Wheatland Conservation Area Inc. – project results from the dry Brown Soil Zone

Non-Peer ReviewedThe Wheatland Conservation Area Inc. manages and operates the brown soil zone Agri-ARM program in southwest Saskatchewan. Our non-profit organization conducts producer driven applied research and extension. The majority of the work done is large plot, replicated studies using field scale equipment. Small plot replicated studies are done to a lesser extent, as well as a few non-replicated demonstrations. Results are extended to producers at tours, workshops, and trade shows, as well as by newsletters, fact sheets, and a weekly radio segment called “Walk the Plots”. Partnerships with government and non-government organizations, as well as industry, and producers are a large part of our overall success. Since we are the only site in the dry Brown Soil Zone we run satellite sites throughout the south west in addition to the main site at Swift Current. This is to insure a wider audience and increased adoption rates by producers in the south west. These sites are located near Assiniboia, Frontier, Aneroid, and Success. Small, single study sites are also located in the area. Approximately forty trials are conducted annually involving pulse crops, forages, oilseeds, cereals, cereal recrops, and many others

Urea treatment affects safe rates of seed placed nitrogen in Saskatchewan

Non-Peer ReviewedPlacing urea in close proximity to seed can cause seedling damage resulting in poor crop establishment. Plant densities are often well below the optimum, and plants that do emerge can exhibit poor vigor. Several strategies have been developed to reduce risk of seed damage from urea. Restricting the amount that is seed placed, placing urea at a safe distance and placement before or after seeding are effective but may not allow for application of adequate N or increase equipment and operating costs. Recently treatments applied to the urea granule such as Agrotain and polymer coating have been developed to slow the conversion to ammonium. Research suggests that the safe rate of N can be increased by 50% where Agrotain is used and are less clear when polymer coatings are used. To demonstrate how Agrotain and polymer treated urea affect crop establishment and yield, rates of 0, 1, 1.5, 2 and 4 times the recommended safe rate were seed placed at Scott, Swift Current, Canora and Redvers, Saskatchewan. Trials were conducted with wheat at all locations, and canola at Scott. Seed placed untreated urea was used as a check. As well, an alternate option using seed placed untreated urea followed by liquid urea ammonium nitrate dribble banded 20 to 35 days after seeding was investigated. Impact of treatments on plant density varied with rainfall across locations. Sites with lower precipitation after seeding indicated more severe damage to seedlings. Untreated urea placed with the seed had the greatest impact on plant density but, Agrotain and polymer treatments also led to decreases at high N rates. The improvement of Agrotain over untreated urea generally confirmed manufacturer recommendations that safe rates of seed placed urea can be increased by about 50%. The polymer was very effective at reducing damage from seed placed urea, but still generally resulted in fewer plants than side band at 4 times the recommended rate of N. Grain yield responses were also variable across locations. At most sites where plant stand reductions were high yield was also affected. Differences between all treatments were small at N rates up to 2 times the recommended rate but at 4 times, yield was reduced for Agrotain treated and untreated seed placed N. For treatments where liquid dribble band was compared to side banding little difference in yield was observed when soil residual N was high and precipitation was low. A reduction in yield was found when soil N and precipitation were low. Where the N supply from soil was large and precipitation higher, yield of dribble banded crop continued to respond after side banded crops had peaked

Towards the characterization and validation of alcohol use disorder subtypes: Integrating consumption and symptom data

BACKGROUND: There is evidence that measures of alcohol consumption, dependence and abuse are valid indicators of qualitatively different subtypes of alcohol involvement yet also fall along a continuum. The present study attempts to resolve the extent to which variations in alcohol involvement reflect a difference in kind versus a difference in degree. METHOD: Data were taken from the 2001–2002 National Epidemiologic Survey of Alcohol and Related Conditions. The sample (51% male; 72% white/non-Hispanic) included respondents reporting past 12-month drinking at both waves (wave 1: n=33644; wave 2: n=25186). We compared factor mixture models (FMMs), a hybrid of common factor analysis (FA) and latent class analysis (LCA), against FA and LCA models using past 12-month alcohol use disorder (AUD) criteria and five indicators of alcohol consumption reflecting frequency and heaviness of drinking. RESULTS: Model comparison revealed that the best-fitting model at wave 1 was a one-factor four-class FMM, with classes primarily varying across dependence and consumption indices. The model was replicated using wave 2 data, and validated against AUD and dependence diagnoses. Class stability from waves 1 to 2 was moderate, with greatest agreement for the infrequent drinking class. Within-class associations in the underlying latent factor also revealed modest agreement over time. CONCLUSIONS: There is evidence that alcohol involvement can be considered both categorical and continuous, with responses reduced to four patterns that quantitatively vary along a single dimension. Nosologists may consider hybrid approaches involving groups that vary in pattern of consumption and dependence symptomatology as well as variation of severity within group

