New Zealand Journal of
Agricultural Research abstracts
Establishing grassland yield
models using Projection Pursuit
Regression Method
Li Jianlong1
Qi Jiaguo2
Zhao Dehua1
Jiang Ping1*
Xu Sheng1
1Department
of Biological
Science
Nanjing
University
Nanjing City
210093, PR China
2Center
for Global Change and
Earth Observations
Michigan State
University
East Lansing
MI 48823, USA
*Author for correspondence.
Abstract There
is a great deal of interest in
grassland yields as they are a required component in the calculation of
carrying capacity, which is very important in grassland management
practices. Traditional approaches to yield information are often
time-consuming, expensive, and limited in areal samplings. It is
therefore desirable to develop new methods that can provide quick,
easy, and cost-effective estimates of grassland yields over large
areas. In this study, an experiment was conducted at Fukang County,
Xinjiang, China, to collect in
situ data and remote sensing
imagery. The in situ
data included green herbaceous forage
yields and weather information at four grassland types (plain desert,
saline steppe, hill desert steppe, and mountain meadow), while the
remote sensing images were acquired by the Landsat satellite TM sensors
at 30-m resolution. Analysis of this dataset resulted in the
development of a yield model using the Projection Pursuit Regression
(PPR) method. The PPR model was compared with traditional multivariate
linear statistical methods and was found to be much more accurate in
estimating grassland yields. When applied to the four grassland types,
the PPR models resulted in an accuracy of 81.76, 88.61, 83.50, and
92.35% for plain desert, saline steppe, hill desert steppe, and
mountain meadow, respectively. Therefore, the new PPR model has the
potential to provide an effective method to estimate grassland yields
over large geographic areas.
Keywords grassland;
estimated grassland yield;
Projection Pursuit Regression Method; PPR model; assessment of
grassland resources
A03045; Received 1 October
2002; accepted 20 July 2004; Online
publication date 21 March 2005
New Zealand Journal of
Agricultural Research, 2005, Vol. 48:
47–55
0028–8233/05/4801–0047 © The Royal Society of New Zealand 2005
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