Developing an Empirical Yield-Prediction Model Based on Wheat and Wild Oat (Avena fatua) Density, Nitrogen and Herbicide Rate, and Growing-Season Precipitation

Weed Science ◽  
2007 ◽  
Vol 55 (6) ◽  
pp. 652-664 ◽  
Author(s):  
N. C. Wagner ◽  
B. D. Maxwell ◽  
M. L. Taper ◽  
L. J. Rew
Keyword(s):  
Field Data ◽  
Wheat Yield ◽  
Growing Season ◽  
Predictor Variables ◽  
Data Sets ◽  
Wild Oat ◽  
Best Fitting ◽  
Weed Infestation ◽  
Growing Season Precipitation ◽  
Better Than

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R2= 0.71), a double-hyperbolic equation including three input predictor variables (R2= 0.63), and a nonlinear model including all five predictor variables (R2= 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our five-variable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with anR2of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation.

Smallseed Falseflax (Camelina microcarpa) Management in Oklahoma Winter Wheat

2021 ◽  
pp. 1-14
Author(s):  
Jodie A. Crose ◽  
Misha R. Manuchehri ◽  
Todd A. Baughman
Keyword(s):  
Winter Wheat ◽  
Weed Control ◽  
Herbicide Resistance ◽  
Wheat Yield ◽  
Acetolactate Synthase ◽  
Growing Season ◽  
Time Of Application ◽  
Adequate Control ◽  
Growing Seasons

Abstract Three herbicide premixes have recently been introduced for weed control in wheat. These include: halauxifen + florasulam, thifensulfuron + fluroxypyr, and bromoxynil + bicyclopyrone. The objective of this study was to evaluate these herbicides along with older products for their control of smallseed falseflax in winter wheat in Oklahoma. Studies took place during the 2017, 2018, and 2020 winter wheat growing seasons. Weed control was visually estimated every two weeks throughout the growing season and wheat yield was collected in all three years. Smallseed falseflax size was approximately six cm in diameter at time of application in all years. Control ranged from 96 to 99% following all treatments with the exception of bicyclopyrone + bromoxynil and dicamba alone, which controlled falseflax 90%. All treatments containing an acetolactate synthase (ALS)-inhibiting herbicide achieved adequate control; therefore, resistance is not suspected in this population. Halauxifen + florasulam and thifensulfuron + fluroxypyr effectively controlled smallseed falseflax similarly to other standards recommended for broadleaf weed control in wheat in Oklahoma. Rotational use of these products allows producers flexibility in controlling smallseed falseflax and reduces the potential for development of herbicide resistance in this species.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Pattern analysis of growing season precipitation in Southern Canada

1987 ◽  
Vol 25 (2) ◽  
pp. 137-158 ◽  
Author(s):  
Michael B. Richman ◽  
Peter J. Lamb
Keyword(s):  
Pattern Analysis ◽  
Growing Season ◽  
Growing Season Precipitation

Applications of the herbicide sethoxydim increase wheat yield in the absence of weeds

1996 ◽  
Vol 36 (5) ◽  
pp. 555
Author(s):  
ID Black ◽  
CB Dyson ◽  
AR Fischle
Keyword(s):  
Leaf Growth ◽  
Wheat Yield ◽  
South Australia ◽  
Growing Season ◽  
Degree Days ◽  
Experimental Site ◽  
Yield Increase ◽  
Growing Seasons ◽  
Positive Linear Correlation ◽  
Area North

In 11 experiments over 6 seasons the herbicide sethoxydim was applied to Machete, Spear and Blade wheat cultivars in the absence or near absence of weeds (10 sites) or where the weeds were controlled by selective herbicides (1 site), in the cropping area north of Adelaide, South Australia. The rates applied included 9-47 g a.i./ha at the 2-3 leaf growth stage and 9-74 g a.i./ha at early tillering. Except for the very long growing season of 1992, there was a highly significant positive linear correlation between the number of degree days in the growing season at each experimental site and relative mean yield increase of these sethoxydim treatments. Yield increases ranged from nil in growing seasons of about 1000 degree days to 32% in a growing season of 1480 degree days, with a median of 8% over the experiments.

