Land Quality and Agricultural Productivity: A Distance Function Approach
adjust for differences in land quality among countries by including the ratio of irrigated land to
total land area and the ratio of cropland to pastureland as variables in their econometric models
of agricultural labor productivity.
Peterson’s (1987) unpublished land quality index has also been used as a land quality
indicator in agricultural productivity studies. Peterson’s index is based on the share of a
country’ s agricultural land that is not irrigated, the share of its cropland that is irrigated, and the
log of its long-run average annual precipitation, weighted by coefficients derived from a cross-
sectional analysis of land prices in the US. Frisvold and Ingram (1995) found this land quality
indicator to be highly significant in explaining differences in land productivity for a sample of 28
sub-Saharan African countries.
More recently, Craig, Pardey, and Roseboom (1997) econometrically estimated the
agricultural labor productivity of 98 countries and included long-run average rainfall, the
percentage of land that was arable, and the percentage of land not irrigated as proxies for land
quality. They found that countries with higher land quality had higher labor productivity.
Mundlak, Larson, and Butzer (1999) used two proxies for land quality—potential dry matter and
a factor of water deficit—and found both to have a significant impact on explaining cross-
country differences in agricultural output. Jaenicke and Lengnick (1999) developed a method
based on distance functions to derive a soil quality index at the plot level. Soil quality attributes
were considered directly as inputs in the production model. A regression model was used to
determine the importance of each attribute in explaining productivity differences between plots.
Researchers have had some success using various proxies for land quality in econometric
productivity studies. However, newly available, spatially referenced land and climate data have
motivated the search for improved land quality indicators. The hope is that such indicators will