Argentina Land Use: Land Area: Permanent Meadows and Pastures data was reported at 746,810.000 sq km in 2022. This stayed constant from the previous number of 746,810.000 sq km for 2021. Argentina Land Use: Land Area: Permanent Meadows and Pastures data is updated yearly, averaging 999,550.000 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 1,183,570.000 sq km in 1961 and a record low of 746,810.000 sq km in 2022. Argentina Land Use: Land Area: Permanent Meadows and Pastures data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

Argentina Land Use: Land Area: Arable Land and Permanent Crops data was reported at 432,767.600 sq km in 2021. This records an increase from the previous number of 430,767.600 sq km for 2020. Argentina Land Use: Land Area: Arable Land and Permanent Crops data is updated yearly, averaging 277,000.000 sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 432,767.600 sq km in 2021 and a record low of 194,720.000 sq km in 1961. Argentina Land Use: Land Area: Arable Land and Permanent Crops data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

Agricultural land (% of land area) in Argentina was reported at 42.44 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Argentina - Agricultural land (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

[This dataset is embargoed until November 10, 2025]. The data resource consists of a series of land cover maps built using raster and shapefiles to evaluate the expansion of the invasive Ligustrum lucidum forest cover in Sierra de San Javier (Yungas forest ecoregion). The classification was conducted to investigate the expansion of the invasion of non-native species Ligustrum at the landscape scale and to model future management strategies using RangeShifter software. The data includes 4 maps with 8 classes of land used: Ligustrum forest; Subtropical montane forest; Dry forest; Montane grasslands; Anthropogenic grasslands and shrubland used for livestock and temporary agriculture, a mixed class including also herbaceous agriculture and low-density urban areas; Sugar cane; Citrus plantations, mostly lemon; high/medium-density urban and build up areas. The work was carried out as part of NERC grant NE/S011641/1 Optimising the long-term management of invasive species affecting biodiversity and the rural economy using adaptive management Full details about this dataset can be found at https://doi.org/10.5285/4d30e697-6a97-45ed-95e6-ac4d66247284

Argentina Land Cover: Wetland: Total data was reported at 86,456.000 sq km th in 2020. This records a decrease from the previous number of 86,605.000 sq km th for 2015. Argentina Land Cover: Wetland: Total data is updated yearly, averaging 86,456.000 sq km th from Dec 2000 (Median) to 2020, with 5 observations. The data reached an all-time high of 86,924.000 sq km th in 2010 and a record low of 82,854.000 sq km th in 2000. Argentina Land Cover: Wetland: Total data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.ESG: Environmental: Land Cover: Non OECD Member: Annual.

Argentina AR: Forest Area data was reported at 284,636.667 sq km in 2021. This records a decrease from the previous number of 285,730.000 sq km for 2020. Argentina AR: Forest Area data is updated yearly, averaging 316,378.000 sq km from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 352,040.000 sq km in 1990 and a record low of 284,636.667 sq km in 2021. Argentina AR: Forest Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Argentina – Table AR.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.;Food and Agriculture Organization, electronic files and web site.;Sum;

Argentina AR: Arable Land: % of Land Area data was reported at 15.749 % in 2022. This records an increase from the previous number of 15.431 % for 2021. Argentina AR: Arable Land: % of Land Area data is updated yearly, averaging 9.768 % from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 15.749 % in 2022 and a record low of 6.795 % in 1961. Argentina AR: Arable Land: % of Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Argentina – Table AR.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.;Food and Agriculture Organization, electronic files and web site.;Weighted average;

The dataset contains landscape metrics for circular buffer areas of increasing radius (250, 500, and 1000 m) around each of 15 sampling sites. landscape metrics were calculated using Fragstats (McGarigal et al. 2012; http://www.umass.edu/landeco/research/fragstats/fragstats.html). Sampling sites were urban green spaces located in Córdoba city, Argentina. Land cover characteristics were assessed for the three circular buffer areas around each sampling site using Google Earth and Sentinel 2 images (December 14, 2016; spatial resolution 10 m). The land-use categories considered were: Built surface, Open vegetation (mostly grasses and low growth), Closed vegetation (mostly trees) and Water bodies. We calculated landscape metrics using two levels of spatial heterogeneity hierarchy: landscape level metrics and class level metrics. These metrics provide information about landscape and patch class fragmentation, interspersion and aggregation (see details in read me file).

