Journal
AGRONOMY-BASEL
Volume 11, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/agronomy11040677
Keywords
LINGRA; nitrogen; soil moisture deficit; soil organic matter; water stress
Categories
Funding
- Natural Environment Research Council [NE/R017387/1]
- Sustainable Agriculture Research and Innovation Club (SARIC)
- Rothamsted Research's Institute Strategic Programme 'Soil to Nutrition' [BBS/E/C/000I0330]
- UK Biotechnology and Biological Sciences Research Council (BBSRC)
- BBSRC [BBS/E/C/000I0330, BBS/E/C/000I0320] Funding Source: UKRI
- NERC [NE/R017387/1] Funding Source: UKRI
Ask authors/readers for more resources
This study investigates the effectiveness of the LINGRA-N-Plus model in simulating the interaction of climate, soil, and management on ryegrass yields in England and Wales. The model showed improved efficiency in predicting grass yields compared to the original model, especially in scenarios involving nitrogen application and harvest intervals. The results emphasize the usefulness of grass models like LINGRA-N-Plus in exploring the combined effects of various factors on grass productivity.
This study examines the effectiveness of a model called LINGRA-N-Plus to simulate the interaction of climate, soil and management on the green leaf and total dry matter yields of ryegrass in England and Wales. The LINGRA-N-Plus model includes modifications of the LINGRA-N model such as temperature- and moisture-dependent soil nitrogen mineralization and differential partitioning to leaves and stems with thermal time from the last harvest. The resulting model was calibrated against the green leaf and total grass yields from a harvest interval x nitrogen application experiment described by Wilman et al. (1976). When the LINGRA-N-Plus model was validated against total grass yields from nitrogen experiments at ten sites described by Morrison et al. (1980), its modelling efficiency improved greatly compared to the original LINGRA-N. High predicted yields, at zero nitrogen application, were related to soils with a high initial nitrogen content. The lowest predicted yields occurred at sites with low rainfall and shallow rooting depth; mitigating the effect of drought at such sites increased yields by up to 4 t ha(-1). The results highlight the usefulness of grass models, such as LINGRA-N-Plus, to explore the combined effects of climate, soil, and management, like nitrogen application, and harvest intervals on grass productivity.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available