About this dataset

Context

The dataset tabulates the population of Farmers Branch by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Farmers Branch. The dataset can be utilized to understand the population distribution of Farmers Branch by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Farmers Branch. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Farmers Branch.

Key observations

Largest age group (population): Male # 25-29 years (1,987) | Female # 25-29 years (2,479). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Farmers Branch population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Farmers Branch is shown in the following column.
• Population (Female): The female population in the Farmers Branch is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Farmers Branch for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmers Branch Population by Gender. You can refer the same here

This dataset consists of soil data for 64 field sites on paired farm sites, with 29 variables measured for soil texture and structural condition, aggregate stability, organic matter content, soil shear strength, fuel consumption, work rate, infiltration rate, water quality and hydrological condition (HOST) data. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

About this dataset

Context

The dataset tabulates the population of Farmer by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Farmer. The dataset can be utilized to understand the population distribution of Farmer by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Farmer. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Farmer.

Key observations

Largest age group (population): Male # 0-4 years (18) | Female # 5-9 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Farmer population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Farmer is shown in the following column.
• Population (Female): The female population in the Farmer is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Farmer for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmer Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the Farmer population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Farmer. The dataset can be utilized to understand the population distribution of Farmer by age. For example, using this dataset, we can identify the largest age group in Farmer.

Key observations

The largest age group in Farmer, SD was for the group of age Under 5 years years with a population of 22 (34.38%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Farmer, SD was the 15 to 19 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Farmer is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Farmer total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmer Population by Age. You can refer the same here

This dataset consists of ecology data from 16 paired field sites; each pair consisting of an organic and conventional farm. A multiscale sampling design was employed to assess the impact of (i) location-within-field (field margin vs. edge vs. centre), (ii) crop type (arable cereal vs. permanent pasture), (iii) farm management (organic vs. conventional) and (iv) landscape-scale management (landscapes that contained low or high fractions of organic land) on a wide range of taxa. Studied taxa include birds, insect pollinators (hoverflies, bumblebees and solitary bees), epigeal arthropods, aphids and their natural enemies, earthworms and plants. The study is part of the NERC Rural Economy and Land Use (RELU) programme. A move to organic farming can have significant effects on wildlife, soil and water quality, as well as changing the ways in which food is supplied, the economics of farm business and indeed the attitudes of farmers themselves. Two key questions were addressed in the SCALE project: what causes organic farms to be arranged in clusters at local, regional and national scales, rather than be spread more evenly throughout the landscape; and how do the ecological, hydrological, socio-economic and cultural impacts of organic farming vary due to neighbourhood effects at a variety of scales. The research was undertaken in 2006-2007 in two study sites: one in the English Midlands, and one in southern England. Both are sites in which organic farming has a 'strong' local presence, which we defined as 10 per cent or more organically managed land within a 10 km radius. Potential organic farms were identified through membership lists of organic farmers provided by two certification bodies (the Soil Association and the Organic Farmers and Growers). Most who were currently farming (i.e. their listing was not out of date) agreed to participate. Conventional farms were identified through telephone listings. Respondents' farms ranged in size from 40 to 3000 acres, with the majority farming between 100 and 1000 acres. Most were mixed crop-livestock farmers, with dairy most common in the southern site, and beef and/or sheep mixed with arable in the Midlands. In total, 48 farms were studied, of which 21 were organic farmers. No respondent had converted from organic to conventional production, whereas 17 had converted from conventional to organic farming. Twelve of the conventional farmers defined themselves as practicing low input agriculture. Farmer interview data from this study are available at the UK Data Archive under study number 6761. Soil data from agricultural land under differing crop and management regimes,are also available. Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

