The interaction between US state‐level TFP growth and weather is investigated using growth‐accounting techniques. The focus is on examining how that interaction changed between the 1960s and the end of the twentieth century. An empirical approximation to the production frontier constructed using state‐level data and mathematical programming techniques is used to decompose observed state‐level agricultural TFP growth into four components: technical change, weather‐related shifts in the frontier, input/scale effects, and adaptation to the frontier. Technical change and adaptation to the frontier play a significant role in determining average state total factor productivity. Weather‐related effects differ across Climate‐Hub Regions but are of particular importance in the Midwest.

This paper examines the impact of technical efficiency on the optimal exit timing of farms in a stochastic dynamic framework. Starting from a standard real options approach, we incorporate technical efficiency via a production function and derive an optimal price trigger at which farms irreversibly exit production. Assuming separability of efficiency on the primal technology, we show that higher efficiency and higher returns to scale make the farm more reluctant to irreversibly exit production. We extend this model to a non-separable case, test it with West German farm-level data (2000 to 2008), and find evidence that efficiency is nonseparable. We find that higher volatility of milk prices and higher efficiency delay farms' exit from the market. Volatility, however, interacts with time-varying efficiency: the propensity of inefficient farms to exit the milk market attenuates under more volatile market conditions.

This article examines the efficiency of wind energy production. We quantify production losses in four wind parks across Germany for 19 wind turbines with non-convex efficiency analysis. In a second stage regression, we adapt the linear regression results of Kneip, Simar, Wilson (2014) to explain electricity losses by means of a bias-corrected truncated regression. Our results show that electricity losses amount to 27% of the maximal producible electricity. These losses can be mainly traced back to changing wind conditions while only 6 % are caused by turbine errors.

With the turn of the century, Australian agricultural productivity growth slowed dramatically. We investigate the connection between this slowdown and climatic factors by comparing regional‐level growth patterns before and after the advent of the Australian Millennium Droughts. The analysis incorporates climatic variates directly into the productivity accounting framework to reflect the stochastic nature of agricultural production, and measured productivity growth is decomposed into four components: technological change, weather‐related change, input‐scale adjustment, and diffusion (adaptation). Nonparametric productivity measurement and statistical techniques are used to quantify and examine the patterns of the observed productivity slowdown. The analysis suggests that the primary determinant of the slowdown is not a slowdown in technological innovation but climatic‐related changes in the pattern and rate of diffusion of technological advances.