Why Rice Farmers Don't Sail: Coastal Subsistence Traditions and Maritime Trends in Early China

The Lower Yangtze River Valley is a key region for the early development of rice farming and the emergence of wet rice paddy field systems. Subsistence evidence from Neolithic sites in this area highlights the importance of freshwater wetlands for both plant and animal food resources. Early Neolithic rice cultivators looked inland, especially to wetlands and nearby woodlands, for their main protein sources. Links to the sea among these Neolithic populations are notably scarce. Due to the high yields of wet rice, compared with other staple crops as well as dryland rice, the wetland rice focused subsistence strategy of the Lower Yangtze would have supported high, and increasing, local population densities. Paddy agriculture demands labor input and water management on a large scale, which would have stimulated and reinforced trends towards more complex societies, such as that represented by Liangzhu in the lower Yangtze region. Population growth could have been largely absorbed locally, suggesting that population packing, not migration, was the dominant trend. Other case studies of agricultural dispersal, for the Korean Peninsula and Japan further illustrate the lack of correlation between the spread of rice agriculture and wet rice cultivation. Although wet rice cultivation was a pull factor that drew local populations towards increased density and increased social complexity, it did not apparently push groups to migrate outwards. Instead, the transition from wetland to rain fed rice cultivation systems and/or the integration of rice with rain fed millet crops are much more likely to have driven the demographic dynamics that underpin early farmer migrations and crop dispersal

Potential volcanic impacts on future climate variability

Volcanic activity plays a strong role in modulating climate variability (ref. 1). Most model projections of the twenty-first century, however, under-sample future volcanic effects by not representing the range of plausible eruption scenarios (ref. 2,3,4). Here, we explore how sixty possible volcanic futures, consistent with ice-core records (ref. 5), impact climate variability projections of the Norwegian Earth System Model (NorESM) (ref. 6) under RCP4.5 (ref. 7). The inclusion of volcanic forcing enhances climate variability on annual-to-decadal timescales. Although decades with negative global temperature trends become ∼50% more commonplace with volcanic activity, these are unlikely to be able to mitigate long-term anthropogenic warming. Volcanic activity also impacts probabilistic projections of global radiation, sea level, ocean circulation, and sea-ice variability, the local-scale effects of which are detectable when quantifying the time of emergence (ref. 8). These results highlight the importance and feasibility of representing volcanic uncertainty in future climate assessments

Bootstrap Procedures for Recursive Estimation Schemes with Applications to Forecast Model Selection

In recent years it has become apparent that many of the classical testing procedures used to select amongst alternative economic theories and economic models are not realistic. In particular, researchers have become more aware of the fact that parameter estimation error and data dependence play a crucial role in test statistic limiting distributions, a role which had hitherto been ignored to a large extent. Given the fact that one of the primary ways for comparing different models and theories is via use of predictive accuracy tests, it is perhaps not surprising that a large literature on the topic has developed over the last 10 years, including, for example, important papers by Diebold and Mariano (1995), West (1996), and White (2000). In this literature, it is quite common to compare multiple models (which are possibly all misspecified - i.e. they are all approximations of some unknown true model) in terms of their out of sample predictive ability, for given loss function. Our objectives in this paper are twofold. First, we introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two applications where predictive accuracy tests are made operational using our new bootstrap procedures. One of the applications outlines a consistent test for out-of-sample nonlinear Granger causality, and the other outlines a test for selecting amongst multiple alternative forecasting models, all of which may be viewed as approximations of some unknown underlying model. More specifically, our examples extend the White (2000) reality check to the case of non vanishing parameter estimation error, and extend the integrated conditional moment (ICM) tests of Bierens (1982, 1990) and Bierens and Ploberger (1997) to the case of out-of-sample prediction. Of note is that in both of these examples, it is shown that appropriate re-centering of the bootstrap score is required in order to ensure that the tests are properly sized, and the need for such re-centering is shown to arise quite naturally when testing hypotheses of predictive accuracy. The results of a Monte Carlo investigation of the ICM test suggest that the bootstrap procedure proposed in this paper yield tests with reasonable finite sample properties for samples with as few as 300 observations

Planung und Gesellschaft : ein "Echtzeit"-System im Raum (Zeitgeographische Aspekte der Raumplanung)

No abstract available