Automated in situ soil sensor network - the data set includes hourly and daily measurements of volumetric water content, soil temperature, and bulk electrical conductivity, collected at 42 monitoring locations and 5 depths (30, 60, 90, 120, and 150 cm) across Cook Agronomy Farm. Data collection was initiated in April 2007 and is ongoing. Description of data Tabular data CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data All spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. Quality Control The Flags folder consists of the files containing the quality control flags for the Cook Farm Sensor Dataset. The nomenclature for the files indicates flags for either temperature (T) or water content (VW) and sensor depths. For example: T_30 is for the temperature data at 30cm. depth VW_120 is for the Volumetric water content at 120 cm. depth Files starting with “missing” contain flags (“M”) for locations and dates (mm/dd/yyyy) with missing data (NA in original dataset). Files starting with “range” contain flags for locations and dates (mm/dd/yyyy) with values outside acceptable ranges: Soil moisture (0-0.6 m^3/m^3) flagged as “C” Soil temperature (<0 deg. C) flagged as “D” Files starting with the name “flats” contain flags (“D”) for locations, dates (mm/dd/yyyy), and times (hh:mm) with constant values (within 1%) for a 24 hour period, as in Dorigo et al. 2013. Files starting with the name “spikes” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden spikes in VWC readings. Files starting with the name “breaks” contain flags (“D”) for locations, dates (mm/dd/yyy), and times (hh:mm) with sudden breaks (jumps or drops) in VWC readings. Code (implemented in R) for the screening and flagging is included in “Code Snippet.txt” A list of the sensor versions as of 06/16/16 at each location and depth. Resources in this dataset:Resource Title: Data package for automated in situ soil sensor network. File Name: CAF_Sensor_Dataset.zipResource Description: Data file descriptions for Cook Farm sensor network data set (CAF_Sensor_Dataset). Data set compiled by Caley Gasch, under supervision of David Brown, Department of Crop and Soil Sciences, Washington State University, Pullman, WA. Updated: 04/01/2017 Tabular data: CAF_sensors: folder with Daily and Hourly subfolders, each containing 42 '.txt' files of water content and temperature sensor readings. Each file represents readings from a single location, indicated in the file name (i.e. CAF003.txt) and in the 'Location' field of the table. Readings are organized by 'Date' (4/20/2007 - 6/16/2016), ‘Time’ (24 hr clock, only in hourly files), and with property (VW or T) and sensor 'Depth' as follows: VW_30cm: volumetric water readings at 30 cm depth (m^3/m^3) VW_60cm: volumetric water readings at 60 cm depth (m^3/m^3) VW_90cm: volumetric water readings at 90 cm depth (m^3/m^3) VW_120cm: volumetric water readings at 120 cm depth (m^3/m^3) VW_150cm: volumetric water readings at 150 cm depth (m^3/m^3) T_30cm: temperature readings at 30 cm depth (C) T_60cm: temperature readings at 60 cm depth (C) T_90cm: temperature readings at 90 cm depth (C) T_120cm: temperature readings at 120 cm depth (C) T_150cm: temperature readings at 150 cm depth (C) Volumetric water content readings are calibrated according to: Gasch, CK, DJ Brown, ES Brooks, M Yourek, M Poggio, DR Cobos, CS Campbell. 2017. A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil specific correction. Computers and Electronics in Agriculture, 137: 29-40. Temperature readings are factory calibrated. CAF_BulkDensity.txt: file containing bulk density values ('BulkDensity' in g/cm^3) for sensor depths at each of the 42 instrumented locations at Cook Farm. Location is indicated in 'Location' field, and sample depths are defined (in cm) by the ’Depth’ field. CAF_CropID.txt: file containing crop codes for each sub-field (A, B and C) and strip (1-6 for A and B, 1-8 for C) at Cook Farm for 2007-2016. This is also part of the attribute table for 'CAF_strips.shp' CAF_CropCodes.txt: file containing crop code names and crop identities, used in 'CAF_CropID.txt' and 'CAF_strips.shp' CAF_ParticleSize.txt: file containing particle size fractions ('Sand', 'Silt', and 'Clay' as percent) for each 'Location' at sensor depths ('Depth', in cm). Spatial data: all spatial data have spatial reference NAD83, UTM11N CAF_sensors.shp: file containing locations of each of the 42 monitoring locations, the 'Location' field contains the location name, which coincides with locations in tabular files. CAF_strips.shp: file containing areal extents of each sub-field, stip, and crop identities for 2007-2016. Crop identity codes are listed in 'CAF_CropCodes.txt' CAF_DEM.tif: file containing a 10 x 10 m elevation (in m) grid for Cook Farm. CAF_Spring_ECa.tif, CAF_Fall_ECa.tif: files containing 10 x 10 m apparent electrical conductivity (dS/m) grids to 1.5 m depth for spring and fall at Cook Farm. CAF_Bt_30cm.tif, CAF_Bt_60cm.tif, CAF_Bt_90cm.tif, CAF_Bt_120cm.tif, CAF_Bt_150cm.tif: files containing 10 x 10 m predictive surfaces for probability (0-1) of Bt horizon at the five sensor depths. (Dataset updated on 10/23/2017 to include QC information.)

