Results 191 to 200 of about 1,380,345 (372)
The occurrence of wheat downy mildew in the United States
, 1921 William H. Weston
openalex +2 more sources
Hydrolysis of the soluble pentosans of wheat flour and Rhodymenia palmata by ruminal micro-organisms [PDF]
, 1957 B. H. Howard
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Heirloom Spring Wheat Seeding Rate Trial [PDF]
, 2014 University of Vermont Extension began its heirloom spring wheat project in 2007 to determine whether heirloom varieties developed before 1950 could thrive in Vermont’s climate.
Blair, Katie +5 more
core +1 more source
Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature [PDF]
arXivWheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. Newly desirable wheat variety traits include disease resistance to reduce pesticide use, adaptation to climate change, resistance to heat and drought stresses, or low gluten content of
arxiv
The chemical composition of wheat and rye and of flours derived therefrom [PDF]
, 1945 R. A. McCance +4 more
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Nitrogen transfer between clover and wheat in an intercropping experiment [PDF]
, 2006 A novel approach to the problem of improving nitrogen supply in organic farming is to use intercropping of cereals with a legume to provide nitrogen transfer within a season and/or to following crops. The affects of intercropping were studied in a column
Pappa, V A, Rees, R M, Watson, C A
core
Is durum wheat-winter pea intercropping efficient to improve the use of N in low input farming ? [PDF]
, 2007 Nitrogen acquisition and grain protein concentration (GPC) of durum wheat is often a major concern, particularly in low input systems where mineral N is a limited resource.
Bedoussac, Laurent, Justes, Eric
core
Developing an Optimal Model for Predicting the Severity of Wheat Stem Rust (Case study of Arsi and Bale Zone) [PDF]
arXivThis research utilized three types of artificial neural network (ANN) methodologies, namely Backpropagation Neural Network (BPNN) with varied training, transfer, divide, and learning functions; Radial Basis Function Neural Network (RBFNN); and General Regression Neural Network (GRNN), to forecast the severity of stem rust. It considered parameters such
arxiv