Mato Grosso - 2016 (Vinyl)
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From this, the secondary sector is the main one when it comes to effects on employment and, also, of the impacts on income. The tertiary sector is the second most important both for the generation of employment and income and the primary sector is in the third position in relation to the capacity to affect employment and income.
Table 2. Multiplier of employment and income, by sector, in Mato Grosso do Sul, in Table 3, from equations 4, 5 and 6, shows that the main activity generating employment is the activity of generation of chemicals, with a technical coefficient of 35 jobs in total. The second place is occupied by the sector of miscellaneous services and the textile industry.
In those last two, it is noteworthy that they are intensive activity in the labor force and, therefore, stand at the end of the generation of employment. Traditional activity of the economy of Mato Grosso do Sul state, agriculture is only the sixth activity in the item total effect of employment generation, possessing the capacity of 22 jobs.
The sector of public administration is in seventh place with medium power to effect on the generation of jobs for Mato Grosso do Sul, as well as the pulp and paper products sector, this being one of the most recent inductors of the state economy, which is in the twelfth place.
Table 3. Direct employment multipliers, indirect and total for MS, in The sectors of metal products excluding machinery and equipmentManufacture of steel and derivatives and the pulp and paper industry are the sectors that have less capacity to generate employment, considering the base year of MIP.
Figure 1 shows the direct and indirect multipliers for employment in the state of MS, by sector, based on the year of Figure 1. Direct and indirect multipliers for employment, in MS, by sector, in According to Figure 1, the chemicals industry has greater capacity for generation of direct jobs than indirect jobs in the productive chain, followed by the services sector, textiles, non-metallic minerals and agriculture.
Whereas the other sectors have greater indirect capacity to impact the level of employment. Considering the emission estimates of CO 2 to the main activities, it is possible to interpret that, in the case of employment, the activities of greater power to effect of multiplication are not considered activities of major environmental externalities. Based on equations 8, 9 and 10, in relation to the effect on income, public administration consolidates itself as the main activity Table 4.
For the state of MS this is an important sector, being one pays the best pay on average of activities. In the second place, the sector of agriculture presents itself as one of the main sectors, followed by the services sector. Another important consideration in the state sector, mainly from the perspective of export, mineral extraction, is in sixth place in relation to the capacity of the effects of income.
Still, in the case of the effect of income, the textile industry and the chemical production sector does not have a role of major significance as it is the case of employment. Table 4. Direct Income multipliers, indirect and total for MS, in The sectors with the lowest capacity to generate income, in this case derive from the secondary sector industrywhich are chemical products, Manufacture of steel and derivatives and articles made of rubber and plastic.
For the purpose of income, the main activities with the power of the multiplier effect are the main emitting sources of CO 2 from the estimates of SEEG, generating this way, greater externalities.
Figure 2. Direct and indirect multipliers of income, in MS, by sector, in Figure 2 shows that the public administration has a greater capacity for direct impact on income and, secondly, the agriculture, followed by miscellaneous services.
At this point, the other sectors have a median ability to impact on income, both directly and indirectly. From the multipliers presented in this study it was possible to estimate for the sectors of the productive chain of MS the number of employees and income by component of final demand. The results indicate there is a reversal between the multipliers of employment and income, because the areas of greatest prominence in the generation of employment do not stand in the increase of income and vice versa.
In the case of the multiplier effect of employment, sectors popularly known in the state economy showed weak dynamism in relation to generating employment as, for example, the agriculture, the extractive industry and the public sector. In compensation, the miscellaneous services sector stands on this request and has important multiplier effect of employment in the state productive chain.
For the multiplier effects on income, the traditional sectors of the economy of Mato Grosso do Sul state have a prominent role. The public administration, agriculture and miscellaneous services lead the ranking, in this case. World Commission on Environment and Development: Our common future. Oxford: Mato Grosso - 2016 (Vinyl) University Press. Munich Personal Repec Archive. DIAS, D. Revista Brasileira de Desenvolvimento Regional, v. Contabilidade social. Elsevier, Rio de Janeiro. Input-Output Analysis: Theory and Foundations.