Surface wave attenuation of seismic records with the co-core trace transform filter

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V115-V128 ◽  
Author(s):  
Ning Wu ◽  
Yue Li ◽  
Baojun Yang
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Field Data ◽  
Wave Attenuation ◽  
Seismic Events ◽  
Data Sets ◽  
Transform Domain ◽  
Recent Developments ◽  
Seismic Records ◽  
Trace Transform

To remove surface waves from seismic records while preserving other seismic events of interest, we introduced a transform and a filter based on recent developments in image processing. The transform can be seen as a weighted Radon transform, in particular along linear trajectories. The weights in the transform are data dependent and designed to introduce large amplitude differences between surface waves and other events such that surface waves could be separated by a simple amplitude threshold. This is a key property of the filter and distinguishes this approach from others, such as conventional ones that use information on moveout ranges to apply a mask in the transform domain. Initial experiments with synthetic records and field data have demonstrated that, with the appropriate parameters, the proposed trace transform filter performs better both in terms of surface wave attenuation and reflected signal preservation than the conventional methods. Further experiments on larger data sets are needed to fully assess the method.

Nitrogen form, time and rate of application, and nitrification inhibitor effects on crop production

2014 ◽  
Vol 94 (2) ◽  
pp. 425-432 ◽  
Author(s):  
R. E. Karamanos ◽  
K. Hanson ◽  
F. C. Stevenson
Keyword(s):  
Crop Production ◽  
Protein Concentration ◽  
Wheat Yield ◽  
Nitrification Inhibitor ◽  
N Fertilizer ◽  
Nitrogen Form ◽  
Anhydrous Ammonia ◽  
N Concentration ◽  
Fertilizer Rates ◽  
Better Than

Karamanos, R., Hanson, K. and Stevenson, F. C. 2014. Nitrogen form, time and rate of application, and nitrification inhibitor effects on crop production. Can. J. Plant Sci. 94: 425–432. Nitrogen management options for anhydrous ammonia (NH3) and urea were compared in a barley–wheat–canola–wheat cropping sequence (2007–2010) at Watrous and Lake Lenore, SK. The treatment design included a factorial arrangement of N fertilizer form (NH3versus urea), nitrification inhibitor application, time of N application (mid-September, mid- to late October, and spring) and four N fertilizer rates (0, 40, 80 and 120 kg ha−1). Anhydrous ammonia applications at 40 kg N ha−1in 2008 (fall) and in 2010 (all times of application) resulted in wheat yield reductions relative to the same applications for urea. For wheat years, yield was reduced for both fall versus spring N fertilizer applications, when no nitrification inhibitor was applied and the inclusion of nitrification inhibitor maintained wheat yield at similar levels across all times of N fertilizer applications, regardless of form. Protein concentration was approximately 2 g kg−1greater with urea compared with NH3at both sites in 2008 and only at Watrous in 2010. Also, early versus late fall N fertilizer applications consistently increased N concentration of grain only for the 40 and/or 80 kg N ha−1rates. Effects of nitrification inhibitor on N concentration were not frequent and appeared to be minimal. Urea had greater agronomic efficiency (AE) than NH3at the lower N fertilizer rates. The nitrification inhibitor had a positive effect on wheat AE only for early fall N fertilizer applications. It can be concluded that for maximum yields NH3or urea will be suitable if applied at rates of 80 kg N ha−1and greater. If N fertilizer is applied at 40 kg N ha−1, especially in fall without inhibitor, urea is better. In terms of protein concentration for wheat, urea seemed to better than NH3and fall was better than spring application.

