Monitoring and Managing Agricultural Drought in India - Monitoring and Predicting Agricultural Drought: A Global Study - page 302

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levels in 47 major reservoirs that are monitored by the Central Water Com-
mission, employment, and other activities affected by droughts. The infor-
mation thus collected by the group is sent to various agrometeorological
weather advisory field units located in different agroclimatic zones of the
country for preparing bulletins to farmers on various agricultural opera-
tions. Additionally, the forecasts are broadcasted through radio, television,
and various newspapers. This helps monitor the development of drought
conditions across the country.

M oisture Adequacy Index

The Central Arid Zone Research Institute, Jodhpur monitors agricultural
drought in the Indian arid regions by using the moisture adequacy index
(MAI). The MAI is equal to the ratio (expressed as a percentage) of actual
evapotranspiration (AET) to the potential evapotranspiration (PET) fol-
lowing a soil water-balancing approach during a cropping season (Ramana
Rao et al., 1981; Rao, 1997; Sastri et al., 1982).

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A ridity Anomaly Index

A drought research unit was established in 1967 at the IMD, Pune. This
unit prepares biweekly aridity anomaly reports for the southwest monsoon
season for the entire nation and during the northeast monsoon season for
five meteorological subdivisions (coastal Andhra Pradesh, Rayalaseema,
south interior Karnataka, Kerala, and Tamil Nadu and Pondichery). The
aridity anomalies are determined based on water balance studies (Thorn-
thwaite and Mather, 1955) and are classified into various categories of arid
conditions (e.g., mild, moderate, and severe) for monitoring and assessing
agricultural droughts in the country. These reports are sent to research
institutes and various agricultural authorities of state and central govern-
ments to help in agricultural planning.

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Yi eld Modeling

Predicting yield of the major crop of a drought-prone region is a simple
and practical way of drought monitoring for the region. For the Indian
arid zone, pearl millet is the single most important crop. Using variables
related to delay in sowing, monthly rainfall, and the number of rainy days
in a month, Kumar (1998) developed early warning models for predicting
pearl millet yield for Jodhpur district (in Rajasthan State), which explained
up to 81% of the yield variation.
The drought research unit developed regressions equations between past
yields and weather variables for forecasting rice yields during Kharif season
for 15 states (comprising 26 meteorological subdivisions) and wheat yields
during Rabi season for 12 states (comprising 16 meteorological subdivi-
sions) as well as annual wheat yields for the entire nation. These forecasts