Crop 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.

Biometeorological Time Scale Model

Robertson (1968) developed the following model to estimate the commencement and termination of five phenological phases (i.e., emergence, jointing, heading, soft dough, and ripening) of wheat:

1 = {[a1 (L - a0) + a2(L - a0)2p1(71 - ¿0) + £2T - ¿0)2

where a0, a1, a2, b0, b1, b2, c0, c1, and c2 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), T1 is the daily maximum temperature (°F), T2is the daily minimum temperature (°F); and S1 and S2 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 significant variable for predicting wheat yield (Boken and Shaykewich, 2002).

In addition to the biometerological time scale model, various crop models 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 Environmental Policy Integrated Climate (EPIC; Williams et al., 1989), and Agricultural 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.

8 BASIC CONCEPTS AND DROUGHT ANALYSIS Table 1.1 Coefficients of the biometeorological time scale model

Development phasea

8 BASIC CONCEPTS AND DROUGHT ANALYSIS Table 1.1 Coefficients of the biometeorological time scale model

Development phasea

Coefficient

PE

EJ

JH

HS

SR

ao

[Vi = i]b

8.413

10.93

10.94

24.38

ai

1.005

0.9256

1.389

-1.140

a2

0

-0.06025

-0.08191

0

bo

44.37

23.64

42.65

42.18

37.67

bi

0.01086

-0.003512

0.0002958

0.0002458

0.00006733

b2

-0.0002230

0.00005026

0

0

0

C1

0.009732

0.0003666

0.0003943

0.00003109

0.0003442

C2

-0.0002267

-0.000004282

0

0

0

Source: Robertson (1968).

aPE = planting to emergence, EJ = emergence to jointing, JH = jointing to heading, HS = heading to soft dough, and SR = soft dough to ripening.

bFor determination of the PE, V1 = [a1(L-ao)+a2(L-ao)2] = 1.0 in equation 1.3, thus not requiring use of ao, a1, and a2 coefficients separately.

Source: Robertson (1968).

aPE = planting to emergence, EJ = emergence to jointing, JH = jointing to heading, HS = heading to soft dough, and SR = soft dough to ripening.

bFor determination of the PE, V1 = [a1(L-ao)+a2(L-ao)2] = 1.0 in equation 1.3, thus not requiring use of ao, a1, and a2 coefficients separately.

Various chapters in this book include variables that are used to predict agricultural droughts in different regions of the world.

In the years to come, a change in average patterns of temperature and precipitation will occur due to climate change. As a result, the existing patterns of drought-prone areas will undergo a transformation. Chapter 34 discusses effects of climate change and global warming on the occurrence of agricultural drought.

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