Response of cereals to fertilizer N on pulse and other stubbles

Non-Peer ReviewedTo optimize cropping systems requires knowledge of effects of the preceding crop on the grain yield and protein and the response to N of a following cereal crop. To gain this knowledge, we grew hard red spring (HRS) wheat, durum wheat, Canadian Prairie Spring (CPS)-class wheat, Canadian Western Extra Strong (CWES)-class wheat, and barley on barley, bean, coriander, fenugreek, kabuli chickpea, lentil, mustard, and pea stubble at different N fertilizer rates over 9 site-yr: Swift Current (1998-2002), Redvers (2001-02), and Canora (1999 and 2002). N rates were medium (recommended rate based on fall soil nitrate in cereal stubble), low (15-30 kg ha-1 less than medium) and high (15-30 kg ha-1). There was a significant effect of stubble on subsequent cereal grain yield. Cereal on cereal stubble was consistently lowest or second lowest yielding (typically 100 – 800 kg ha-1 lower than other stubbles) with the exception of 2001 at Swift Current when it was the highest yielding. This latter effect was attributed to the superior moisture conserving benefits of cereal stubble during this year with extreme early drought. No single cereal crop was consistently highest or lowest yielding. The trend was for greatest grain protein on pulse stubbles although stubble effects on protein were not as great as on yield owing to confounding yield dilution effects. Within this narrow range of fertilizer N rates, yield or protein response to N was weak. Generally, there were no significant interactions between stubble and cereal crop or stubble and fertilizer indicating the effect of stubble was consistent across cereal type and N rates. The cereal yield and protein response to N on the non-cereal stubbles was not significantly different than that on cereal stubble with the exception that barley protein responded more positively to N on lentil stubble than on cereal stubble. Cereals grown on pulse stubbles tended to have higher yields and protein than on other stubbles. For HRS wheat and durum, the chance of achieving high protein grain was greatest with high fertilizer N on pea stubble (>75% of years). Applying a high fertilizer N rate on cereal stubbles did not markedly increase the chance of attaining high protein wheat or durum. For barley, where low protein is desired for malting, the best chance for low protein barley was on cereal and mustard stubble although barley protein appeared less affected by stubble and fertilizer N than wheat or durum

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio

Improving cluster recovery with feature rescaling factors

The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at rescaling features so that none of them overpowers the others in the objective function of the selected clustering algorithm. In this paper, we argue that the rescaling procedure should not treat all features identically. Instead, it should favour the features that are more meaningful for clustering. With this in mind, we introduce a feature rescaling method that takes into account the within-cluster degree of relevance of each feature. Our comprehensive simulation study, carried out on real and synthetic data, with and without noise features, clearly demonstrates that clustering methods that use the proposed data normalization strategy clearly outperform those that use traditional data normalization

MaxMin Linear Initialization for Fuzzy C-Means

International audienceClustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed Standardized Fuzzy Difference (TSFD)

A Confidence Interval for the Wallace Coefficient of Concordance and Its Application to Microbial Typing Methods

Very diverse research fields frequently deal with the analysis of multiple clustering results, which should imply an objective detection of overlaps and divergences between the formed groupings. The congruence between these multiple results can be quantified by clustering comparison measures such as the Wallace coefficient (W). Since the measured congruence is dependent on the particular sample taken from the population, there is variability in the estimated values relatively to those of the true population. In the present work we propose the use of a confidence interval (CI) to account for this variability when W is used. The CI analytical formula is derived assuming a Gaussian sampling distribution and recurring to the algebraic relationship between W and the Simpson's index of diversity. This relationship also allows the estimation of the expected Wallace value under the assumption of independence of classifications. We evaluated the CI performance using simulated and published microbial typing data sets. The simulations showed that the CI has the desired 95% coverage when the W is greater than 0.5. This behaviour is robust to changes in cluster number, cluster size distributions and sample size. The analysis of the published data sets demonstrated the usefulness of the new CI by objectively validating some of the previous interpretations, while showing that other conclusions lacked statistical support