What? A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021. Why? Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders. How? Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. What is 'new' or corrected in version 2.2? Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. Added: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell. CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

This data collection results from abundance surveys of seven species of weeds in ca. 500 lowland arable fields in 49 farms over three years. Each field was divided into large grids of 20x20 metre cells, and the density of seven species was estimated three times a year. The study is part of the NERC Rural Economy and Land Use (RELU) programme. In the context of changing external and internal pressures on UK agriculture, particularly those associated with the ongoing reform of the EU Common Agricultural Policy, it is imperative to determine whether all of the various dimensions of sustainability - including the relevant economic and environmental objectives as well as social and cultural values - can be integrated successfully at the farm and landscape levels. Although the ways in which economic, technological, and regulatory changes are likely to affect the profitability and management of farms of varying size are reasonably well understood, there is not the knowledge or understanding to predict the resulting effects on biodiversity. For example, the effect of changes in arable farming practices on field weeds and, in turn, on habitats and food supply required to sustain farm birds is a case in point. This knowledge is critical, however, if we are to understand the ecological consequences of changes in agricultural policy. Furthermore, it is also important if we are to design and justify changes in farming methods that can not only enhance nature conservation, but do this is ways that are practical and appealing from a farmer's point of view. This understanding is essential if we are to achieve an agriculture that is sustainable in both economic and environmental terms and is widely perceived to have social and cultural value. A consistent theme in all components of this research project is to understand the behaviour (of farmers, weeds or birds) and then use this information to produce predictive models. Whilst there have been a number of models of economic behaviour, weed populations and bird populations - including many by the research team here - the really novel component of this research is to integrate these within one framework. Farmer interviews on economic attitudes and preferences associated with and importance of different land-use objectives to lowland arable farmers are available at the UK Data Archive under study number 6728 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This table provides the number of employees in the agriculture sector, and agricultural operations with at least one employee, by industry in Canada. A breakdown by full-time, part-time and seasonal employees is also available.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 1,776 observations with 37 variables; Profiles Layer Field = 1,493 observations with 64 variables; Profiles Layer Lab= 1,386 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The BENEFIT-REALISE program implements its interventions in 60 PSNP weredas in four regions (Tigray, Amhara, Oromia, and SNNPR).Accordingly, in 2019, BENEFIT-REALISE along with the MoA initiated a wereda-wide soil resource characterization and mapping task at1:50,000 scale in 15 BENEFIT-REALISE intervention weredas: 3 of Tigray, 6 of Amhara, 3 of Oromia, and 3 of SNNPR. Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. 10.13140/RG.2.2.31759.41123. Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen, 2020b.

TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

About this dataset

Context

The dataset tabulates the Farmer City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Farmer City. The dataset can be utilized to understand the population distribution of Farmer City by age. For example, using this dataset, we can identify the largest age group in Farmer City.

Key observations

The largest age group in Farmer City, IL was for the group of age 60-64 years with a population of 228 (12.06%), according to the 2021 American Community Survey. At the same time, the smallest age group in Farmer City, IL was the 40-44 years with a population of 29 (1.53%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Farmer City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Farmer City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Farmer City Population by Age. You can refer the same here

Abstract

The Census of agriculture is defined to be a government sponsored large-scale Island-wide operation for the collection and derivation of quantitative statistical information on the structure of the agriculture, using agricultural holding as the unit of enumeration and referring to a single agricultural year.