The present dataset originates from a research project aimed at evaluating existing knowledge regarding how land use impacts amphibian species in the Pampa Biome. In this dataset, we have compiled information from 76 selected articles based on the following criteria: 1) articles published in scientific journals between the years 2000 and 2022; 2) a combination of the keywords 'anuran AND agroecosystem AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND monoculture AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND urbanization AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND livestock AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND land use AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND soybean AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND rice AND Uruguay OR Argentina OR Rio Grande do Sul'; 'anuran AND forestry AND Uruguay OR Argentina OR Rio Grande do Sul'; 3) research on the platforms Scopus, Science Direct, JSTOR, Google Scholar, Timbó Foco, and Periódicos CAPES. This dataset provides information on authors, publication year, the scientific journal in which the study was published, location (regions of Argentina, Uruguay, or Brazil), method (field or field complemented with experiments), anthropogenic activity, chemicals (when specified), effects on anurans, biological model (assemblage or species), developmental stage, and conservation status.

Agricultural land area of Argentina rose by 0.17% from 1,177,578 sq. km in 2020 to 1,179,578 sq. km in 2021. Since the 1.43% decline in 2018, agricultural land area went up by 1.72% in 2021. Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.

Synergistic combinations of climatic and land use changes have the potential to produce the most dramatic impacts on land cover. Although this is widely accepted, empirical examples, particularly involving deforestation in Latin America, are still very few. The geographic extent and causes of deforestation in subtropical seasonally dry forests of the world have received very little attention. This is especially true for the Chaco forests in South America, which are being lost at an alarming rate, sometimes higher than those reported for tropical forests. On this basis, the aims of this study were to analyze the changes in land cover that have occurred during the last three decades of the 20th century in the Chaco forests of central Argentina, and to explain the factors that have driven those changes. Results show major land cover changes. Approximately 80% of the area that was originally undisturbed forest is now occupied by crops, pastures, and secondary scrub. The main proximate cause of deforestation has been agricultural expansion, soybean cultivation in particular. This appears as the result of the synergistic convergence of climatic, technological, and socioeconomic factors, supporting the hypothesis of a multiple-factor explanation for forest loss, while providing one of the very few existing analyses of changes in subtropical forests of the world.

Argentina Land Use: Land Area data was reported at 2,736,690.000 sq km in 2022. This stayed constant from the previous number of 2,736,690.000 sq km for 2021. Argentina Land Use: Land Area data is updated yearly, averaging 2,736,690.000 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 2,736,690.000 sq km in 2022 and a record low of 2,736,690.000 sq km in 2022. Argentina Land Use: Land Area data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

The Saffron-cowled Blackbird (Xanthopsar flavus) is a globally endangered icterid endemic to the southern South American grasslands. Temperate grasslands are among the most threatened ecosystems in South America due to their high rate of land use change. In northeastern Argentina, over the last century, the conversion of natural grasslands to livestock farming, croplands, and afforestation have resulted in significant losses of breeding habitat for the Saffron-cowled Blackbird. Consequently, it has suffered severe populational declines, and its remaining populations are fragmented. In order to understand the impact of agricultural systems on the Saffron-cowled Blackbird populations, we studied its breeding biology (clutch size, hatching success, nestling production, and fledgling success) and the main parameters that influence nest survival rate in five breeding habitat types. During the breeding seasons of 2015 to 2019 we located nests and monitored their fate. We found that the cumulative probability of nest survival over the entire nesting cycle (i.e., laying, incubation, and chick rearing) was 0.06, and was lower for nests situated in grazed land covers. Conversely, nest survival was greater in breeding sites without agricultural use, particularly in marshes. Predation was the main cause of nest failure (76%), followed by brood parasitism (10%) and trampling by cattle and agricultural machinery (6%). Brood parasitism rates were higher in grazed paddocks, contributing together with predation to the failure of nests in this habitat. Our findings indicate a negative impact of livestock ranching on Saffron-cowled Blackbird reproduction. Non-agriculture habitats, like wetlands and flooded areas, are important as refuges for nesting. Thus, the creation of breeding refuges (non-productive sites) within agricultural matrices, in association with biodiversity-friendly agricultural practices, is crucial to ensure the Saffron-cowled Blackbird's maintenance. Methods GENERAL INFORMATION