Rent derives from land through the formation of a powerful network state-landowners-private agroindustrial sector that provides the conditions for rent extraction. Citation Metrics. DOI: Farms can optimize fertilizer and pesticide use through smart farming [ 2 ] and satellite-based precision agriculture [ 3 ]. Retailers can use satellite images to measure client flows by counting cars in parking lots [ 4 ]. Non-governmental organizations and governments can target poor households for social interventions [ 5 ] or improve their response to humanitarian crises by using high-resolution satellite images [ 6 ].
The examples are plentiful, and the full potential of the new data sources is far from explored. In parts of the developing world where rural areas are changing rapidly, such as the Brazilian Cerrado and Amazon biomes, or the Gran Chaco region in South America, documenting the evolution of new forms of land use is essential for timely policy interventions [ 78 ].
However, the necessary data are often either unavailable or stored in databases that were designed for disparate purposes. Here, we explore a case where the careful triangulation of public information sources along with the use of open-access high-resolution satellite imagery greatly improved our ability to map supply chains. The beef industry in Brazil has a substantial economic and environmental impact, yet knowledge of its dynamics remains incomplete, particularly with regard to slaughterhouses.
By focusing on Mato Grosso—a Brazilian state the size of France and Germany put together and a cattle ranching powerhouse that spans the Amazon and Cerrado biomes—we developed a new method to map the slaughter industry across space and time. Documenting the slaughter industry dynamics in the Amazon is important for economic, conservation, and sanitation policies. In terms of agricultural and conservation policy, understanding the role of infrastructure development on the dynamics of land cover change is important [ 1718 ].
While a key predictor of the expansion of cattle-related deforestation is the provision of roads [ 1819 ], the role of slaughterhouses, a central type of infrastructure, remains elusive. Do slaughterhouses precede the expansion of cattle ranching, or the opposite? What drives company decisions for locating new plants and how does that vary through time and space?
Another benefit of generating such data is to produce a more precise understanding of the market share, distribution, and time dynamics of uninspected slaughter. This can be a central input to policymaking in the food inspection arena. The slaughter of animals with poor sanitation standards is a serious concern in the poorer areas of Brazil.
For Brazil as a whole, the estimated rate of uninspected slaughter is between Yet targeted action by sanitation agencies requires information on the location and capacity of the uninspected plants, which is currently unavailable. Knowledge of the location and clustering patterns of different types of animal slaughter infrastructure, and of ownership structure and changes, has been constrained by severe data limitations.
Not surprisingly, the best public data currently available provide only cross-sectional snapshots of the larger plants [ 2324 ], which limits analysis and policymaking efforts. Applications involving distant locations and markets where the slaughter infrastructure is mostly small scale are especially hindered by the lack of data. In this paper, we introduce the first longitudinal assessment of the cattle slaughter industry in Brazil.
After this introduction, we provide Mato Grosso - 2016 (Vinyl) information on the industry and the limitations of the existing data. In the methods section, we first provide a step-by-step explanation of how we mapped the space-time signature of slaughterhouses; second, we present the data validation procedures; and third, we describe how we analyze the results to learn about the temporal dynamics of slaughterhouses, pastures and cattle.
The results section provides information on the location, temporal dynamics, and slaughter volume of the slaughterhouses. Based on that, we provide estimates of the minimum size and distribution of the uninspected market. We also present the data validation results. Finally, we generate maps of the expansion of slaughterhouses across time and space and compare their evolution to the dynamics of grazing areas and cattle herds between and In the final section, we discuss the results and data limitations, list potential applications of the data, and provide new insights for the study of cattle and land use change.
Slaughterhouses have a central role in organizing supply chains, exerting influence on the size, type, and location of ranches [ 102526 ]. In Brazil, cattle herds and abattoirs have expanded since the late 16 th century [ 26 ] as cattle, buffalos, sheep and other bovid animals were essential for food, clothing and transportation in colonial times [ 27 ].
Such coevolving pattern of human settlements and cattle herds continues, especially in Mato Grosso - 2016 (Vinyl) frontier regions of the Amazon and Cerrado biomes [ 2930 ]. Accordingly, the infrastructure necessary to slaughter animals remains a constraint for the expansion of human settlements.
