scispace - formally typeset
D

D. R. Kindred

Researcher at ADAS

Publications -  42
Citations -  1591

D. R. Kindred is an academic researcher from ADAS. The author has contributed to research in topics: Agriculture & Crop yield. The author has an hindex of 18, co-authored 39 publications receiving 1250 citations. Previous affiliations of D. R. Kindred include University of Reading.

Papers
More filters
Journal ArticleDOI

Analysing nitrogen responses of cereals to prioritize routes to the improvement of nitrogen use efficiency

TL;DR: In order to elicit faster improvement in NUE on farms, breeding and variety testing should be conducted at some sites with more than one level of applied N, and that grain N%, N harvest index, and perhaps canopy N ratio should be measured more widely.
Journal ArticleDOI

The potential for land sparing to offset greenhouse gas emissions from agriculture

TL;DR: In this paper, the authors assess the technical mitigation potential offered by land sparing, increasing agricultural yields, reducing farmland area and actively restoring natural habitats on the land spared, and find that a land-sparing strategy has the technical potential to achieve significant reductions in net emissions from agriculture and land-use change.
Journal ArticleDOI

Effects of variety and fertiliser nitrogen on alcohol yield, grain yield, starch and protein content, and protein composition of winter wheat

TL;DR: The effects of nitrogen (N) fertiliser on grain size and shape, starch and protein concentration, vitreosity, storage protein composition, and alcohol yield of two winter wheat varieties contrasting in endosperm texture were studied in a field trial in Herefordshire, UK in 2004 as mentioned in this paper.
Journal ArticleDOI

Cereal yield gaps across Europe

TL;DR: In this paper, the authors used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions.
Journal ArticleDOI

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

TL;DR: The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone.