In 2023, it is estimated that about *********** Brazilians lived in the United States. Of these, ************** lived in the state of New York. The largest community resided in the state of Florida, with around ******* Brazilians divided between the consulate in Miami and the consulate in Orlando. Brazil-U.S. relations In 2024, Brazil and the United States celebrated 200 years of diplomatic relations. The countries cooperate in various sectors, but the economy stands out the most, as the United States was Brazil's second-largest trading partner in 2023. The trade between these countries amounted to over ** billion dollars in that year. This proximity between the countries is appreciated by Brazilian citizens, who mostly have a good image of the North American country. U.S. Brazilian imports The value of U.S. imports of Brazilian origin has grown in recent decades. After a decline in 2020, the value of imports increased by around ***** billion U.S. dollars and, in 2023, the United States imported approximately 39 billion U.S. dollars’ worth of Brazilian goods. This was the highest level of Brazilian imports since 1985. Furthermore, the imports of agricultural products from Brazil totaled nearly *** billion U.S. dollars in 2023.

Earthworms are often related to fertile soils and frequently used as environmental quality indicators. However, to optimize their use as bioindicators, their populations must be evaluated together with environmental and anthropogenic variables regulating earthworm communities. In this review we identify the earthworm, soil chemical, physical, environmental and management-related variables evaluated in 124 published studies that quantified earthworm abundance (>7300 samples) in 765 sites with different types of climate, soils, land use and management systems in Brazil. Most soil chemical and physical attributes (except pH) were less reported (<50% of studies) than other environmental variables such as sampling date, altitude, temperature, precipitation, climate and soil type and land use (all >50% of studies). Earthworms were rarely identified (24%) and few studies (31%) measured their biomass, although most provided adequate information on sampling protocol. Based on the importance in regulating earthworm populations, we propose a set of variables that should be evaluated when studying earthworm communities and other macrofauna groups. This should help guide future studies on earthworms in Brazil and other countries, optimize data collection and replicability, allow comparisons between different studies and promote the use of earthworms as soil quality bioindicators.

Methods A dataset of earthworm abundance in Brazilian ecosystems was constructed using published literature on the topic. Published studies that evaluated earthworm populations in Brazilian ecosystems were searched in the literature from 1976 up to the year 2017. The literature review included searchable online databases such as Web of Science, Scielo, Lattes-CNPq Platform, Biblioteca Digital de Teses e Dissertações (BDTD- Brazilian digital library of theses and dissertations), Google Scholar and the Alice-Embrapa Repository. As we were aiming to review all studies available and determine which soil, environmental, earthworm and sampling-related factors were evaluated, we also included non-indexed journals, book chapters, and conference proceedings in soil science, zoology, ecology, agroecology and conservation agriculture. We also made personal contact with colleagues who work with earthworms and/or are members of the Brazilian CNPq research group “Biology, ecology and function of terrestrial Brazilian oligochaetes (earthworms and enchytraeids)”, in order to help us expand our data search.

The data on soil, environmental and earthworm sampling variables were extracted from each of the 124 publications and entered into an excel data file. Using the data for all sites, the number of publications with each environmental, earthworm and soil physical and chemical variables was quantified, as well as the corresponding number of points/sampling sites. The data contains information from 765 specific sites, representing over 7300 earthworm samples, from a wide range of soils, vegetation types and management systems in 135 counties in Brazil. Additional environmental and soils data were also obtained from these sites and complemented with information from various sources (e.g. online climate data, municipal GPS coordinates, Köppen climate database), or calculated (e.g. CEC or Sum of bases, pH, H+Al, C:N) and included, when possible. Climate information was corrected also when needed, following the data of Alvares et al. (2013). When not provided, geographic position data used was for the county seats, and when found to be incorrect, the county or state information was corrected. The data thus includes 13 climate and vegetation-related environmental variables (Sampling date, Sampling season, Locality, County, State, Geographical coordinates, Altitude, Mean annual precipitation, Mean annual temperature, Köppen climate, Biome, Soil cover/Vegetation type, Type of native vegetation), 8 management-related variables (Crop type, Soil management, Years in current land use, Previous land use, Pesticide use, Pesticides type, Fertilizers, Fertilizer type), 17 soil-related variables (Soil type, pH, H+Al, K, Ca , Mg, P , C, Sum of bases, CEC, Base saturation, N , C:N, Sand, Clay, Silt, Textural class), and 6 earthworm sampling-related variables (Size of holes dug, Number of holes, Depth, Density, Biomass, Species identification).

Native vegetation types were classified according to standard categoreies (IBGE, 2012), while forest plantations were divided into four main types: Eucalyptus or Pinus spp. trees, Araucaria angustifolia, and Others (contemplating all other tree species). Pasture grasses were separated into only two categories, Brachiaria spp. pastures (some of which are currently in the Urochloa genus), and Others, including all other types or species of pasture grasses. Soil types information was obtained from the publications or using the Brazilian national or state maps available online and from Embrapa databases (IBGE; Santos et al., 2018). Soil were classified according to the Brazilian (Santos et al., 2018) and the FAO (2015) soil classification systems. Soil tillage was classified into four categories, according to decreasing intensity: conventional tillage (CT), minimum tillage (MT), no tillage (NT) and permanent crop with no tillage (PC). Similarly, pesticide and fertilizer use information were searched for in each publication, including types (herbicides, fungicides or insecticides and fertilizer formulation) and active ingredients.

