کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5750273 1619696 2017 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Assessment of soil water, carbon and nitrogen cycling in reseeded grassland on the North Wyke Farm Platform using a process-based model
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم محیط زیست شیمی زیست محیطی
پیش نمایش صفحه اول مقاله
Assessment of soil water, carbon and nitrogen cycling in reseeded grassland on the North Wyke Farm Platform using a process-based model
چکیده انگلیسی


- SPACSYS could simulate forage cutting dry matter, soil movement and nutrient cycling.
- Simulation showed that deep-rooting grass may reduce water loss from soil.
- Deep-rooting grass increased soil carbon storage from simulation results.
- Simulation demonstrated deep-rooting grass lowered soil denitrification loss.

The North Wyke Farm Platform (NWFP) generates large volumes of temporally-indexed data that provides a valuable test-bed for agricultural mathematical models in temperate grasslands. In our study, we used the primary datasets generated from the NWFP (https://nwfp.rothamsted.ac.uk/) to validate the SPACSYS model in terms of the dynamics of water loss and forage dry matter yield estimated through cutting. The SPACSYS model is capable of simulating soil water, carbon (C) and nitrogen (N) balance in the soil-plant-atmosphere system. The validated model was then used to simulate the responses of soil water, C and N to reseeding grass cultivars with either high sugar (Lolium perenne L. cv. AberMagic) or deep rooting (Festulolium cv. Prior) traits. Simulation results demonstrated that the SPACSYS model could predict reliably soil water, C and N cycling in reseeded grassland. Compared to AberMagic, the Prior grass could fix more C in the second year following reseeding, whereas less C was lost through soil respiration in the first transition year. In comparison to the grass cultivar of the permanent pasture that existed before reseeding, both grasses reduced N losses through runoff and contributed to reducing water loss, especially Prior in relation to the latter. The SPACSYS model could predict these differences as supported by the rich dataset from the NWFP, providing a tool for future predictions on less characterized pasture.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Science of The Total Environment - Volumes 603–604, 15 December 2017, Pages 27-37
نویسندگان
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