Agriculture Reference
In-Depth Information
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Chapters 5, 6, 7, and 8 provide details on how different types of the satellite
data can contribute to monitoring agricultural droughts.
C
rop Growth Models
Based on a typical crop growth period, the Julian dates are identified to
acquire satellite data for monitoring crop conditions. Weather conditions,
particularly during times of drought, cause a shift in the planting dates and,
consequently, in the commencement and termination of various phenolog-
ical phases. As a result, selecting dates to acquire satellite data becomes a
challenge. A biometeorological time scale model can be used to determine
commencement and termination of various phenological phases of a crop.
[7],
Bi
ometeorological Time Scale Model
Robertson (1968) developed the following model to estimate the com-
mencement and termination of five phenological phases (i.e., emergence,
jointing, heading, soft dough, and ripening) of wheat:
Line
——
0.2
——
Norm
PgEn
S
2
S
1
[
a
1
(
L
a
0
)
+
a
2
(L
−
a
0
)
2
][
b
1
(T
1
−
b
0
)
2
1
=
−
−
b
0
)
+
b
2
(T
1
+
c
1
(T
2
−
b
0
)
+
c
2
(T
2
−
b
0
)
2
]
[1.1]
where
a
0
,
a
1
,
a
2
,
b
0
,
b
1
,
b
2
,
c
0
,
c
1
, and
c
2
are coefficients (table 1.1),
L
is the
daily photoperiod (duration from sunrise to sunset, in hours), which can
be estimated for a given location following a procedure by Robertson and
Russelo (1968),
T
1
is the daily maximum temperature (°F),
T
2
is the daily
minimum temperature (°F); and
S
1
and
S
2
refer to the commencement and
the termination stages, respectively, for a phenological phase.
Kumar (1999) developed a computer program to apply Robertson's
biometeorological time scale model for the prairie region to determine dates
for the heading phase of wheat. The program helped select the satellite-
data-based normalized difference vegetation index (NDVI) data for the
heading phase. The average NDVI during the heading phase was a signifi-
cant variable for predicting wheat yield (Boken and Shaykewich, 2002).
In addition to the biometerological time scale model, various crop mod-
els have been developed to simulate crop growth using various agromete-
orological data. Some of the commonly used models are Decision Support
System for Agrotechnology Transfer (DSSAT; Tsuji et al., 1994; Hoogen-
boom et al., 1999), Erosion Productivity Impact Calculator or Environ-
mental Policy Integrated Climate (EPIC; Williams et al., 1989), and Agri-
cultural Production Systems Simulator (APSIM;
www.apsru.gov.au/apsru/
Predicting agricultural drought requires predicting crop yield. Chapter
4 describes some common techniques that can be used to predict crop
yield and hence agricultural droughts. In this context, variables, based on
weather and satellite data, play a pivotal role in the prediction process.
[7],
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