1. Dataset used for the article: Saffron-cowled Blackbirds' reduced nest success in Argentina's agricultural land highlights the importance of non-agricultural habitat for its conservation.

2. Study published in Ornithological Applications by Pucheta, M.F; Pereda, M.I, and Di Giacomo, A.S. Corresponding author email: pucheta.mf@gmail.com Correspondig author institution: Centro de Ecología Aplicada del Litoral, CONICET.

3. Date of data collection: 2015-2019

4. Geographic location of data collection: Argentina (Entre Ríos and Corrientes provinces).

For specific data collection methods please reefer to method section of the manscript in the DOI: 10.1093/ornithapp/duae006. Journal: The Condor: Ornithological Applications

1. Information about funding sources that supported the collection of the data: PICT2014–3397, PICT2014–2057, PIP2022-2024, Neotropical Bird Club, Neotropical Grassland Conservancy, The Rufford Fundation, Young Conservation Leadership Program from BirdLife International and Aves Argentinas.

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain

2. Links to publications that cite or use the data: Pucheta, Maria Florencia; Pereda, Maria Inés; Di Giacomo, Adrián Santiago (Forthcoming 2024). Saffron-cowled Blackbirds' reduced nest success in Argentina's agricultural land highlights the importance of non-agricultural habitat for its conservation. Ornithological Applications.

3. Links to other publicly accessible locations of the data: None

4. Links/relationships to ancillary data sets: None

5. Was data derived from another source? No A. If yes, list source(s): NA

6. Recommended citation for this dataset: Pucheta, Maria Florencia; Pereda, Maria Inés; Di Giacomo, Adrián Santiago (Forthcoming 2024). Data from: Saffron-cowled Blackbirds' reduced nest success in Argentina's agricultural land highlights the importance of non-agricultural habitat for its conservation. Dryad Digital Repository. https://doi.org/10.5061/dryad.4mw6m90hw

DATA & FILE OVERVIEW

1. File List (nomenclature: abbreviated name of Xanthopsar flavus (xafl) + parameters presented in the datase)

A) xafl_Nest_Height.xlsx B) xafl_Brood_parasitism.xlsx C) xafl_Reproductive_Parameters.xlsx D) xafl_Nest_Failure_Causes.xlsx E) xafl_Nest_Survival_for_Mark.xlsx F) xafl_Nest_Attempts.xlsx G) xafl_Nest_survival_across_Breeding_Season.xlsx H) xafl_Nest_Survival_and_Brood_Parasitism_Graphs

A) DATA-SPECIFIC INFORMATION FOR: xafl_Nest_Height.xlsx

1. Number of variables: 2

2. Number of cases/rows: 222

3. Variable List:

   • breeding_habitat: the five breeding habitat studied (marsh, roadside, hedgerow, grazed_paddock, cropland)

   • nest_height: height in centimeters at which each of the nests was constructed.

4. Missing data codes: None

B) DATA-SPECIFIC INFORMATION FOR: xafl_Brood_parasitism.xlsx

1. Number of variables: 3

2. Number of cases/rows: 223

3. Variable List:

   • nest_ID:the identity of each nest.

   • BreedingHabitat: the five breeding habitat analyzed (marsh, roadside, hedgerow, grazed_paddock, cropland)

   • BroodParasitism: binary variable: presence (1) or absence (0) of brood parasitism in each nest.

4. Missing data codes: None

C) DATA-SPECIFIC INFORMATION FOR: xafl_Reproductive_Parameters.xlsx

1. Number of variables: 9

2. Number of cases/rows: 107

3. Variable List:

   • Hatching_success:Hatching success per nest (number of eggs hatched over the total number of eggs laid).

