and GreenSeeker(TM) red NDVI) were then examined under greenhouse and field conditions. Results show that NDVI from the amber and red sensors equally distinguished applied N differences in maize. Each active sensor's NDVI values had high R2 values with applied N rate and plant N concentration. Results also show that each sensor's NDVI readings had high R2 values with applied N rate and yield at the V12 and V14 maize growth stages. An N recommendation algorithm was then created for use at the V12 maize growth stage for both the amber and red sensors using NDVI. These algorithms yielded N recommendations that were not significantly different across sensor type suggesting that the amber and red NDVI sensors performed equally. Also, each N recommendation algorithm yielded unbiased N recommendations suggesting that each was a valid estimator of required N at maize growth stage V12. Overall results show that the amber and red sensors equally determine N variability in irrigated maize and could be very important tools for managing in-season application of N fertilizer.
ISBN: 9781109353327Subjects--Topical Terms:
1018679
Agriculture, Agronomy.
Ground based active remote sensors for precision nitrogen management in irrigated maize production.
LDR
:03186nam 2200277 4500
001
1397880
005
20110907152213.5
008
130515s2009 ||||||||||||||||| ||eng d
020
$a
9781109353327
035
$a
(UMI)AAI3374621
035
$a
AAI3374621
040
$a
UMI
$c
UMI
100
1
$a
Shaver, Timothy Michael.
$3
1676738
245
1 0
$a
Ground based active remote sensors for precision nitrogen management in irrigated maize production.
300
$a
185 p.
500
$a
Source: Dissertation Abstracts International, Volume: 70-08, Section: B, page: 4734.
500
$a
Adviser: Dwayne Westfall.
502
$a
Thesis (Ph.D.)--Colorado State University, 2009.
520
$a
Precision agriculture can increase farm input efficiency by accurately quantifying variability within a field. Remotely sensed normalized difference vegetation index (NDVI) has been shown to quantify maize (Zea mays ) N variability. Ground-based active remote sensors that can determine NDVI are commercially available and have been shown to accurately distinguish N variability in maize. There are several active sensors available but no studies directly comparing active sensors have been reported. Therefore, a study was conducted to evaluate active sensor performance and develop an in-season maize N recommendation algorithm for use in Colorado using NDVI. Previous studies have demonstrated an association of active sensor NDVI with maize N content and height. However, the NDVI from a GreenSeeker(TM) green NDVI prototype active sensor had not yet been tested when our study began. Therefore, the green sensor was evaluated to determine if differences in plant growth across MZ could be determined by the active sensor. Results show that the prototype active sensor did not record NDVI values that were associated with MZ. The NDVI from two different sensors (Crop Circle(TM) amber NDVI and GreenSeeker(TM) red NDVI) were then examined under greenhouse and field conditions. Results show that NDVI from the amber and red sensors equally distinguished applied N differences in maize. Each active sensor's NDVI values had high R2 values with applied N rate and plant N concentration. Results also show that each sensor's NDVI readings had high R2 values with applied N rate and yield at the V12 and V14 maize growth stages. An N recommendation algorithm was then created for use at the V12 maize growth stage for both the amber and red sensors using NDVI. These algorithms yielded N recommendations that were not significantly different across sensor type suggesting that the amber and red NDVI sensors performed equally. Also, each N recommendation algorithm yielded unbiased N recommendations suggesting that each was a valid estimator of required N at maize growth stage V12. Overall results show that the amber and red sensors equally determine N variability in irrigated maize and could be very important tools for managing in-season application of N fertilizer.
590
$a
School code: 0053.
650
4
$a
Agriculture, Agronomy.
$3
1018679
650
4
$a
Remote Sensing.
$3
1018559
690
$a
0285
690
$a
0799
710
2
$a
Colorado State University.
$3
675646
773
0
$t
Dissertation Abstracts International
$g
70-08B.
790
1 0
$a
Westfall, Dwayne,
$e
advisor
790
$a
0053
791
$a
Ph.D.
792
$a
2009
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3374621
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9161019
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入