John G. King

Mountain erosion over 10 yr, 10 k.y., and 10 m.y. time scales

We used cosmogenic 10 Be to measure erosion rates over 10 k.y. time scales at 32 Idaho mountain catchments, ranging from small experimental watersheds (0.2 km 2 )t o large river basins (35 000 km 2 ). These long-term sediment yields are, on average, 17 times higher than stream sediment fluxes measured over 10‐84 yr, but are consistent with 10 m.y. erosion rates measured by apatite fission tracks. Our results imply that conventional sediment-yield measurements—even those made over decades—can greatly underestimate long-term average rates of sediment delivery and thus overestimate the life spans of engineered reservoirs. Our observations also suggest that sediment delivery from mountainous terrain is extremely episodic, sporadically subjecting mountain stream ecosystems to extensive disturbance.

The Contribution of Private Industry to Agricultural Innovation

Led by seed biotechnology, private-sector spending in agricultural R&D grew 43% from 1994 to 2010. Most of the increase in global agricultural production over the past half-century has come from raising crop and livestock yields rather than through area expansion. This growth in productivity is attributed largely to investments in research and innovation (1). Since around 1990, there has been a decline in the rate of growth in yield per area harvested for several important crops (2). In parallel, the rate of growth in public spending on agricultural research and development (R&D) has also fallen, which may account for declining crop yield growth and may be contributing to rising food prices (3).

Reply to comment by Claude Michel on ''A general power equation for predicting bed load transport rates in gravel bed rivers''

[1] We thank Michel [2005] for the opportunity to improve our bed load transport equation [Barry et al., 2004, equation (6)] and to resolve the dimensional complexity that he identified. However, we do not believe that the alternative bed load transport equation proposed by Michel [2005] provides either the mechanistic insight or predictive power of our transport equation. [2] Although some bed load transport data exhibit nonlinear trends in log-log plots of transport rate versus discharge, a simple linear function is sufficient to describe our data [Barry et al., 2004, paragraph 43]. The Figure 7 data of Barry et al. [2004] could be fit by a nonlinear function as suggested by Michel [2005], but we believe this to be an unnecessary complication, particularly given how well our simple equation predicts observed transport rates compared to other more complex equations, such as Parker’s [1991] three-part bed load transport function [Barry et al., 2004, Figure 11]. Furthermore, an important aspect of our equation, that is not preserved in Michel’s alternative, is the between-site variation in the exponent of the transport function that results from supply related channel armoring (i.e., transport capacity in excess of bed load sediment supply) which provides a mechanistic understanding of the bed load transport process [Barry et al., 2004].Michel [2005] proposes a bed load transport equation that mimics our equation in terms of the range of exponents that we observe (i.e., 1.5–4 [Barry et al., 2004, Figure 8a] but lacks the mechanistic insight and consequent predictive power. Moreover, Michel’s equation requires a sufficient number of bed load transport observations across a broad range of discharges to empirically calibrate his a and b values. [3] Michel [2005] correctly points out a dimensional complexity of our transport equation that we resolve here by scaling discharge by the 2-year flood (Q2)

The Creation of New Administrative Data

Administrative data have a number of advantages. They can be used to provide information quite quickly and reliably, with minimum burden. The STAR METRICS program is a good example: legislators wanted immediate information about jobs resulting from the US stimulus funding, and agencies needed better data for analysis and reporting, but manual reporting of information from researchers and research institutions was unreliable and burdensome. The LEHD and LEED programs are examples of how administrative data can be used to create information about important economic activities that can’t readily be captured by survey data: namely the dynamic interactions of workers and firms – jobs.