Multiple statistical approaches of community fingerprint data reveal bacterial populations associated with general disease suppression arising from the application of different organic field management strategies

2007 ◽  
Vol 39 (9) ◽  
pp. 2289-2301 ◽  
Author(s):  
María-Soledad Benítez ◽  
Fulya Baysal Tustas ◽  
Dorith Rotenberg ◽  
Mathew D. Kleinhenz ◽  
John Cardina ◽  
...  
Keyword(s):  
Management Strategies ◽  
Disease Suppression ◽  
Bacterial Populations ◽  
Field Management ◽  
Community Fingerprint ◽  
Statistical Approaches ◽  
Fingerprint Data

scholarly journals Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

2021 ◽  
Vol 10 (5) ◽  
pp. 309
Author(s):  
Zixu Wang ◽  
Chenwei Nie ◽  
Hongwu Wang ◽  
Yong Ao ◽  
Xiuliang Jin ◽  
...  
Keyword(s):  
Crop Production ◽  
Accurate Determination ◽  
Management Strategies ◽  
Canopy Structure ◽  
Object Oriented ◽  
Texture Features ◽  
Sowing Date ◽  
Kappa Coefficient ◽  
Surface Model ◽  
Field Management

Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production.

Comparison of Conventional and Alternative Nursery Weed Management Strategies

1995 ◽  
Vol 9 (4) ◽  
pp. 761-767 ◽  
Author(s):  
James B. Calkins ◽  
Bert T. Swanson
Keyword(s):  
Weed Management ◽  
Cover Crop ◽  
Production Systems ◽  
Management Strategies ◽  
Life Cycles ◽  
Weed Suppression ◽  
Winter Rye ◽  
Field Management ◽  
Companion Crop ◽  
Herbicide Management

Soil cultivation (3 to 5 times/yr) and herbicide management (oxadiazon, 3.92 kg ai/ha), agricultural standards for reducing weed competition, were compared to three alternative nursery field management systems regarding weed suppression: ‘Norcen’ bird's-foot trefoil companion crop, ‘Wheeler’ winter rye cover crop/mulch, and grass sod (80% ‘Eton’ perennial ryegrass and 20% ‘Ruby’ red fescue). Field management treatment had a significant effect on observed weed populations. Weed densities were also subject to yearly variations caused by climate and endogenous weed life cycles. Herbicide management (oxadiazon) consistently provided the best control of undesired vegetation (0.3 weeds/m2) followed by the grass sod (0.7 weeds/m2), Wheeler rye cover crop/mulch (1.7 weeds/m2), Norcen bird's-foot trefoil companion crop (8.6 weeds/m2), and cultivated (55.7 weeds/m2) treatments, respectively. Although the grass sod treatment provided excellent control of undesired vegetation, as an alternative to cultivation and herbicide use, it proved to be excessively competitive with the nursery crop. The bird's-foot trefoil treatment quickly became infested with broadleaf weeds the eradication of which proved difficult. The Wheeler winter rye cover crop/mulch field management system provided acceptable weed control combined with other beneficial effects on the plant/soil environment. Results support the effectiveness of Wheeler winter rye and perhaps other allelopathic cover crop/mulch systems in controlling undesired vegetation in horticultural field production systems.

Effects of Diseases on Soybean Yields in the United States 1996 to 2007

2009 ◽  
Vol 10 (1) ◽  
pp. 24 ◽  
Author(s):  
Allen Wrather ◽  
Steve Koenning
Keyword(s):  
Soybean Cyst Nematode ◽  
Management Strategies ◽  
The United States ◽  
Cyst Nematode ◽  
Disease Suppression ◽  
Stem Rot ◽  
Soybean Yield ◽  
Funding Agencies ◽  
The Usa ◽  
Soybean Diseases

Research must focus on management of diseases that cause extensive losses, especially when funds for research are limited. Knowledge of yield suppression caused by various soybean diseases is essential when prioritizing research. The objective of this project was to compile estimates of soybean yield suppression due to diseases in the USA from 1996 to 2007. The goal was to provide information to help funding agencies and scientists prioritize research objectives and budgets. Yield suppression due to individual diseases varied among years. Soybean cyst nematode suppressed USA soybean yield more from 1996 to 2007 than any other disease. Phytophthora root and stem rot ranked second among diseases that most suppressed yield seven of 12 years. Seedling diseases and charcoal rot also suppressed soybean yield during these years. Research and extension efforts must be expanded to provide more preventive and therapeutic disease management strategies for producers to reduce disease suppression of soybean yield. Accepted for publication 25 February 2009. Published 1 April 2009.

scholarly journals Prospects for Biological Soilborne Disease Control: Application of Indigenous Versus Synthetic Microbiomes

2017 ◽  
Vol 107 (3) ◽  
pp. 256-263 ◽  
Author(s):  
Mark Mazzola ◽  
Shiri Freilich
Keyword(s):  
Disease Control ◽  
Crop Production ◽  
Production Systems ◽  
Agricultural Soils ◽  
Management Strategies ◽  
Plant Diseases ◽  
Disease Suppression ◽  
Rapid Screening ◽  
Soilborne Disease ◽  
Functional Attributes