   • Breeding_habitat_HS: reproductive habitats types for nests with information on hatching success(marsh, roadside, hedgerow, grazed_paddock, cropland).

   • Year_HS: the corresponding year (2015-2019) of the breeding season for which hatching success was calculated.

   • Clutch_size:clutch size (number of eggs) per nest.

   • Breeding_habitat_CS: reproductive habitats types for nests with information on clutch size.

   • Year_CS: the corresponding year (2015-2019) of the breeding season for which clutch size was calculated.

   • Fledgling_success: fledging success for each nest (chicks that successfully flew from the nest over total chicks present in the nest).

   • Breeding_habitat_FS: reproductive habitats types for nests with information on fledgling success.

   • Year_FS: the corresponding year (2015-2019) of the breeding season for which fledgling success.

4. Missing data codes: rows with "n/a" for nests with some parameter data not available.

D) DATA-SPECIFIC INFORMATION FOR: xafl_Nest_Failure_Causes.xlsx

1. Number of variables: 2

2. Number of cases/rows: 168

3. Variable List:

   • BreedingHabitat: the five breeding habitat analyzed (marsh, roadside, hedgerow, grazed_paddock, cropland).

   • FailureCause: the causes that led to failure in each nest (depredated, parasitated, trampling, abandonment, storms).

4. Missing data codes: None

E) DATA-SPECIFIC INFORMATION FOR: xafl_Nest_Survival_for_Mark.xlsx

1. Number of variables: 17

2. Number of cases/rows: 223

3. Variable List:

   • NestID: nest identity number

   • FirstFound: Julian day of the encounter date.

   • LastCheck: Julian day of the last date of the nest with contents.

   • Fate: Final status of the nest: successful (0) or failed (11).

   • NestAge: Days elapsed since the laying of the first egg on day 0.

   • year2015: Dummy variable for the year 2015 (nest of 2015 season are 1, nest from another seadon are 0).

   • year2016: Dummy variable for the year 2016 (nest of 2016 season are 1, nest from another seadon are 0).

   • year2017: Dummy variable for the year 2017 (nest of 2017 season are 1, nest from another seadon are 0).

   • year2018: Dummy variable for the year 2018 (nest of 2018 season are 1, nest from another seadon are 0).

   • Roadside:Dummy variable for the roadside habitat.

   • Hedgerow: Dummy variable for the hedgerow habitat.

   • GrazedPaddock: Dummy variable for the grazed paddock habitat.

   • Cropland: Dummy variable for the cropland habitat.

   • BroodParasitism: Binary variable for brood parasitism presence.

   • NestHeight: cuantitative variable measured as nest height in cm.

   • ColonySize: The number of total nest in the colony.

4. Missing data codes: None.

   This dataset (E) is for Daily survival rate estimations in the software Mark, tool "Nest survival"

F) DATA-SPECIFIC INFORMATION FOR: xafl_Nest_Attempts.xlsx

1. Number of variables: 4

2. Number of cases/rows: 8

3. Variable List:

   • TimeOfBreeding: The progression of the breeding season expressed in fortnights.

   • TimeOfBreeding_name: The name assigned to each fortnight on the x-axis of the graph.

   • n: The number of nest attempts per fortnigh.

   • PercentageNestAttemps: The percentage of nest attempts per fortnight relative to the total nest attempts.

4. Missing data codes: None.

This dataset (F) was used to plot nest attempts against the progression of the breeding season.

G) DATA-SPECIFIC INFORMATION FOR: xafl_Nest_survival_across_Breeding_Season.xlsx

1. Number of variables: 417

2. Number of cases/rows: 124

3. Variable List:

   • Breeding_time: Progression of the breeding season in days.

   • Daily_survival_rate: Daily survival rate calculated with MARK for each day within the breeding season.

   • LCI:Lower confidence interval of the daily survival rate.

   • UCI:Upper confidence interval of the daily survival rate.

4. Missing data codes: None

The present dataset (G) was used to plot the daily survival rate against the quadratic trend of the breeding time.