As of the last four decades of the 20 th century, bovine herds saw a steady movement toward the Amazon region [ 31 ], spurring environmental concerns due to the land-intensive nature of the activity. In this recent expansion wave, slaughterhouses became even more important players in the supply chain, increasingly influencing production practices at the farm level [ 33 — 35 ].
Information about the temporal and spatial dynamics of the slaughter infrastructure in Brazil is limited. Only recently have comprehensive maps of the larger slaughterhouses become available [ 23 — 24 ], but they provide only a contemporary snapshot of the larger plants. When were the plants created? When did holding companies merge? When were plants deactivated or closed? What were the existing plants at each point in time, and what was their productive capacity?
Knowledge of the local level infrastructure is also weak, with a gap in the understanding of the volume of slaughter in facilities with different types of food safety inspection. Slaughterhouses in Brazil are classified according to their sanitation inspection status: federal, state, municipality-level inspection, and uninspected. Plants with state inspection SIE have within-state market access, while plants with municipality inspection SIM are restricted to the county.
Uninspected plants tend to be small, local abattoirs and are more common where there is a less developed institutional framework. For example, the poorer states in the Northeast of Brazil as well as remote locations in the Amazon, where distances are large and population densities low, have a higher frequency of uninspected facilities [ 2022 ]. Unmet sanitation standards can have serious consequences.
Health implications for humans include the direct contamination by bacteria such as Salmonella and Escherichia Coli, Brucellosis a contagious zoonosisTaeniasis infection with Taeinia tapewormsand Toxoplamosis—all of which can lead to death—but also the increased chance of environment-related contaminations due to the improper management of waste products. Poor sanitation at slaughter can also send a signal to livestock producers that animals without appropriate vaccination and health care are acceptable.
This can lead to an increased risk of epidemiologic events such as the spread of the highly contagious viral foot-and-mouth disease. Finally, the economic consequences include a higher likelihood of low labor standards as well as depressed prices due to lack of market access. Whereas IBGE shows that uninspected slaughter in Brazil is on a downward trend, poorer areas lag behind. Moreover, even if most slaughter is sanitation-inspected, inadequate inspection remains an issue.
The federally inspected plants are recognized as the most likely to comply with sanitation standards. Lack of inspection and inadequate inspection are serious problems. One part of the uninspected market remains obscure despite all data gathering efforts. Clandestine slaughter is an illegal activity that takes place without a formal business registration, so it is absent from all official records.
This includes slaughter for auto-consumption taking place sporadically inside farms and ranches, but also unregistered abattoirs of different sizes.
Uninspected slaughter is thus a general concept that is more easily quantifiable as it can take place with a formal business registration. For the clandestine market share, only indirect inferences can be made. Bythis share had dropped to In terms of its spatial distribution, the best that can be said—given the data limitations—is that the prevalence of clandestine slaughter is likely higher in less developed locations, where regulations are less enforced, and that locations with a greater incidence of uninspected slaughter may also have more clandestine facilities.
Mato Grosso is the 3 rd largest state in Brazilkm 2 and a key conservation target for the Amazon and Cerrado biomes, which respectively occupy It has been leading the modernization of Brazilian agriculture and cattle ranching for over a decade, especially in large-scale commercial agriculture [ 41 — 43 ].
For example, Mato Grosso had the largest GDP growth relative to the country in —, and the fourth largest per capita income growth in the same period [ 4446 ]. With a small population 1. An important part of the industrial agglomeration that took place in Brazil since approximatelywhich led to the creation of the largest meatpacking conglomerate in the world [ 49 ], involved operations in the state of Mato Grosso [ 50 ]. Land use patterns in Mato Grosso are atypical for Brazilian standards.
Pasture area growth decelerated more rapidly than in other locations, while the opposite happened to crop areas. According to the agricultural census, pastures in Mato Grosso grew by In —, however, Mato Grosso had an additional 2. The total crop area, on the other hand, grew by In more recent years, while census data are unavailable, remote sensing data show that pasture areas continued expanding by a very small percentage [ 5455 ].
This has come with increased cattle densities, as we show in the Results section. In this study we chose to focus on plants that slaughter at least head per year, which may be called abattoirs but not butcheries.
