Lilyan E. Fulginiti
Richard K. Perrin
Date of this Version
Queiroz, P. 2021. “Essays on agricultural technology, resource allocation and the value of information”. A DISSERTATION. Dissertations & Theses in Agricultural Economics, University of Nebraska-Lincoln.
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.