Biological disease control of soilborne plant diseases has traditionally employed the biopesticide approach whereby single strains or strain mixtures are introduced into production systems through inundative/inoculative release. The approach has significant barriers that have long been recognized, including a generally limited spectrum of target pathogens for any given biocontrol agent and inadequate colonization of the host rhizosphere, which can plague progress in the utilization of this resource in commercial field-based crop production systems. Thus, although potential exists, this model has continued to lag in its application. New omics’ tools have enabled more rapid screening of microbial populations allowing for the identification of strains with multiple functional attributes that may contribute to pathogen suppression. Similarly, these technologies also enable the characterization of consortia in natural systems which provide the framework for construction of synthetic microbiomes for disease control. Harnessing the potential of the microbiome indigenous to agricultural soils for disease suppression through application of specific management strategies has long been a goal of plant pathologists. Although this tactic also possesses limitation, our enhanced understanding of functional attributes of suppressive soil systems through application of community and metagenomic analysis methods provide opportunity to devise effective resource management schemes. As these microbial communities in large part are fostered by the resources endemic to soil and the rhizosphere, substrate mediated recruitment of disease-suppressive microbiomes constitutes a practical means to foster their establishment in crop production systems.

scholarly journals Improving Management Strategies of Plant Diseases Using Sequential Sensitivity Analyses

2019 ◽  
Vol 109 (7) ◽  
pp. 1184-1197 ◽  
Author(s):  
Loup Rimbaud ◽  
Sylvie Dallot ◽  
Claude Bruchou ◽  
Sophie Thoyer ◽  
Emmanuel Jacquot ◽  
...  
Keyword(s):  
Net Present Value ◽  
Management Strategies ◽  
Optimal Solution ◽  
Plant Diseases ◽  
Control Measures ◽  
Disease Suppression ◽  
Simultaneous Optimization ◽  
Disease Spread ◽  
Sensitivity Analyses ◽  
Plum Pox Virus

Improvement of management strategies of epidemics is often hampered by constraints on experiments at large spatiotemporal scales. A promising approach consists of modeling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimization of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach (i.e., a practical improvement method approximating an optimal solution) based on sequential sensitivity analyses. In addition, we use an economic improvement criterion based on the net present value, accounting for both the cost of the different control measures and the benefit generated by disease suppression. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach, and plum), the management of which in orchards is mainly based on surveillance and tree removal. We identified the key parameters of a spatiotemporal model simulating sharka spread and control and approximated optimal values for these parameters. The results indicate that the current French management of sharka efficiently controls the disease, but it can be economically improved using alternative strategies that are identified and discussed. The general approach should help policy makers to design sustainable and cost-effective strategies for disease management.

scholarly journals Assessment of Soil Quality under Different Soil Management Strategies: Combined Use of Statistical Approaches to Select the Most Informative Soil Physico-Chemical Indicators

2021 ◽  
Vol 11 (11) ◽  
pp. 5099
Author(s):  
Anna Maria Stellacci ◽  
Mirko Castellini ◽  
Mariangela Diacono ◽  
Roberta Rossi ◽  
Concetta Eliana Gattullo
Keyword(s):  
Soil Quality ◽  
Management Practices ◽  
Selection Process ◽  
Management Strategies ◽  
Soil Layer ◽  
Stepwise Discriminant Analysis ◽  
No Tillage ◽  
Combined Use ◽  
Statistical Approaches

Assessment of soil quality under different management practices is crucial for sustainable agricultural production and natural resource use. In this study, different statistical methods (principal component analysis, PCA; stepwise discriminant analysis, SDA; partial least squares regression with VIP statistics, PLSR) were applied to identify the variables that most discriminated soil status under minimum tillage and no-tillage. Data collected in 2015 from a long-term field experiment on durum wheat (Triticum durum Desf.) were used and twenty soil indicators (chemical, physical and biological) were quantified for the upper soil layer (0–0.20 m). The long-term iteration of different management strategies affected soil quality, showing greater bulk density, relative field capacity (RFC), organic and extractable carbon contents (TOC and TEC) and exchangeable potassium under no-tillage. PCA and SDA confirmed these results and underlined also the role of available phosphorous and organic carbon fractions as variables that most discriminated the treatments investigated. PLSR, including information on plant response (grain yield and protein content), selected, as the most important variables, plant nutrients, soil physical quality indicators, pH and exchangeable cations. The research showed the effectiveness of combining variable selection methods to summarize information deriving from multivariate datasets and improving the understanding of the system investigated. The statistical approaches compared provided different results in terms of variables selected and the ranking of the selected variables. The combined use of the three methods allowed the selection of a smaller number of variables (TOC, TEC, Olsen P, water extractable nitrogen, RFC, macroporosity, air capacity), which were able to provide a clear discrimination between the treatments compared, as shown by the PCA carried out on the reduced dataset. The presence of a response variable in PLSR considerably drove the feature selection process.

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