Agricultural Economics Department
First Advisor
Lilyan E. Fulginiti
Second Advisor
Richard K. Perrin
Date of this Version
Spring 4-22-2021
Document Type
Dissertation
Citation
A dissertaton presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Doctor of Philosophy
Major: Agricultural Economics
Under the supervision of Professors Lilyan E. Fulginiti and Richard K. Perrin
Lincoln, Nebraska, April 2021
Abstract
This dissertation studied the adoption of agricultural technologies and the value of information for the allocation of resources in agriculture. Chapter 1 studied traditional (e.g., land and labor) and commercial (e.g., machinery and fertilizers) inputs in South American agriculture. Acemoglu’s directed technical change was used to estimate the process of induced innovation using deforestation as source of exogenous variation for the agricultural land supply. The results indicated that larger availability of land in intensive deforestation countries caused more land-complementary inputs (machinery) to be used relative to labor-complementary inputs (fertilizers). Technical change was biased towards land. Chapter 2 studied nitrogen fertilizer application in U.S. agriculture. Soil information (signal) allowed the adoption of variable rate technology (VRT) applications of nitrogen across the plots (cells) of the fields. I provided a Bayesian structural model, based on the Expected Value of Sample Information (EVSI), with an application using data from the Data-Intensive Farm Management (DIFM) project to evaluate the expected returns of VRT. Soil electroconductivity (EC) and VRT provided low expected returns which can be explained by EC being “poorly” correlated with the true soil conditions and/or the quality of the soil might be uniform across the fields, hence, not supporting the VRT adoption. Chapter 3 used remote sensing information to estimate the effects of droughts on agriculture for Brazilian municipalities. First, the effect of droughts for all the corn- and soybeans-producing Brazilian municipalities was estimated, then a model adding remote sensing data was estimated for the municipalities from a soybeans-producing region of Southern Brazil, both for the 2002-2016 period. The results implied that the lack of biophysical variables, reflecting the interaction among the soil, the plant, and the atmosphere, would bias the drought effects. This is important because economic decisions are made based on the effects of climate conditions in agriculture and remote sensing information can provide more reliable estimates of the true climatic effects.
Advisors: Lilyan E. Fulginiti and Richard K. Perrin.
Comments
Copyright © 2021, Pedro W. Vertino de Queiroz