Random fluctuations in temperature and precipitation have substantial impacts on agricultural output. However, the contribution of these changing configurations in weather to total factor productivity (TFP) growth has not been addressed explicitly in econometric analyses. Thus, the key objective of this study is to quantify and to investigate the role of changing weather patterns in explaining yearly fluctuations in TFP. For this purpose, we define TFP to be a measure of total output divided by a measure of total input. We estimate a stochastic production frontier model using U.S. state-level agricultural data incorporating growing season temperature and precipitation, and intra-annual standard deviations of temperature and precipitation for the period 1960–2004. We use the estimated parameters of the model to compute a TFP index that has good axiomatic properties. We then decompose TFP growth in each state into weather effects, technological progress, technical efficiency, and scale-mix efficiency changes. This approach improves our understanding of the role of different components of TFP in agricultural productivity growth. We find that annual TFP growth averaged 1.56% between 1960 and 2004. Moreover, we observe substantial heterogeneity in weather effects across states and over time.

Analyses of the costs of regulating greenhouse gas emissions from dairy production, which could be used to assess the effectiveness of alternative policy measures, is a missing link in the literature. This article addresses this gap by establishing the economic impact associated with a hypothetical greenhouse gas environmental regulatory regime across major dairy producing counties in the United States. In doing so, the article makes three important contributions to the literature. First, it develops a comprehensive pollution index based on Environmental Protection Agency methodologies, which contrasts with previous studies that rely on partial measures based only on surplus nitrogen stemming from the over‐application of fertilizer. Second, the article uses a directional output distance function, an approach that has not been employed previously to evaluate polluting technologies in the U.S. dairy sector. Third, the article incorporates a four‐way error approach that accounts for unobserved county heterogeneity, time‐invariant persistent technical efficiency, time‐varying transient technical efficiency, and a random error. The results indicate that regulating greenhouse gas emissions from dairy farming would induce a 5‐percentage point increase in average technical efficiency. In addition, the economic costs of implementing this hypothetical regulatory framework exhibit significant spatial variation across counties in the United States.

This study makes two key contributions to the agricultural productivity literature. First, it demonstrates, using US agricultural state-level data, how a random-parameters stochastic frontier model can be used to account for environmental heterogeneity across decision-making units. Second, it uses the estimated parameters of the model to compute and decompose a productivity index that satisfies several key axioms from index theory. Because the decomposition explicitly accounts for both observed and unobserved environmental effects, we are able to obtain a more realistic and flexible assessment of productivity growth. We find substantial differences between productivity results generated using a model with random slope parameters and those generated using a more conventional model with constant slope parameters.

There is increasing interest in how big data will affect U.S. crop production, yet little is known about the field‐level effects of “small” (i.e., individual farm) data. We help to fill this void by studying the relationship between Midwest corn production and the information contained in yield and soil maps. Research on this relationship is lacking, perhaps because maps are information inputs that may not enter the production function in a way comparable to conventional inputs. Using detailed USDA survey data, we implement a stochastic frontier analysis to evaluate how mapping technologies influence field productivity. Controlling for farmers' endogenous choice of technologies, we find evidence of direct (frontier‐shifting) and indirect (efficiency‐enhancing) productivity effects. Depending on model, field output increases by 5.6% or 11.9% as a result of map adoption. Yield maps increase expected efficiency by 8.5%, and soil maps increase expected efficiency by 7.2%, on average. These effects differ by operator demographics, such as years of experience with the field, and structural characteristics, such as whether the field is insured and if it is owned by the operator. Given that yield and soil maps are not universally adopted, our results suggest there remain opportunities to increase productivity through field‐level information use.

This study evaluates the environmental performance of northeastern U.S. dairy operations that differ in size using a directional output-distance function that measures the joint production of milk and emissions while incorporating a fourway error approach that captures farm-size heterogeneity, transient and persistent technical efficiency, and random errors. For the emission component, a comprehensive pollution index is generated that incorporates three major sources of pollution in dairy farming: fuel, fertilizer, and livestock. Computed shadow prices and Morishima elasticities of substitution reveal that large dairy operations are environmentally inefficient compared to their smaller counterparts.