J
John Blackwell
Researcher at Charles Sturt University
Publications - 36
Citations - 1150
John Blackwell is an academic researcher from Charles Sturt University. The author has contributed to research in topics: Seeder & Drainage. The author has an hindex of 13, co-authored 35 publications receiving 967 citations. Previous affiliations of John Blackwell include University of Southern Queensland & Commonwealth Scientific and Industrial Research Organisation.
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Wastewater irrigation and environmental health: Implications for water governance and public policy
TL;DR: There is a need to better integrate water reuse into core water governance frameworks in order to effectively address the challenges and harness the potential of this vital resource for environmental health protection.
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Can Irrigation Be Sustainable
TL;DR: In this paper, the authors compared three semi-arid regions: Rechna Doab (RD), Pakistan; Liuyuankou irrigation system (LIS), China; and Murrumbidgee irrigation area (MIA), Australia.
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The Happy Seeder enables direct drilling of wheat into rice stubble
HS Sidhu,Manpreet-Singh,Elizabeth Humphreys,Elizabeth Humphreys,Yadvinder-Singh,Balwinder-Singh,Balwinder-Singh,S. S. Dhillon,John Blackwell,John Blackwell,Bector,Malkeet-Singh,Sarbjeet-Singh +12 more
TL;DR: The Happy Seeder as discussed by the authors combines the stubble mulching and seed drilling functions in the one machine, which reduces air pollution and loss of nutrients and organic carbon due to burning, at the same time as maintaining or increasing yield.
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Development and evaluation of the Turbo Happy Seeder for sowing wheat into heavy rice residues in NW India
Harminder S. Sidhu,Manpreet Singh,Yadvinder Singh,John Blackwell,Shiv Kumar Lohan,Elizabeth Humphreys,Mangi L. Jat,Vicky Singh,Sarbjeet Singh +8 more
TL;DR: In the extensive rice-wheat system of north-west India, harvesting is by large combines and the rice residues are normally burnt after harvest, followed by irrigation and intensive tillage prior to sowing wheat as discussed by the authors.
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Water quality monitoring using Landsat Themate Mapper data with empirical algorithms in Chagan Lake, China
TL;DR: In this paper, both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals, and it was found that the neural network model performed at better accuracy than empirical regression with TM visible and near-infrared bands as spectral variables.