The Census of Agriculture and Livestock is a large scale undertaking designed to

         Collect and disaggregate statistical data at lower administrative division level needed for planning, 
         Establish benchmark data on the structure in order to evaluate the progress of agricultural sector
         Prepare a frame of agricultural holdings, agricultural households etc. for the purpose of conducting 
         sample surveys during the intercensal period.

The Census of Agriculture and Livestock conducted during the period from August – October 2002 is the latest in the series of Censuses. The extent of land operated for the purpose of agricultural crops and livestock have been enumerated in this Census. Such agricultural land were grouped into two categories viz. (a) Small Holdings (b) Estate or Large holdings

There were about 3.3 million holdings in the "Small Holdings sector" out of which 1.5 million was enumerated in the category of less than 40 perches in extent. The rest 1.8 million was found to be more than 40 perches or their produce is mainly devoted for sale purposes.

Geographic coverage

National Coverage Urban and Rural Separate enumaration for Estate Sector The extent of land operated for the purpose of agricultural crops and livestock have been enumerated in this Census. Such agricultural land were grouped in to two categories viz.

                           (a) Small Holdings
                           (b) Estate or Large holdings

Analysis unit

Individuals

Note: - Operator basis Data set: In this data set the operator does the agricultural work in the same District where he resides.

Agricultural Operator, Agricultural Holding

(1) Agricultural Operator

An agricultural operator is the person responsible for operating the agricultural land and /or livestock. He/She may carry out the agricultural operations by himself/herself or with the assistance of others or simply direct day-to-day operations. Here the Operator cultivates the land and/or tends the livestock himself. or He/she may do so with the assistance of hired labour or any other persons. or He/She may simply direct operations by taking decisions only.

It is important to note that the operator need not necessarily be the owner of the land or livestock and also that mere ownership does not entitle a person to be considered as an operator. This means that a person may attend to all the work needed to cultivate a land or tend livestock, but will not be considered the operator, if there is some one else directing day to day work on the holding. It also means that a person may supervise the work in a holding appearing for all purposes to be in charge of the operations of the holding, but if there is someone else who is giving day to day directions, he/she does not become the operator.

In respect of livestock, any person who is actually responsible for the management of livestock in the same way that a land operator is responsible for his holding will be considered as the operator. The livestock may be owned, obtained on "Ande" or lease or any other form of arrangement. While most livestock operators will also be land operators, there would be cases of livestock operators who are not land operators and therefore they may have no land holding. The term agricultural operator includes both land operator as well as purely livestock or poultry operator. While most of the operators have only one holding, there could be cases of an operator having more than one holding.

(2) Agricultural Holding An agricultural holding consists of all land and/or livestock used wholly or partly for agricultural production and is operated under one operational status and situated within one Divisional Secretariat. (D.S.) Division subject to the following conditions:

                One holding may consist of one or more parcels.
                Does not matter whether operator owns the land or not.
                Does not matter whether the land is operated legally or not.
                Holding may consist only crops, only livestock or crops and livestock.
                Does not matter whether the land is very marginal or big in size.
                Holding may consist only paddy, only highlands or paddy and highlands.

However, should any land is situated outside the D.S.division where the operator is resided, it could be considered as a separate agricultural holding taking into account of above conditions.

Universe

There were about 3.3 million holdings in the "Small Holdings sector" out of which 1.5 million was enumerated in the category of less than 40 perches in extent. The rest 1.8 million was found to be more than 40 perches or their produce is mainly devoted for sale purposes.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was published both in Sinhala / Tamil languages. Main sections were: Identification Information Agricultural Operator Agricultural Holding Extent under permanent crops Seasonal crops Agriculture Machinery/Equipment Livestock Other Information Land Utilization

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) Manual editing and coding b) During data entry (Range edits) c) Computer editing - Structural and consistency d) Secondary editing e) Imputations

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource. -To data entry and computer editing used IMPS software package developed by the US Bureau of the Census.

