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The authors explore the use of
cross-sectional analysis to measure the impacts of climate
change on agriculture. The impact literature, using
experiments on crops in laboratory settings combined with
simulation models, suggests that agriculture will be
strongly affected by climate change. The extent of these
effects varies by country and region. Therefore, local
experiments are needed for policy purposes, which becomes
expensive and difficult to implement for most developing
countries. The cross-sectional technique, as an alternative
approach, examines farm performance across a broad range of
climates. By seeing how farm performance changes with
climate, one can estimate long-run impacts. The advantage of
this approach is that it fully captures adaptation as each
farmer adapts to the climate they have lived in. The
technique measures the full net cost of climate change,
including the costs as well as the benefits of adaptation.
However, the technique is not concern-free. The four
chapters in this paper examine important potential concerns
of the cross-sectional method and how they could be
addressed, especially in developing countries. Data
availability is a major concern in developing countries. The
first chapter looks at whether estimating impacts using
individual farm data can substitute using agricultural
census data at the district level that is more difficult to
obtain in developing countries. The study, conducted in Sri
Lanka, finds that the individual farm data from surveys are
ideal for cross-sectional analysis. Another anticipated
problem with applying the cross-sectional approach to
developing countries is the absence of weather stations, or
discontinued weather data sets. Further, weather stations
tend to be concentrated in urban settings. Measures of
climate across the landscape, especially where farms are
located, are difficult to acquire. The second chapter
compares the use of satellite data with ground weather
stations. Analyzing these two sources of information, the
study reveals that satellite data can explain more of the
observed variation in farm performance than ground station
data. Because satellite data are readily available for the
entire planet, the availability of climate data will not be
a constraint. A continuing debate is whether farm
performance depends on just climate normals-the average
weather over a long period of time-or on climate variance
(variations away from the climate normal). Chapter 3 reveals
that climate normals and climate variance are highly
correlated. By adding climate variance, the studies can
begin to measure the importance of weather extremes as well
as normals. A host of studies have revealed that climate
affects agricultural performance. Since agriculture is a
primary source of income in rural areas, it follows that
climate might explain variations in rural income. This is
tested in the analysis in Chapter 4 and shown to be the
case. The analysis reveals that local people in rural areas
could be heavily affected by climate change even in
circumstances when the aggregate agricultural sector in the
country does fine.