This choice was made because very small facilities have a negligible impact on quantities but demand more data processing work as data are either unavailable or less transparent. Each plant has a single holding company at each point in time, but multiple plants can be owned by a holding. We documented and report only the most recent holding company of each plant, and as such miss the dynamics of previous ownership changes.
Our definition of holding comprises local businesses that own at least one small plant. These legal identities, known by the acronym CNPJ, are used to process corporate taxes and financial information. One plant will sometimes operate through two or more CNPJs. To generate a map of all slaughterhouses in Mato Grosso, we triangulated across Mato Grosso - 2016 (Vinyl) data sources including a registry of companies, government records of cattle transactions and of the sanitation inspection system, data compilations by a think-tank and a research lab, Mato Grosso - 2016 (Vinyl), open-access high-resolution satellite imagery, and others S1 Table.
Fig 1 provides a schematic view of the data sources used and the sequence of steps applied. The process was divided into five steps that we describe below: compiling a core dataset with key company identifiers, such as names and addresses; populating the dataset with attributes from multiple sources, especially opening and closing dates of a CNPJ ; grouping registered companies by physical plant and geocoding the addresses; documenting and inferring ownership changes and dates for the larger holding groups; and validating the data through comparisons with other sources of information.
We started by stacking up the two main sources of raw data step 1. We filtered 21 million records to obtain a list of businesses registered across Mato Grosso under economic activities related to cattle slaughter. The GTA records include 2, transactions, from which we identified companies slaughterhouses or abattoirs responsible for slaughtering cattle between and Next, we added the data from Imazon, a Brazilian environmental think-tank, and from Lapig, a remote sensing and geoprocessing research lab.
The Imazon data include inspection codes and the geolocations of 49 plants under federal or state inspection, and the Lapig data have the same information for 37 plants with federal inspection. We dropped observations with repeated CNPJs to get a raw list of registered companies. In step 2we populated the dataset with the attributes in S1 Table.
Sintegra can be queried using a CNPJ number and it provides the most accurate opening and closing dates. At this point, the units of observation were still the CNPJs, but these do not bear a one-to-one relationship with physical plants. In step 3we aggregated the CNPJs into plants. We grouped CNPJs within plants only if they were registered in the same municipality. The final dataset has plants. For the spatial coordinates, we followed a hierarchical decision rule. First, we used GPS points obtained in field visits.
Second, we used the coordinates provided by Google on its enterprise registry. Third, we used the coordinates provided by [ 2324 ], both of which were visually inspected using high resolution satellite imagery. Similar to plants operating through multiple CNPJs, a holding group may control multiple plants across the state. In step 4we grouped plants into holding companies. Plants with at least one CNPJ whose name or legal name was that of a known holding group, defined as any group listed in [ 23 ], the most comprehensive list available, were allocated to the known holding.
After the plants were aggregated into holdings, all the plants within each holding were coded as having signed a TAC commitment when at least one CNPJs associated to any of the plants within the holding had a TAC in place. Where a plant from a known holding group had CNPJs with different names, the plant was flagged as having gone through an ownership change.
We used [ 4850 ] to document the timing of the last ownership change. Where the date was not documented in those sources, we used two alternative assumptions. We used a switch in CNPJs as an indicator of ownership change. Finally, in step 5 we validated the spatial coordinates by the visual inspection of high resolution imagery on Google Earth. We assessed whether the mapped location showed the typical structure of a slaughter facility, which includes cattle corrals, industrial buildings and waste storage lagoons.
High resolution images are available for relatively recent years, so plants that closed before the early s were more difficult to identify.
This was a relatively minor problem, however, because even the plants that closed before satellite images were available could in some cases be identified since their structure remained visible years after the closing. Activity is defined as an ongoing company registration at least one open CNPJ for the years when no GTA information is available prior to or positive slaughter activity for other years. For example, if there was at least one active CNPJ but no slaughter activity in and after, the plant was coded as inactive in and after.
If there was slaughter activity in but no legally active CNPJ, the plant was active in If there was at least one active CNPJ inthe plant was active then. If there was no recorded slaughter activity in any year, the company was coded as inactive in the latest year when one of its CNPJs became inactive, even if one or more active CNPJs remained.