scholarly journals Identifying maize yield and precipitation gaps in Uganda

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Terence Epule Epule ◽  
Driss Dhiba ◽  
Daniel Etongo ◽  
Changhui Peng ◽  
Laurent Lepage
Keyword(s):  
Cropping Systems ◽  
Growing Season ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
World Bank Group ◽  
Precipitation Data ◽  
Yield Data ◽  
Yield Gaps ◽  
Maize Yields ◽  
Growing Season Precipitation

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

scholarly journals The Thermal Performance of Traditional Residential Buildings in Kathmandu Valley

2014 ◽  
Vol 10 (1) ◽  
pp. 172-183 ◽  
Author(s):  
Sushil B. Bajracharya
Keyword(s):  
Thermal Performance ◽  
Field Data ◽  
Thermal Environment ◽  
Residential Buildings ◽  
Residential Building ◽  
Kathmandu Valley ◽  
Indoor Thermal Environment ◽  
Outdoor Thermal Environment ◽  
Different Seasons ◽  
Better Than

This paper seeks to investigate into the aspects of thermal performance of traditional residential buildings in traditional settlements of Kathmandu valley. This study proceeds to analyze the detailed field data collected, with a view to identify the indoor thermal environment with respect to outdoor thermal environment in different seasons. This paper also compares the thermal performance of traditional buildings with modern residential buildings of traditional settlements of the valley. There is a regression analysis to obtain information about the thermal environment of different traditional and modern residential buildings with different conditions. The paper concludes that, thermal performance of traditional residential building, adapted in various ways to the changing thermal regime for thermal comfort is better than that of contemporary buildings.DOI: http://dx.doi.org/10.3126/jie.v10i1.10898Journal of the Institute of Engineering, Vol. 10, No. 1, 2014,  pp. 172–183

Quantifying paleogeography using biogeography: a test case for the Ordovician and Silurian of Avalonia based on brachiopods and trilobites

Paleobiology ◽  
2002 ◽  
Vol 28 (3) ◽  
pp. 343-363 ◽  
Author(s):  
David C. Lees ◽  
Richard A. Fortey ◽  
L. Robin M. Cocks
Keyword(s):  
Goodness Of Fit ◽  
Total Evidence ◽  
Pairwise Distance ◽  
Test Case ◽  
Data Sets ◽  
Plate Tectonic ◽  
Plate Model ◽  
Optimal Arrangement ◽  
Evidence Analysis ◽  
Better Than

Despite substantial advances in plate tectonic modeling in the last three decades, the postulated position of terranes in the Paleozoic has seldom been validated by faunal data. Fewer studies still have attempted a quantitative approach to distance based on explicit data sets. As a test case, we examine the position of Avalonia in the Ordovician (Arenig, Llanvirn, early Caradoc, and Ashgill) to mid-Silurian (Wenlock) with respect to Laurentia, Baltica, and West Gondwana. Using synoptic lists of 623 trilobite genera and 622 brachiopod genera for these four plates, summarized as Venn diagrams, we have devised proportional indices of mean endemism (ME, normalized by individual plate faunas to eliminate area biogeographic effects) and complementarity (C) for objective paleobiogeographic comparisons. These can discriminate the relative position of Avalonia by assessing the optimal arrangement of inter-centroid distances (measured as great circles) between relevant pairs of continental masses. The proportional indices are used to estimate the “goodness-of-fit” of the faunal data to two widely used dynamic plate tectonic models for these time slices, those of Smith and Rush (1998) and Ross and Scotese (1997). Our faunal data are more consistent with the latter model, which we use to suggest relationships between faunal indices for the five time slices and new rescaled inter-centroid distances between all six plate pairs. We have examined linear and exponential models in relation to continental separation for these indices. For our generic data, the linear model fits distinctly better overall. The fits of indices generated by using independent trilobite and brachiopod lists are mostly similar to each other at each time slice and for a given plate, reflecting a common biogeographic signal; however, the indices vary across the time slices. Combining groups into the same matrix in a “total evidence” analysis performs better still as a measure of distance for mean endemism in the “Scotese” plate model. Four-plate mean endemism performs much better than complementarity as an indicator of pairwise distance for either plate model in the test case.

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