This dataset gathers information used for the paper Couthouis et al. "Ecosystem multifunctionality is promoted by organic farming and hedgerows at the local scale but not at the landscape scale", part of the BIOMHE project (2019-2022) funded by the Fondation de France. It contains data about (i) species richness and abundances used for calculating ecological performance, (ii) crop yield used for calculating agronomic performance, (iii) labour and semi-net margin used for calculating socio-economic performance. Combined, these information allows for the calculation of ecosystem multifunctionaliy indices. Methods We selected 40 winter cereal fields (20 fields under organic farming - OF and 20 under conventional farming - CF), distributed along two independent landscape gradients based on (i) the varying extent (%) of OF and (ii) the varying density (length) of hedgerows, in the Zone Atelier Armorique, northwestern France. Cereal fields were under monoculture of winter cereals such as wheat (Triticum spp.), triticale (Triticosecale spp.) or oat (Avena sativa L.), hereafter referred to as ‘Cereal crops’ (10 OF and 20 CF fields), or winter cereals intercropped with legumes, namely faba bean (Vicia faba L.) or pea (Pisum sativum L.), without distinct row arrangement, hereafter referred to as ‘Mixed crops’ (10 OF fields). Predatory arthropods (carabids; Coleoptera: Carabidae), spiders (Araneae), staphylinids (Coleoptera: Staphylinidae), ladybirds (Coleoptera: Coccinellidae) and insect pests (aphids; Hemiptera: Aphidoidea) were collected twice, in May and June 2020, using a vacuum method (D-vac), with a series of five aspirations performed at 10-m intervals along a 50 m-long transect located in each habitat (hedgerow and centre of the field).The D-vac aspirations were carried out through the vegetation and on the ground in order to capture the arthropods present on these two strata. Data from the series of aspirations (N = 5) and the sampling periods (N = 2) were pooled to obtain estimates of the total abundance of predatory arthropods and aphids per habitat type (hedgerow or crop field). Carabid beetles were identified at the species level following Roger et al. (2010). Visual counts of flower-visiting insects were performed three times in May, June and July 2020, by walking along 50 m-long transects (one per habitat) at a slow pace for 5 min. Flower-visiting insects were assigned to one of the following morpho-groups: honeybees, bumblebees (Bombus lapidarius, B. pasucorum, B. terrestris), solitary bees (< 1 cm and > 1 cm), hoverflies, butterflies, other Diptera, other Coleoptera, other Hymenoptera. Data were pooled over the three sampling periods to determine the abundance of flower-visiting insects per habitat. Single surveys of spontaneous vegetation were conducted in 10 quadrats (1 × 1 m) placed at 5 m intervals along each 50 m-long transect. Plant species were identified according to Flora Europaea (Tutin et al., 1993) and corresponding percentage cover was estimated visually. We distinguished troublesome weeds (i.e. species that potentially cause high yield losses or hinder harvesting operations) based on expert knowledge from personnel at the Chamber of Agriculture of Brittany, France). Species richness and abundances are gathered in the "DATA_habitat" sheet. We conducted interviews with farmers (OF fields = 16 farmers; CF fields = 17 farmers) to obtain data on soil preparation practices (from harvest of the preceding crop [n – 1] to sowing of the next crop [n + 1]), pest management (mechanical or chemical weeding, fungicide, insecticide and molluscicide use), fertilisation (mineral or organic), and harvested products (yield). The labour proxy (h.ha-1) corresponded to the cumulative duration of interventions in sampled fields, while the semi-net margin (€.ha-1) was calculated by subtracting operational expenses (seeds and inputs) and equipment (depreciation, maintenance and gasoline) from the market price of crops. The semi-net margin closely approximates the actual income of farmers. Crop yield, labour and semi-net margin are gathered in the "DATA_habitat" sheet. Land-cover maps of the landscape sites were digitised using aerial ortho-photographs (BD ORTHO IGN, 2017) and field surveys using Arcgis 10.8.1 (Environmental Systems Resource Institute; ESRI, 2020) in 1000 m radius circles centred on each sampled field. In addition, maps of faming systems (OF vs. CF) were created based on existing data obtained for the same study area (Puech et al., 2015) and updated based on information obtained from our interviews with farmers in the present study. From these maps, we calculated two metrics for landscape composition (the percentage cover of SNH [% SNH including woodlands, permanent grasslands, hedgerows and fallows] and the percentage cover of OF [% OF]); and one metric for landscape configuration: (hedgerow density [total hedgerow length). All metrics were calculated in 250 m, 500 m and 1000 m radius circles using Chloe software (Boussard et al., 2020). Landscape metrics are gathered in the "DATA_landscape" sheet.