Spatial regression methods to evaluate beekeeping production in the state of Rio de Janeiro

Brazilian beekeeping has been developed from the africanization of the honeybees and its high performance launches Brazil as one of the world´s largest honey producer. The Southeastern region has an expressive position in this market (45%), but the state of Rio de Janeiro is the smallest producer, despite presenting large areas of wild vegetation for honey production. In order to analyze the honey productivity in the state of Rio de Janeiro, this research used classic and spatial regression approaches. The data used in this study comprised the responses regarding beekeeping from 1418 beekeepers distributed throughout 72 counties of this state. The best statistical fit was a semiparametric spatial model. The proposed model could be used to estimate the annual honey yield per hive in regions and to detect production factors more related to beekeeping. Honey productivity was associated with the number of hives, wild swarm collection and losses in the apiaries. This paper highlights that the beekeeping sector needs support and help to elucidate the problems plaguing beekeepers, and the inclusion of spatial effects in the regression models is a useful tool in geographical data.

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Using Spatial Regression Methods to Examine the Association Between County-Level Racial/Ethnic Composition and Reported Cases of Chlamydia and Gonorrhea: An Illustration With Data From the State of Texas

Sexually Transmitted Diseases ◽

10.1097/olq.0b013e3181b6ac93 ◽

2009 ◽

Vol 36 (10) ◽

pp. 657-664 ◽

Cited By ~ 18

Author(s):

Kwame Owusu-Edusei ◽

Harrell W. Chesson

Keyword(s):

Spatial Regression ◽

The State ◽

Ethnic Composition ◽

County Level ◽

Regression Methods ◽

Racial Ethnic

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On the diversity of the effects of the state support for agriculture

Voprosy Ekonomiki ◽

10.32609/0042-8736-2019-4-59-73 ◽

2019 ◽

pp. 59-73 ◽

Cited By ~ 3

Author(s):

Nikolai M. Svetlov ◽

Renata G. Yanbykh ◽

Dariya A. Loginova

Keyword(s):

Agricultural Production ◽

The State ◽

Russian Regions ◽

Negative Effects ◽

State Support ◽

Soft Budget Constraints ◽

Institutional Conditions ◽

Positive Effects ◽

Production Factors ◽

Corporate Farms

In this paper, we assess the effects of agricultural state support of corporate farms on their revenues from agricultural production sales in 14 Russian regions that differ in technology, environment and institutional conditions. In addition to the direct effect of the state support, the indirect effects via labor and capital are revealed. For this purpose, we identify production functions and statistical models of production factors for each of these regions separately. We find out diverse effects of the state support on revenues among the regions. Positive effects prevail. Negative effects are mainly caused by labor reductions that follow subsidy inflows. Another cause of negative effects is the soft budget constraints phenomenon.

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Integração local da população refugiada no estado do Rio de Janeiro:

Revista Prumo ◽

10.24168/revistaprumo.v4i6.1179 ◽

2019 ◽

Vol 4 (6) ◽

pp. 42-53

Author(s):

Ariane Rego de Paiva ◽

Roberta Gomes Thomé

Keyword(s):

Rio De Janeiro ◽

Social Protection ◽

The State ◽

Refugee Population ◽

Local Integration

This work presents a problematization about the local integration of refugees as one of the lasting solutions proposed by the UNHCR and the present challenges for the consolidation of policies of social protection for this segment in the state of Rio de Janeiro. This study was made through the bibliographic analisis and systematization of a workshop about the thematics carried with governamental and non-governamental agents and leaderships in the refugee population in the occasion of the seminar Mobility Crossroads: knowledge and practices in protection policies for refugee populations and migrants in vulnerable situation, which occurred in November 2017, organized by Cátedra Sérgio Vieira de Mello of PUC-Rio.

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About Measures of the State Support for Technical Equipment of Agricultural Producers

Economy of agricultural and processing enterprises ◽

10.31442/0235-2494-2020-0-9-20-27 ◽

2020 ◽

pp. 20-27

Author(s):

A.Ya. Kibirov ◽

Keyword(s):

Statistical Analysis ◽

Energy Saving ◽

High Performance ◽

The State ◽

Agricultural Products ◽

Technical Equipment ◽

Agricultural Machinery ◽

State Support ◽

Agricultural Producers ◽

Technical Base

The article uses methods of statistical analysis, deduction and analogy to consider programs at the Federal, regional and economic levels, which provide for measures aimed at improving the technical equipment of agricultural producers. Particular attention is paid to the acquisition of energy-saving, high-performance agricultural machinery and equipment used in the production and processing of agricultural products. An assessment of the effectiveness of state support for updating the material and technical base of agriculture is given. Based on the results of the study, conclusions and recommendations were formulated.

