کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10997802 1318699 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluating the grassland net primary productivity of southern China from 2000 to 2011 using a new climate productivity model
ترجمه فارسی عنوان
ارزیابی بهره وری اولیه خالص چمنزارهای جنوب چین از سالهای 2000 تا 2011 با استفاده از مدل بهره وری جدید آب و هوا
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک علوم کشاورزی و بیولوژیک (عمومی)
چکیده انگلیسی
Grassland is the important component of the terrestrial ecosystems. Estimating net primary productivity (NPP) of grassland ecosystem has been a central focus in global climate change researches. To simulate the grassland NPP in southern China, we built a new climate productivity model, and validated the model with the measured data from different years in the past. The results showed that there was a logarithmic correlation between the grassland NPP and the mean annual temperature, and there was a linear positive correlation between the grassland NPP and the annual precipitation in southern China. All these results reached a very significant level (P<0.01). There was a good correlation between the simulated and the measured NPP, with R2 of 0.8027, reaching the very significant level. Meanwhile, both root mean square errors (RMSE) and relative root-mean-square errors (RRMSE) stayed at a relatively low level, showing that the simulation results of the model were reliable. The NPP values in the study area had a decreasing trend from east to west and from south to north, and the mean NPP was 471.62 g C m−2 from 2000 to 2011. Additionally, there was a rising trend year by year for the mean annual NPP of southern grassland and the tilt rate of the mean annual NPP was 3.49 g C m−2 yr−1 in recent 12 years. The above results provided a new method for grassland NPP estimation in southern China.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Integrative Agriculture - Volume 15, Issue 7, July 2016, Pages 1638-1644
نویسندگان
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