The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981-2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.total factor productivity | agricultural economy | economic growth | climate impacts | crop yield A long-standing challenge of climate impact assessment has been to determine how climate has influenced the agricultural economy, and how its effects may change in the future. Climate affects agriculture regionally, depending not only on local weather factors but also on specific crops, livestock, and related goods and services, as well as agricultural systems, infrastructures, and interventions. Aggregating these disparate and potentially contradictory regional impacts into larger-scale economic outcomes is particularly difficult because the ultimate consequences are influenced by market fluctuations and policy incentives. As a result, understanding of how climate has influenced the agricultural economy is limited, making projection of the future under climate change extremely uncertain.This uncertainty is reflected in the lack of consensus regarding the overall impacts of climate change on US agriculture (1, 2). In general, studies follow two approaches, both focusing on partial productivity measures or local economic indicators. One approach seeks to determine the impact of weather shocks on common partial productivity measures such as crop yield (3-7). These studies tend to show that weather variability substantially influences local crop production. The other approach aims to iden...

No abstract

This research introduces a novel fault diagnosis method for an industrial robot based on manifold learning algorithms, Treelet Transform (TT) and Naive Bayes. The vibration signals of an industrial robot working under three working conditions are acquired as the raw data. Three typical manifold learning algorithms, Principal Component Analysis (PCA), Locality Preserving Projections (LPPs), and Isometric Feature Mapping (ISOMAP), are utilized to extract three-dimensional features from the vibration signals. Then, these features were combined into nine-dimensional features and, these nine-dimensional features were reduced to three-dimensional feature vectors by TT. Finally, a Naive Bayes model is trained with these three-dimensional feature vectors. Experimental results show that compared with the three methods, PCA, LPP, and ISOMAP, the accuracy of the proposed combined method is higher than the single method. The fault diagnosis method presented in this paper is easy to implement and can effectively identify the fault types.