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2005 Shasta County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). DPLA was later reorganized into the Division of Statewide Integrated Water Management and the Division of Integrated Regional Water Management. The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters and Northern Region, under the supervision of Tito Cervantes. The finalized countywide land use vector data is in a single, polygon, shapefile format. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Shasta County conducted by DWR, Northern District Office staff(ND), currently known as Northern Region Office, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2005. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary date was developed using: 1. Linework developed for DWR’s 1995 survey of Shasta County was used as the starting point for the digital field boundaries developed for this survey. Where needed, Northern Region staff made corrections to the field boundaries using the 1993 Digital Orthophoto Quarter Quadrangle (DOQQ) images. After field visits had been completed, 2005 National Agricultural Imagery Program (NAIP), one-meter resolution imagery from the U.S. Department of Agriculture’s Farm Services Agency was used to locate boundary changes that had occurred since the 1993 imagery was taken. Field boundaries for this survey follow the actual borders of fields, not road center lines. Line work for the Redding area was downloaded from the City of Redding website and modified to be compatible with DWR land use categories and linework. 2. For field data collection, digital images and land use boundaries were copied onto laptop computers. The staff took these laptops into the field and virtually all agricultural fields were visited to positively identify agricultural land uses. Site visits occurred from July through September 2005. Using a standardized process, land use codes were digitized directly into the laptop computers using ArcMap. For most areas of urban land use, attributes were based upon aerial photo interpretation rather than fieldwork. 3. The digital land use map was reviewed using the 2005 NAIP four-band imagery and 2005 Landsat 5 images to identify fields that may have been misidentified. The survey data was also reviewed by summarizing land use categories and checking the results for unusual attributes or acreages. 4. After quality control procedures were completed, the data was finalized by staff in both ND and Sacramento's DPLA. Important Points about Using this Data Set: 1. The land use boundaries were drawn on-screen using orthorectified imagery. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries or meant to be used as parcel boundaries. 2. This survey was a "snapshot" in time. The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and whatever additional information the aerial photography might provide. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. Double cropping and mixed land use must be taken into account when calculating the acreage of each crop or other land use mapped in this survey. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. For double cropped fields, a “D” will be entered in the “MULTIUSE” field of the DBF file of the shapefile. To calculate the crop acreage for that field, 40 acres should be allocated to the grain category and then 40 acres should also be allocated to corn. For polygons mapped as “mixed land use”, an “M” will be entered in the “MULTIUSE” field. To calculate the appropriate acreages for each land use within this polygon, multiply the percent (as a decimal fraction) associated with each land use by the acres represented by the polygon. 4. All Land Use Codes are respresentative of the current 2016 Legend unless otherwise noted. Not all land use codes will be represented in the survey. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 9' x 9' color photos, is approximately 23 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

