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/
Products/apsim.htm).
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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