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  1. Abstract

    In this study, we report 20 years of data from three ponderosa pine plantations in northern California. Our sites span a natural gradient of forest productivity where climate variability and edaphic conditions delineate marked differences in baseline productivity (approximately threefold). Experimental herbicide application and fertilization significantly reduced competition and improved tree growth by 1.4‐ to 2.2‐fold across sites. At the site of lowest productivity, where soils are poorly developed and water limiting, tree growth increased strongly in response to understory suppression. Small but significant improvements in tree growth were observed in response to understory suppression at the moderate‐productivity site. At the site of highest productivity, where climate is favorable and soils well developed, fertilization increased productivity to a greater extent than did understory suppression. In most cases, the effect of understory suppression and fertilization caused an unexpected growth release, exceeding the anticipated maximum productivity by >5 m of additional height and 60–100% more basal area. At the site of highest productivity, however, understory suppression caused a weak increase on late‐season growth compared to fertilization alone, suggesting a beneficial effect of understory vegetation on long‐term growth at that site. Tree ring cellulose carbon isotopes indicate a negative relationship between intrinsic water use efficiency (iWUE) and tree growth in control stands, which shifted to a positive relationship as both iWUE and tree growth increased in response to management. Cellulose oxygen isotope ratios (δ18O) were positively correlated with iWUE and negatively correlated with vapor pressure deficit across sites, but δ18O was not a strong predictor of tree growth.

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  2. Abstract The prediction of reactor antineutrino spectra will play a crucial role as reactor experiments enter the precision era. The positron energy spectrum of 3.5 million antineutrino inverse beta decay reactions observed by the Daya Bay experiment, in combination with the fission rates of fissile isotopes in the reactor, is used to extract the positron energy spectra resulting from the fission of specific isotopes. This information can be used to produce a precise, data-based prediction of the antineutrino energy spectrum in other reactor antineutrino experiments with different fission fractions than Daya Bay. The positron energy spectra are unfolded to obtain the antineutrino energy spectra by removing the contribution from detector response with the Wiener-SVD unfolding method. Consistent results are obtained with other unfolding methods. A technique to construct a data-based prediction of the reactor antineutrino energy spectrum is proposed and investigated. Given the reactor fission fractions, the technique can predict the energy spectrum to a 2% precision. In addition, we illustrate how to perform a rigorous comparison between the unfolded antineutrino spectrum and a theoretical model prediction that avoids the input model bias of the unfolding method. 
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