Dynamic Adjustment in US Agriculture under Climate Change

Abstract

We construct a stochastic dynamic dual model to investigate the structural adjustment of two aggregate output and three aggregate input categories in US agriculture under stochastic climatic change. More than a century of national annual data (1910–2011) is used in the empirical analysis. No constraints on asset fixity are imposed. Results indicate that, with rational expectations, both output categories as well as all input categories exhibit quasi‐fixity in response to market change and stochastic climate change. Crops adjust more than twice as fast as livestock—49% versus 20% of the way toward their long‐run equilibrium in one year. Fertilizer adjusts most rapidly toward equilibrium levels (88% in one year), and capital adjusts most slowly (5% in one year). Labor oscillates rather than converging smoothly toward equilibrium; its distance from equilibrium is the same as if it adjusted 59% of the way toward its optimal level in one year. Failing to anticipate climate change dramatically slows the estimated rate of adjustment for two netputs and modestly speeds the rate for two others, thus likely increasing overall adjustment costs. Failing to account for uncertainty in anticipated climate change has little impact on adjustment rates.

Asset fixity under state-contingent production uncertainty

“…The conclusion that the U.S. agricultural production system adjusts to its optimal level sluggishly is robust to functional form. This result is in accord with the findings of Vasavada and Chambers (1986), Luh and Stefanou (1996), and Asche, Kumbhakar and Tveteras (2008), and Yang and Shumway (2015) although none considered production uncertainty. Strong support is found for the existence of adjustment costs in individual adjustment paths of inputs, as all the functional forms reject the hypothesis of full variability for each input.…”

Impacts of Climate Change and Extreme Weather on U.S. Agricultural Productivity: Evidence and Projection

“…One body of work focuses on biophysical impacts through examining the relationship between climatic factors and individual commodity production or productivity, such as weather and crop yield or livestock production (e.g., Cobanov, and Schnitkey, 2003;Schlenker and Roberts, 2009;Lobell, Schlenker, and Costa-Roberts, 2011;Hatfield et al 2014;St-Pierre, Cobanov and Schnitkey, 2003;and Key and Sneeringer, 2014). A second body of work focusses on adaptive response at the individual/firm level through evaluating how an individual farm/firm/person reacts to climatic impacts, such as a farmer's behavior under uncertainty (risk management, see Schimmelpfennig, 1996;Kim and Chavas, 2003;Falco and Veronesi, 2013;Yang and Shumway, 2015.) The third stream of literature addresses impacts at a regional/national/sectoral scale, considering both biophysical effects and adaptation.…”

EstimatingEx AnteCost Functions for Stochastic Technologies

“…Pope and Just (1996) used earlier versions (1948-1989 of this data set. Other studies using different versions of these data include Ball (1985Ball ( , 1988, Chambers and Lichtenberg (1994), Chavas (2008), and Yang and Shumway (2016). However, as we point out below, these earlier studies did not recognize some of the time-series problems that are now apparent in these data.…”