H) DATA-SPECIFIC INFORMATION FOR: xafl_Nest_Survival_and_Brood_Parasitism_Graphs.xlsx

1. Number of variables: 7

2. Number of cases/rows: 5

3. Variable List:

   • Environment: the five reroductive habitat analyzed (marshes, native hedgerow, croplands, roadside ditches

Argentina Land Use: Land Area: Other data was reported at 1,264,654.000 sq km in 2022. This records a decrease from the previous number of 1,272,269.000 sq km for 2021. Argentina Land Use: Land Area: Other data is updated yearly, averaging 1,279,030.500 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 1,462,890.000 sq km in 1988 and a record low of 1,109,000.000 sq km in 1990. Argentina Land Use: Land Area: Other data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.ESG: Environmental: Land Use: Non OECD Member: Annual.

This dataset contains 2001-2020 burned areas and climate variables for three regions with Mediterranean climates: South America from 31-46 degrees South, including Chile and the forested Andean region of Argentina; the western United States from 33-49 degrees North from the coast extending to the eastern extent of forest, and the Iberian Peninsula, including all of Spain and Portugal.

Burned areas are polygon shapefiles for all regions except Chile, for which the burn area is represented in a point shapefile. The data sources for the fire shapefiles are: Chile: unpublished, originally from Corporación Nacional Forestal (CONAF) and compiled by Miranda Argentina: unpublished, compiled by Diego Mohr-Bell and others at Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP) North America: NIFC 2023 Iberian Peninsula: EFFIS 2022

All of the fire shapefiles are contained within the zip folder fire_areas, and the individual regions are ch_fire (Chile), ar_fire (Argentina), na_fire (North America), ib_fire (Iberian Peninsula). The attributes of the shapefiles are the year and the fire area in square kilometers. For Chile, the fire start dates were documented. If the fire started in June-December, the year assigned is advanced by 1 from the original year. This is because the summer fire season straddles the calendar year boundary, and the fire year is assigned based on the year with most of the summer season. For Argentina, the end dates of the fire were available, so these end dates were used to assign the fire year.

Annual summaries of fire area and climate variables are provided in the fire_ann_all.csv file. The columns in this file are: year wetdryzone: dry if mean annual aridity index <1; wet if mean annual aridity index >1 cont: location, either Iberian Peninsula, North America, or South America area_km2: total burned area in square km AIann: annual aridity index calculated as total precipitation over total potential evapotranspiration AIjas: summer aridity index for July-September in northern hemisphere; January-March, southern hemisphere vpd: mean annual vapor pressure deficit (kPa) vpd_jas: mean summer vapor pressure deficit (kPa) def: mean annual climatic water deficit (mm) def_jas: summer climatic water deficit (mm)

Climate data were obtained from TerraClimate (Abatzoglou et al. 2018).

Mean annual summaries of fire and climate data by aridity index zone are provided in the file AI_bins_meanannual.csv. AI zones/bins are in increments of 0.2. Columns are: cont: location, either Iberian Peninsula, North America, or South America AI_max: maximum AI for the AI zone. For example, if the value is 0.2, the zone is from AI=0 to AI=0.2 zone_area: total area of the climate zone in square kilometers forest_area: total area forested in 2000 in square kilometers (Potapov et al. 2022) fire_area: total area burned from 2001-2020 in square kilometers (same sources as fire shapefiles) frac_forest: fraction of climate zone area that is forested frac_fire: fraction of the climate zone area that was burned mean_patch_area: mean size of forest patches identified within each AI zone in square kilometers fwi: mean fire weather index from the European Center for Medium-range Weather Forecasts (Vitolo et al. 2020)

Original data sources: Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C. (2018), Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 170191.

European Forest Fire Information System EFFIS (2022). Burnt area mapped using Sentinel-2/MODIS images. Accessed September, 2022.

NIFC 2023. Interagency Fire Perimeter History https://data-nifc.opendata.arcgis.com/search?tags=Category%2Chistoric_wildlandfire_opendata, downloaded 3/25/23.

Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing, 3.

Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11.

