Results 131 to 140 of about 108,052 (320)

Editorial

open access: yesGrassland Research, 2022
Zhibiao Nan   +6 more
doaj   +1 more source

Seedling emergence and herbage yield of summer‐active tall fescue sown at different times and sowing depths

open access: yesGrassland Research
Background Tall fescue is sensitive to sowing depth and, in the Pampas region of Argentina, its sowing is often delayed from autumn (average air temperature 18.5°C) to winter (average air temperature 10.0°C).
María José Beribe   +2 more
doaj   +1 more source

THE GRASSLAND MESSAGE

open access: yesProceedings of the New Zealand Grassland Association, 1981
With the Grassland Association now celebrating its 50th anniversary an attempt has been made to outline the main thrust of research and extension work and its effect on farming trends over ten year periods. The important grassland messages are covered and an attempt made to supply the grassland message for the future.
openaire   +2 more sources

Short‐term effects of soil texture, biochar, manure, and tillage practices on warm‐climate forage yields and nutrient content

open access: yesGrassland Research
Background Biochar (BC) amendment to soils can affect crop yields negatively, especially during the first season following application, by binding essential nutrients; however, little data exist on its effects on warm‐climate forage yields and nutritive ...
Cade P. Cooper   +7 more
doaj   +1 more source

A UAV-Enabled Time-Sensitive Data Collection Scheme for Grassland Monitoring Edge Networks [PDF]

open access: yesarXiv
Grassland monitoring is essential for the sustainable development of grassland resources. Traditional Internet of Things (IoT) devices generate critical ecological data, making data loss unacceptable, but the harsh environment complicates data collection.
arxiv  

Cloud gap-filling with deep learning for improved grassland monitoring [PDF]

open access: yesarXiv
Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose a deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic
arxiv  

Home - About - Disclaimer - Privacy