The soil chemical (pH, H+Al, K, Ca, Mg, P, C, sum of Bases, CEC, Base saturation, N and C:N ratio) and physical (sand, clay and silt proportions) attributes included were those generally provided in routine soil fertility analyses in Brazilian soil analysis laboratories (van Raij, 1987), except for C and N by combustion (which are not routine in most laboratories). Soil pH values were transformed to equivalent of pH in CaCl2 or KCl for all samples, using a conversion factor of 0.6 when measured in water (i.e., pH in water was considered to be 0.6 points higher than that estimated in CaCl2 or KCl). Values obtained in CaCl2 or KCl were maintained and considered of similar magnitude for these two methods. All values of H+Al, K, Ca, Mg, sum of Bases and CEC not in standard units (cmolc dm-3) were transformed. Phosphorus values were standardized to mg dm-3 and only studies using the Mehlich-extraction method were included. Carbon and Nitrogen values were in g dm-3 and based on analyses performed using combustion or Walkley-Black digestion. When OM values were given, C was estimated using the “Van Bemmelen” factor, dividing OM by 1.72. Total sand, clay and silt contents were all in g kg-1. Textural soil classes were based on the soil texture triangle of IBGE (2007), similar to that of FAO (2015). When several sources of data were available for the same collection site, or when it was sampled more than once (for example, at different times of the year, or different years), the mean values of chemical and physical attributes of soils were calculated.

Only studies performed using the standard method for collecting earthworms in tropical soil conditions, based on ISO (2018) and the Tropical Soil Biology and Fertility (TSBF) method (Anderson & Ingram, 1993) were included in the database. Information on the size of excavated area to collect the earthworms, the number of samples taken per site, and the depth of the sample were extracted from each publication. Mean earthworm density and biomass data (when available) were entered into the database as well as information on species identification (if performed or not). Earthworm data was entered as number of individuals per m2 (no. indiv. m-2) and biomass expressed as fresh mass in g m-2 (fresh weight in preservative liquid, including intestinal contents). When the data were not expressed in these units, they were obtained by using sample number and the size (area, in m2) at each date and site.

When more than one source of data was available by the same authors for the same site, i.e., if it was sampled more than once (e.g., at different times of the year or different years), mean density and biomass values were calculated for each sample site or treatment evaluated, so that each individual treatment or site was represented by only one line in the database. A preliminary analysis of climate effects showed that earthworm abundance and biomass at the same site could be significantly affected by the sampling date, particularly at sites in climates with pronounced dry and wet seasons. For sites in these climates, dry season sample dates were excluded from the database, as they could not be compared with samples of earthworm populations taken in the wet season. For cases where multiple samples were taken during the wet season, data were combined and means calculated per sample site. For climate types where there is no dry season, data from all sampling dates, irrespective of season were combined and means calculated per site.

All data is provided in excel format, and includes four tabs in the data file: Legend, Data base, count and References. The Legend tab provides a detailed explanation for each variable included in the Data base tab. The counts tab provides a table with the total number and percentage of times each variable was provided in the 124 publications used. The References tab gives the full bibliographic information on each of the 124 studies (148 publications) used, of which most (80%) are in Portuguese.

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of African households in rural Eastern Cape and African and Coloured households in urban Western Cape.

Kind of data

Survey data

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or coloured EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Statssa) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Coloured)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly-selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban coloured (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban coloured households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least 100

The franciscana, Pontoporia blainvillei, is the most endangered small cetacean in the Southwestern Atlantic Ocean, occurring from Itaúnas, Espírito Santo, Brazil to Chubut province, Argentina. This area is divided into four Franciscana Management Areas (FMA). The northern portion of this species distribution is not continuous and a previous genetic study using mitochondrial DNA (mtDNA) separated it into FMAIa (Espírito Santo state) and FMAIb (North of Rio de Janeiro state). In order to increase the information about this population we expanded the sample number and evaluated mitochondrial and nuclear DNA diversity. Samples of 68 franciscanas found stranded on beaches from 2005 to 2020 were analyzed. Analyses included 350 bp of the mtDNA control region (D-loop) and 12 microsatellite loci. We identified three control region haplotypes in FMAIa, two of them not previously observed in this population, one being a new haplotype. Haplotype and nucleotide diversities were 0.0408 and 0.00012 respectively, the lowest reported for all FMAs analyzed until now. The Neutrality tests were not significant and Mismatch Distribution analysis did not reject the hypothesis of population expansion. One of the microsatellite loci was monomorphic, and for the other loci, two to nine alleles were identified, with expected heterozygosities ranging from 0.306 to 0.801. No substructure was revealed and effective population size (Ne) was estimated in 117.9 individuals. Even with an increased sample size, the high mitochondrial genetic homogeneity suggested for the population in a previous study was confirmed. Among six loci previously analyzed in other franciscana populations, five showed the lowest observed heterozygosities for the Espírito Santo population. The novel microsatellite data also showed low genetic diversity and could not reject the hypothesis of a single, panmitic population along the coast of Espírito Santo. This species has been intensively impacted in the last years by incidental capture during fishing activities and habitat degradation, caused by pollution, coastal development and environmental disasters in FMAIa. Considering that this population is small, isolated, and with low levels of genetic diversity, we reinforce the necessity of different conservation actions, focusing mainly on the reduction of bycatch of this species in the region.