First Fall Frost (0 °C) is defined as the average day, during the second half of the year, of the first occurrence of a minimum temperature at or below 0 °C.

These values are calculated across Canada in 10x10 km cells.

The GRDC Hyper Yielding Crops (HYC) project was set up to challenge the current boundaries of productivity and profitability in the high yielding regions of southern Australia. To scale up the findings of the research, and to drive enthusiasm and adoption in the different HRZ regions the annual national HYC Awards was created.

This component of the project involved the whole HYC team collecting paddock samples, to issue agronomic benchmarking reports to participating growers. Over the four years of the project, more than 330 crops of wheat, and then in 2022 and 2023 barley were entered into the HYC Awards competition, with more than 40 winners announced, and award plaques presented.

Sharing/access information (D-3.1*)

Available to Third Parties under terms and conditions to be agreed by co-owners of the data

Data and file overview (D-3.2)

The data presented here is the 2022 and 2023 Hyper Yielding Crops Project award paddock data. The results cover yield, grain quality, yield component, soil, input and decision data from SA, TAS, VIC and WA.

Collated 2022 HYC Barley Dataset.xlsx

Collated 2022 HYC Wheat Dataset.xlsx

Collated 2023 HYC Barley Dataset.xlsx

Collated 2023 HYC Wheat Dataset.xlsx

Data files

The data is organised in each sheet by trial site ID, trial site location, state, year, crop type, and variety name. Specific information contained in datasets below:

Previous crop, sow date, harvest date, season length, yield (t/ha), harvest method, thousand seed weight (g), germination rate, plant population, sow rate (kg/ha), seeds per m2 sown, stubble management, fallow management, grazed start date, grazed end date, seeder type, seeder name, row spacing, seeder width, spreader width and sprayer width, ph cacl2 (0-10 cm), Colwell p (0-10 cm), Colwell k (0-10 cm), ECEC (0-10 cm), organic carbon (0-10 cm), APAL soil texture 0 10 (0-10 cm), other soil texture (0-10 cm), grain N, grain P, grain K, grain S, grain Cu, grain Zn, grain Mn, avg dry matter at harvest (t/ha), avg harvest index post-harvest (%), avg head count at harvest, avg grains per head post-harvest, avg grains per m2 at harvest, avg 1000 grain weight at harvest (g), avg protein post-harvest (%), avg test weight post-harvest (Kg/hl), avg screenings post-harvest (%), avg seeder row spacing at harvest (mm), fertiliser total (kg/ha), number of fertiliser applications, fertiliser application details, fungicide cost (per ha), number of fungicide applications, fungicide application details, herbicide cost (per ha), number of herbicide applications, herbicide application details and PGR application details, ASC soil order and APAL soil texture and reason for sowing [spring sown barley].

This repository includes:

The most recent version of the excel data sheet(s) that record original data and contains related calculations (eg. Ave dry matter at harvest from dry matter quadrant cut).

Methodological information (D-3.3)

Data was collected by HYC project officers in each region working with local growers. Recording of paddock information was conducted across three assessment visits during the course of the season.

Yield component data was calculated by FAR Australia using samples supplied by regional project officers.

Raw data has been summarised per plot into columns representing each assessment conducted exported from the HYC portal developed by CeRDI.

Assessment descriptions and units are summarised in the 'Read Me' tab contained in each file.

Environmental/experimental conditions (D-3.4)

Soil information ( including ASC soil order and APAL soil texture) are described for each location in each dataset.

The data spreadsheets are self-describing. The rows above the data typically contain, a brief description of the assessment made and any notes.

Acknowledgement

The research undertaken as part of these projects is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC, the authors would like to thank them for their continued support. FAR Australia gratefully acknowledges the support of all of its research and extension partners in the Hyper Yielding Crops project. These are CSIRO, the Department of Primary Industries and Regional Development (DPIRD) in WA, Brill Ag, Southern Farming Systems (SFS), Techcrop, the Centre for eResearch and Digital Innovation (CeRDI) at Federation University Australia, MacKillop Farm Management Group (MFMG), Riverine Plains Inc and Stirling to Coast Farmers.