Abstract

The documented dataset covers Enterprise Survey (ES) panel data collected in Argentina in 2006, 2010 and 2017, as part of the Enterprise Survey initiative of the World Bank. An Indicator Survey is similar to an Enterprise Survey; it is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the 2006-2017 Enterprise Survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the Indicator Survey data provides information on the constraints to private sector growth and is used to create statistically significant business environment indicators that are comparable across countries.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2006-2017 Argentina Enterprise Survey (ES) was selected using stratified random sampling, following the methodology explained in the Sampling Manual. Stratified random sampling was preferred over simple random sampling for several reasons: - To obtain unbiased estimates for different subdivisions of the population with some known level of precision. - To obtain unbiased estimates for the whole population. The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors (group D), construction (group F), services (groups G and H), and transport, storage, and communications (group I). Groups are defined following ISIC revision 3.1. Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, excluding sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors. - To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions. - To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)

Three levels of stratification were used in every country: industry, establishment size, and region.

Industry stratification was designed in the following way: In small economies the population was stratified into 3 manufacturing industries, one services industry - retail-, and one residual sector as defined in the sampling manual. Each industry had a target of 120 interviews. In middle size economies the population was stratified into 4 manufacturing industries, 2 services industries -retail and IT-, and one residual sector. For the manufacturing industries sample sizes were inflated by 25% to account for potential non-response in the financing data.

For the Argentina ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposed, the number of employees was defined on the basis of reported permanent full-time workers. This resulted in some difficulties in certain countries where seasonal/casual/part-time labor is common.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies:

a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).

b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from "Don't know" responses.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals; whenever this was done, strict rules were followed to ensure replacements were randomly selected within the same stratum. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

ABSTRACT The seasonally dry subtropical forest, especially the Espinal forest in central Argentina (one of the most fragmented ecoregions), is affected by intensive agricultural activity. These activities are the main anthropogenic sources of atmospheric nitrogen compounds and their effects on lichens have been extensively studied, making them excellent ecological indicators. However, in the Espinal forest, the agricultural emissions are not monitored therefore analysis of the response of lichen diversity to these activities has a fundamental role in providing baseline data for monitoring. We analyzed changes in the frequency of families and genera of epiphytic lichen communities in 39 circular buffer areas at different scales comprising crop production, stock farming (feedlots), grazing and forest. Significant correlations at different taxonomic levels were detected in relation to land use. Frequency of Physciaceae increased with an increasing area of cropland to a distance of 600 m. Likewise a positive correlation was observed between the frequency of Collemataceae and the forest area. At genus level, Physcia presented a different response to livestock according to the intensity of production, since the frequency of these species increased in forest patches surrounded by grazing but decreased in areas with livestock farming where the stocking density is higher. This result could indicate an eutrophication process in the Espinal ecosystem, even for Physcia species. Our results can be used to start a list of indicator species to impact of agricultural in forest ecosystems.

TIAS 13-215 First signed 11/30/2012 Last signed 02/15/2013 Entry into force (supplemented by last signed) 02/15/2013 C06540387 cover memo

This raster layer contains the total area occupied by the built-up area of Buenos Aires, Argentina and its urbanized open space in 2000. Categories of urban land use represented in these data include: urban, suburban, rural and urbanized open land. The built-up area of the city is the area occupied by built-up pixels within the set of administrative boundaries defining the city. The urbanized open space consists of all fringe open spaces (including exterior open spaces) and all captured open spaces. These data are part of the Atlas of Urban Expansion.

In this study we investigate the landscape of a multifunctional peasant socio-ecosystem using a dialectic and complex theoretical-methodological framework rooted in agroecology. We describe vegetation floristics and structure, as well as biophysical and anthropic variables. We use maps, multivariate analysis and validation workshops to integrate and analyze information. The vegetation showcases distinct typologies, marked by gradual, non-linear variations, without precise boundaries tied to biophysical and anthropic drivers. Furthermore, based on vegetation structure and floristic heterogeneity, this agroecosystem constitutes a well-managed multifunctional native forest landscape, exemplifying a case of biocultural conservation.