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Locaaao De Bens E Serviios De Informmtica No Estado Do Rio De Janeiro (Rental of Informatics Goods and Services in the State of Rio De Janeiro)

SSRN Electronic Journal ◽

10.2139/ssrn.2618457 ◽

2014 ◽

Author(s):

Andrr Saddy

Keyword(s):

Rio De Janeiro ◽

The State ◽

Goods And Services

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Spatiotemporal clustering, social inequities and the risk of leptospirosis in an endemic area of Brazil: a retrospective spatial modelling

Transactions of the Royal Society of Tropical Medicine and Hygiene ◽

10.1093/trstmh/traa128 ◽

2020 ◽

Author(s):

Katyucia O C de Souza ◽

José Augusto P Góes ◽

Matheus S Melo ◽

Paula M G Leite ◽

Lucas A Andrade ◽

...

Keyword(s):

Urban Areas ◽

Spatial Regression ◽

Temporal Trends ◽

Spatial Dynamics ◽

Secondary Data ◽

The State ◽

Scan Statistics ◽

Northeastern Brazil ◽

Metropolitan Region ◽

Social Inequities

Abstract Background Leptospirosis is an endemic disease in Brazil of social and economic relevance related to behavioural and socioenvironmental factors. This study aimed to analyse the spatiotemporal distribution of the incidence of leptospirosis and its association with social determinants in health in a state of northeastern Brazil. Methods An ecological study of temporal series with techniques of spatial analysis using secondary data of the cases of leptospirosis notified in the Information System of Notifiable Diseases of the state of Sergipe (2008–2017) was conducted. The analysis of temporal trends was performed using Poisson regression. Spatial analyses were performed using the Moran index, the local empirical Bayesian model, scan statistics and spatial regression. Results The incidence rate decreased from 3.66 to 1.44 cases per 100 000 inhabitants in 2008 and 2017, respectively. Leptospirosis was associated with social inequities, mostly affecting males aged 20–49 y living in urban areas. The space-time scan indicated the formation of a risk cluster in municipalities in the metropolitan region of the state. Conclusions The data indicated the persistence of leptospirosis transmission, maintaining a pattern of high endemicity in some municipalities associated with social inequities. The study showed the temporal and spatial dynamics of the disease to better target specific actions for prevention and control.

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Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽

10.3390/agriculture11070651 ◽

2021 ◽

Vol 11 (7) ◽

pp. 651

Author(s):

Shengyi Zhao ◽

Yun Peng ◽

Jizhan Liu ◽

Shuo Wu

Keyword(s):

Neural Network ◽

High Performance ◽

Model Comparison ◽

Research Direction ◽

Disease Diagnosis ◽

Tomato Leaf ◽

Identification Accuracy ◽

Main Research ◽

Proposed Model ◽

Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

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A New Approach to Inform Restoration and Management Decisions for Sustainable Apiculture

Sustainability ◽

10.3390/su13116109 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6109

Author(s):

Joanne Lee Picknoll ◽

Pieter Poot ◽

Michael Renton

Keyword(s):

Floral Resources ◽

Pollination Services ◽

New Approach ◽

Honey Production ◽

Proposed Model ◽

Colony Loss ◽

Supply Potential ◽

Australian Case ◽

Stocking Rates

Habitat loss has reduced the available resources for apiarists and is a key driver of poor colony health, colony loss, and reduced honey yields. The biggest challenge for apiarists in the future will be meeting increasing demands for pollination services, honey, and other bee products with limited resources. Targeted landscape restoration focusing on high-value or high-yielding forage could ensure adequate floral resources are available to sustain the growing industry. Tools are currently needed to evaluate the likely productivity of potential sites for restoration and inform decisions about plant selections and arrangements and hive stocking rates, movements, and placements. We propose a new approach for designing sites for apiculture, centred on a model of honey production that predicts how changes to plant and hive decisions affect the resource supply, potential for bees to collect resources, consumption of resources by the colonies, and subsequently, amount of honey that may be produced. The proposed model is discussed with reference to existing models, and data input requirements are discussed with reference to an Australian case study area. We conclude that no existing model exactly meets the requirements of our proposed approach, but components of several existing models could be combined to achieve these needs.

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