The role of soils in production: Aggregation, separability, and yield decomposition in Kenyan agriculture.

摘要

Agricultural production relies on soils. Increasing global population and the impact of climate change threaten the sustainability of soil for agricultural production. For these reasons, it is necessary to broaden present current methodological approaches to incorporating soil into economic analysis.;The first essay proposes a methodology to aggregate quantitative soil characteristics through the use of separability theory in a Data Envelopment Analysis framework. This yields an aggregate soil-quality measure that appropriately aggregates soil characteristics. The application is to Kenyan maize farmers.;The second essay develops a nonparametric statistical test of structural separability based on a bias correction of a central limit theorem for Data Envelopment Analysis estimators developed in Kneip, Simar and Wilson (2015a). The proposed nonparametric test for structural separability adapts the statistical procedures to test technology restrictions present in Kneip, Simar and Wilson (2015b). Monte Carlo experiments determine the size and power properties of the proposed test. An empirical analysis of Kenyan household farmers illustrates the use of the methodology.;Global needs for higher agricultural production require understanding whether the frequently noted inverse land size-yield relationship is a true empirical regularity or an artifact of data collection methods. To examine this relationship, the third essay of this dissertation generalizes productivity decomposition methods to incorporate the quantification of a soil-productivity contribution. The generalized method decomposes a yield index into separate components attributable to (1) efficiency, (2) soil quality, (3) land size, (4) variable inputs, (5) capital inputs, and (6) output mix. Nonparametric productivity accounting methods are used to decompose the inverse land size-yield relationship in a multi-output representation of the technology without specific assumptions on returns to scale. A strongly significant inverse land size-yield relationship is present among Kenyan farmers.