Location, Seeding Date, and Variety Interactions on Winter Wheat Yield in Southeastern United States

2013 ◽  
Vol 105 (2) ◽  
pp. 509-518 ◽  
Author(s):  
Mathew Tapley ◽  
Brenda V. Ortiz ◽  
Edzard van Santen ◽  
Kipling S. Balkcom ◽  
Paul Mask ◽  
...  
Keyword(s):  
United States ◽  
Winter Wheat ◽  
Southeastern United States ◽  
Wheat Yield ◽  
Seeding Date ◽  
Winter Wheat Yield

Seeding Date, Seeding Rate, and Row Spacing Affect Wheat (Triticum aestivum) and Cheat (Bromus secalinus)

1991 ◽  
Vol 5 (4) ◽  
pp. 707-712 ◽  
Author(s):  
Jeffrey A. Koscelny ◽  
Thomas F. Peeper ◽  
John B. Solie ◽  
Stanley G. Solomon
Keyword(s):  
Triticum Aestivum ◽  
Winter Wheat ◽  
Field Experiments ◽  
Wheat Yield ◽  
Row Spacing ◽  
Infestation Level ◽  
Seeding Rate ◽  
Seeding Date ◽  
Bromus Secalinus ◽  
Winter Wheat Yield

Field experiments were conducted in Oklahoma to determine the effects of winter wheat seeding date and cheat infestation level on cultural cheat control obtained by increasing winter wheat seeding rates and decreasing row spacing. Seeding rate and row spacing interactions influenced cheat density, biomass, or seed in harvested wheat (dockage) at two of three locations. Suppressive effects on cheat of increasing wheat seeding rates and reduced row spacings were greater in wheat seeded in September than later. At two other locations, increasing seeding rate from 67 to 101 kg ha–1or reducing row spacings from 22.5 to 15 cm increased winter wheat yield over a range of cheat infestation levels.

scholarly journals Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

2020 ◽  
Vol 12 (8) ◽  
pp. 1232 ◽  
Author(s):  
Yumiao Wang ◽  
Zhou Zhang ◽  
Luwei Feng ◽  
Qingyun Du ◽  
Troy Runge
Keyword(s):  
United States ◽  
Machine Learning ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Growing Season ◽  
The United States ◽  
Learning Approaches ◽  
Source Data ◽  
The World ◽  
Winter Wheat Yield

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

The effect of seeding date, seeding rate and N fertilization on winter wheat yield and yield components in eastern Newfoundland

2000 ◽  
Vol 80 (4) ◽  
pp. 703-711 ◽  
Author(s):  
D. Spaner ◽  
A. G. Todd ◽  
D. B. McKenzie
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Yield Components ◽  
Wheat Yield ◽  
Triticum Aestivum L ◽  
N Fertilization ◽  
Seeding Rate ◽  
Seeding Date ◽  
Yield And Yield Components ◽  
Winter Wheat Yield

Livestock farmers in Newfoundland presently import most of their feed grain, and local self-sufficiency in grain production is a desirable long-term goal. The overall objective of this work was to refine our understanding of winter wheat (Triticum aestivum L.) production in Newfoundland, with the aim of improving present cropping recommendations. We conducted trials near St. John's in 1998 and 1999 to examine the effect of seeding rate and topdress ammonium nitrate (N) fertilization rate on Borden winter wheat yield and yield components. We also conducted four seeding date trials in the same region. Optimum-treatment grain yields in our six trials ranged from 2.76 to 5.39 t ha−1. In years of variable winter kill, increasing seeding rate up to 450 seeds m−2 increased spikes m−2 at harvest, resulting in increased grain yield. Seeding rate, however, was not as important as N fertilization in maximizing grain yield. Increasing topdress fertilization to 60 kg N ha–1 increased spikes m–2 at harvest in years of variable winter kill, resulting in greater grain yield. In years of high winter survival, the main source of higher grain yield levels (through higher N application rates) was not achieved through greater spikes m−2 at harvest, but rather through an increase in kernel weight. Optimum grain yields occurred at seeding rates of 400 ± 50 seeds m−2, and at topdress fertilizer applications up to a rate of at least 30 kg N ha−1. Given the results of our seeding date experiments, in conjunction with previously developed climatic models, we now consider the optimum seeding date for the eastern region of Newfoundland to be August 31. Key words: Yield component analysis, two-dimensional partitioning, Triticum aestivum L., ammonium nitrate

Effect of nitrogen top-dressing on winter wheat yield, quantity and quality

2005 ◽  
Vol 34 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Zs. Szentpétery ◽  
Cs. Kleinheincz ◽  
G. Szöllősi ◽  
M. Jolánkai
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Winter Wheat Yield
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