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Optimal nitrogen fertilization of boreal conifer forest
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作者 Timo Pukkala 《Forest Ecosystems》 SCIE CSCD 2017年第2期123-132,共10页
Background: Forest fertilization offers a means to increase the production of renewable resources.Nitrogen is the most common fertilizer in boreal upland forests.There is plenty of research on the effect of nitrogen ... Background: Forest fertilization offers a means to increase the production of renewable resources.Nitrogen is the most common fertilizer in boreal upland forests.There is plenty of research on the effect of nitrogen fertilization on volume growth, but less research on the optimal timing of fertilization and optimal management of fertilized stands.Methods: This study used simulation and optimization to analyze the profitability of fertilization, optimal management of fertilized stands and the effects of fertilization on cash flows and timber yields.The management of 100 stands representing the most common growing sites of Scots pine and Norway spruce was optimized.Results: Fertilization improved profitability in most of the analyzed stands.Profitability improved most in spruce stands growing on mesic site.Improving stem quality increased the economic benefit of fertilization.The timber yields of medium-aged conifer stands can be increased by almost 1 m^3·ha^(-1)·a^(-1)(15%) in sub-xeric pine and mesic spruce sites and about 0.5 m^3·ha^(-1)·a^(-1)(5%) in mesic pine and herb-rich spruce sites when the recommended nitrogen dose(150kg·ha^(-1)) is applied once in 30 years.Conclusions: Nitrogen fertilization of boreal conifer forest should be used mainly in spruce-dominated stands growing on medium sites.The gains are the highest in stands where the mean tree diameter is 16–20 cm and stand basal area is 14–20 m^2·ha^(-1). 展开更多
关键词 forest fertilization Optimal management response model
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The dynamic response of forest vegetation to hydro- thermal conditions in the Funiu Mountains of western Henan Province 被引量:3
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作者 朱文博 李双成 《Journal of Geographical Sciences》 SCIE CSCD 2017年第5期565-578,共14页
This paper uses HJ-1 satellite multi-spectral and multi-temporal data to extract forest vegetation information in the Funiu Mountain region. The S-G filtering algorithm was employed to reconstruct the MODIS EVI(Enhan... This paper uses HJ-1 satellite multi-spectral and multi-temporal data to extract forest vegetation information in the Funiu Mountain region. The S-G filtering algorithm was employed to reconstruct the MODIS EVI(Enhanced Vegetation Index) time-series data for the period of 2000–2013, and these data were correlated with air temperature and precipitation data to explore the responses of forest vegetation to hydrothermal conditions. The results showed that:(1) the Funiu Mountain region has relatively high and increasing forest coverage with an average EVI of 0.48 over the study period, and the EVI first shows a decreasing trend with increased elevation below 200 m, then an increasing trend from 200–1700 m, and finally a decreasing trend above 1700 m. However, obvious differences could be identified in the responses of different forest vegetation types to climate change. Broad-leaf deciduous forest, being the dominant forest type in the region, had the most significant EVI increase.(2) Temperature in the region showed an increasing trend over the 14 years of the study with an anomaly increasing rate of 0.27℃/10a; a fluctuating yet increasing trend could be identified for the precipitation anomaly percentage.(3) Among all vegetation types, the evergreen broad-leaf forest has the closest EVI-temperature correlation, whereas the mixed evergreen and deciduous forest has the weakest. Almost all forest types showed a weak negative EVI-precipitation correlation, except the mixed evergreen and deciduous forest with a weak positive correlation.(4) There is a slight delay in forest vegetation responses to air temperature and precipitation, with half a month only for limited areas of the mixed evergreen and deciduous forest. 展开更多
关键词 forest vegetation hydro-thermal condition response time lag EVI Funiu Mountains China
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