In seven situations—which we coded as active—did a plant show a positive slaughter activity in years subsequent to the closing of its last CNPJ 94 plants had GTA slaughter activity. CNPJ closing dates were taken from two sources. If at least one of these sources showed a closing date, we coded the CNPJ as closed. For non-SIF slaughterhouses, we used only the company registry. For plants operating earlier thanthese are the only sources of information for the starting date as the public GTA slaughter records start in For plants that were operative in or after, the GTAs allowed us to estimate the starting date even if the plants had no legal registration or sanitation inspection.
In these cases, we assigned the first appearance in the GTA as the opening year. The weakest part of the animal inspection system is the municipality [ 56 ]. This is the first reason why we code SIM plants as uninspected. The second reason is that, even if desirable, distinguishing SIMs from uninspected at a large scale is not possible due to the lack of consistent data on the location of SIM plants. So for simplicity, we refer to both as uninspected.
In the past, even the plants with state inspection where classified as uninspected due to the lack of data [ 38 ]. Moreover, only seven plants in Mato Grosso were reported by IBGE as being under municipality inspection in [ 39 ].
We used the following formula to calculate the slaughter volumes of uninspected plants at the municipality level: 1. We used external sources of information other than those presented in S1 Table to validate our results and discuss uncertainty and error in the data. First, we assessed the degree to which the sample used is representative of the population of slaughter transactions.
The IBGE records are collected from informants in the slaughterhouses, while the GTA records are collected from the ranchers selling to the slaughterhouses. Second, we evaluated the historical accuracy of the data. Registration years indicate the formal registration of companies, but the plants may have operated from an earlier time or the registration date of those that were closed before the digital era may have gotten lost.
We thus collected historic information on the first slaughterhouses operating in parts of Mato Grosso. Finally, to validate our estimates of the uninspected market, we used municipality-level data on formal employment from the Brazilian Ministry of Labor.
The employment data provide counts of workers in businesses registered as bovine cattle slaughter units across the country. If a municipality does not have any inspected slaughterhouse but does have workers registered in the slaughtering industry, then it is likely Mato Grosso - 2016 (Vinyl) have uninspected cattle slaughter.
To put the evolution of slaughterhouses into context, we produced maps of their expansion as compared to grazing areas and cattle herds for the years — For cattle herds, we used municipality-level yearly data from [ 33 ] to calculate cattle densities for each year and municipality. For pastures, we combined [ 54 ] and Lapig [ 56 ] pasture classifications to produce maps of the maximum area under pasture at each year. The Lapig product classifies pasture and non-pasture based on Landsat-8 images with a resolution of 30 meters.
The Mapbiomas product uses Landsat 5, 7 and 8 with a 30 meter resolution. We then overlaid all pasture maps to obtain a maximum pasture area among all years.
From that, we sampled 15, random points, in three iterations, to represent the maximum pasture surface of Mato Grosso S1 Fig.
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Mato Grosso (Portuguese pronunciation: [ˈmatu ˈɡɾosu] – lit. "Thick Bush") is one of the states of Brazil, the third largest by area, located in the western part of the country. The state, which has % of the Brazilian population, is responsible for % of the Brazilian GDP.. Neighboring states (from west clockwise) are: Rondônia, Amazonas, Pará, Tocantins, Goiás and Mato Grosso.
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Mato Grosso (Portuguese pronunciation: [ˈmatu ˈɡɾosu] – lit. "Thick Bush") is one of the states of Brazil, the third largest by area, located in the western part of the country. The state, which has % of the Brazilian population, is responsible for % of the Brazilian GDP.. Neighboring states (from west clockwise) are: Rondônia, Amazonas, Pará, Tocantins, Goiás and Mato Grosso. Dec 10, · Label: B4 Before -- BEF Format: Vinyl, 12", 45 RPM Country: Italy Released: Genre: Electronic Style: Techno.
Ph.D. in Applied Linguistics and Language Studies from the PUC-São Paulo and M.A. degree in the same area and university. Work as Associate Professor at Universidade Federal de Mato Grosso do Sul (UFMS) teaching subjects on the scope of Linguistics and English for .
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