The Economics of Desertification, Land Degradation, and Drought

The preceding discussion shows the complex relationship of the proximate and underlying
causes, which makes it hard to generalize using a simple relationship of one underlying factor with a
proximate cause of land degradation. The results imply that one underlying factor is not, in itself,
sufficient to address land degradation. Rather, a number of underlying factors need to be taken into
account when designing policies to prevent or mitigate land degradation.
Associations between Potential Drivers of Land Degradation
Land cover change is the most direct and pervasive anthropogenic factor used to determine land
improvement or degradation (Vitousek 1994; Morawitz et al. 2006). Several studies have used the
normalized difference vegetation index (NDVI) and other measures based on the NDVI, as an indicator of
changes in ecosystem productivity and land degradation. In chapter 2, we have described and analyzed
the limitations and criticisms related to the use of this measure. However, NDVI remains the only dataset
available at the global level and the only dataset that reliably provides information about the condition of
the aboveground biomass. Mindful of all the limitations, we follow an approach similar to Bai et al.
(2008b) and investigate the relationship between changes in NDVI (from 1981 to 2006) and some key
biophysical and socioeconomic variables (Table 2.7).
Data and Methods
The analysis of NDVI change is based on data derived from the Global Inventory Modeling and Mapping
Studies (GIMMS), which supply NDVI data from July 1981 to December 2006. In the GIMMS dataset,
two NDVI observations are available for each month and therefore each year is composed of 24 sets of
global data 26 (Pinzon, Brown, and Tucker 2005; Tucker et al. 2005). For each pixel (which is the unit of
observation in our analysis) and year, the average NDVI is computed then averaged across two time
periods: (1) the baseline of 1982 to 1986 and (2) the end line of 2002 to 2006. Subtracting pixel by pixel
the baseline from the end line NDVI value we obtain the change in average NDVI.
NDVI values in agricultural areas are strongly dependent on farmers’ production decisions (for
example, crop choices, fertilizer usage, and irrigation). As a consequence, the relationship between NDVI
and land degradation for the observations that cover agricultural areas is tenuous. We decided therefore to
eliminate from the dataset those observations that cover areas where agriculture is the predominant land
use. In order to perform this operation, we used data from the Spatial Analysis Model (SPAM). This
model is used to identify at the global level areas where agriculture is predominant. Specifically, we
identified all the locations (roughly one pixel of ten-by-ten kilometers at the equator) where cropland
represents 70 percent or more of the land use. The NDVI observations that fall in these areas were
dropped from the study.
The choice of the biophysical and socioeconomic variables used to explain the change is strongly
dictated by data availability. Unfortunately, important information on poverty, cost of access, road
networks, and urban areas is not available as panel data and therefore could not be included in the
analysis. The variables used (Table 2.7) include precipitation, population density, government
effectiveness, agricultural intensification (proxied by fertilizer application), and country gross domestic
product (GDP). 27 To avoid influence by abnormal years, we take an average of four consecutive years for
the baseline and end line periods. However, not all data were available for the baseline and end line
periods. In such cases, we used time periods closest to the two NDVI time periods (Table 2.7).
We know a priori changes in precipitation have a strong effect on NDVI and expect a positive
correlation between positive changes in precipitation and NDVI. The impact of population density on
land degradation is ambiguous. While the induced innovation theory (Hayami and Ruttan 1970; Boserup
1965) predicts that farmers will intensify their land investment as population increases, other studies have
26
This includes one maximum composite value from the first 15 days of the month and one from day 16 to the end of the
month (Tucker, Pinzon, and Brown 2004).
27
As discussed below, we also included the squared value of GDP to account for possible non-linearity.
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