This dataset contains Manitoba Agriculture soil survey point data at various scales ranging from highly detailed to broader reconnaissance level information. The intent of this file is to display addtional labels for some attributes of large map polygons. Soil is essential to human survival. We rely on it for the production of food, fibre, timber and energy crops. Together with climate, the soil determines which crops can be grown, where, and how much they will yield. In addition to supporting our agricultural needs, we rely on the soil to regulate the flow of rainwater and to act as a filter for drinking water. With such a tremendously important role, it is imperative that we manage our soils for their long-term productivity, sustainability and health. The first step in sustainable soil management is ensuring that the soil will support the land use activity. For example, only the better agricultural soils in Manitoba will support grain and vegetable production, while more marginal agricultural soils will support forage and pasture-based production. For this reason, agricultural development should only occur in areas where the soil resource will support the agricultural activity. The only way to do this is to understand the soil resource that is available. Soil survey information is the key to understanding the soil resource. Soil survey is an inventory of the properties of the soil (such as texture, internal drainage, parent material, depth to groundwater, topography, degree of erosion, stoniness, pH, and salinity) and their spatial distribution over a landscape. Soils are grouped into similar types and their boundaries are delineated on a map. Each soil type has a unique set of physical, chemical and mineralogical characteristics and has similar reactions to use and management. The information assembled in a soil survey can be used to predict or estimate the potentials and limitations of the soils’ behaviour under different uses. As such, soil surveys can be used to plan the development of new lands or to evaluate the conversion of land to new uses. Soil surveys also provide insight into the kind and intensity of land management that will be needed. The survey scale of soils data for Manitoba ranges from 1:5,000 to 1:126,720, as identified in the 'SCALE' column.This file contains soils data that has been collected at a survey intensity level of the first order. This includes data collected at a scale of 1:5,000. The survey objective at this scale is to collect high precision field scale data and it is mostly used in research plots and other highly intensive areas. It is also applicable to agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Profile descriptions and samples are collected for all soils. At least one soil inspection exists per delineation and the minimum size delineation is 0.25 acres. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:5,000 or 12.7 in/ mile.This file also contains soils data that has been collected in Manitoba at a survey intensity level of the second order. This includes data collected at a scale of 1:20,000. The survey objective at this scale is to collect field scale data and it is mostly used in agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Soil pits are generally about 200 metres apart and are dug along transects which are about 500 metres apart. This translates to about 32 inspections sites per section(640 acres). The soils in each delineation are identified by field observations and remotely sensed data. Boundaries are verified at closely spaced intervals. Profile descriptions are collected for all major named soils and 10 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in over 90% of delineations and the minimum size delineation is generally about 4 acres at 1:20,000. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:20,000 or 3.2 inch/ mile.This file also contains data that has been collected at the third order. This includes scales of 1:40,000 and 1:50,000.The survey objective at this scale is to collect field scale or regional data. If the topography is relatively uniform, appropriate interpretations include agriculture capability, engineering, recreation, potato/irrigation suitability, and productivity indices. Soil pits are generally dug adjacent to section perimeters. This translates to about 16 inspection sites per section(640 acres). Soil boundaries are plotted by observation and remote sensed data. Profile descriptions exist for all major named soils and 2 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in 60-80% of delineations and the minimum size delineation is generally in the 10 to 20acre range. The soil taxonomy is generally Series or Phases of Soil Series. The mapping scale is 1:40,000 or 2 inch/ mile; 1:50,000 or 1.5 inch/mile.This file also contains soils data that has been collected at a survey intensity level of the fourth order. This includes scales of 1:63,360, 1:100,000, 1:125,000, and 1:126,720.The survey objective is to collect provincial data and to provide general soil information about land management and land use. The number of soil pits dug averaged to about 6 inspections per section (640 acres). Soil boundaries are plotted by interpretation of remotely sensed data and few inspections exist. Profile descriptions are collected for all major named soils. At least one soil inspection exists in 30-60% of delineations and the minimum size delineation is 40 acres (1:63,360), 100 acres (1:100,000), 156 acres (126,700) and 623 acres (250,000). The soil taxonomy is generally phases of Subgroup or Association. As of 2022, soil survey field work and reports are still currently being collected in certain areas where detailed information does not exist. This file will be updated as more information becomes available. Typically, this is conducted on an rural municipality basis.In some areas of Manitoba, more detailed and historical information exists than what is contained in this file. However, at this time, some of this information is only available in a hard copy format. This file will be updated as more of this information is transferred into a GIS format.This file has an organizational framework similar to the original SoilAID digital files and a portion of this geographic extent was originally available on the Manitoba Land Initiative (MLI) website.Domains and coded values have also been integrated into the geodatabase files. This allows the user to view attribute information in either an abbreviated or a more descriptive manner. Choosing to display the description of the coded values allows the user to view the expanded information associated with the attribute value (reducing the need to constantly refer to the descriptions within the metadata). To change these settings in ArcCatalog, go to Customize --> ArcCatalog Options --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'.To change these settings in ArcMap, go to Customize --> ArcMapOptions --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'. This setting can also be changed by opening the attribute table, then Table Options (top left) --> Appearance --> check or uncheck 'Display coded value domain and subtype descriptions'. The file also contains field aliases, which can also be turned on or off under Table Options.This same capability is available in ArcGIS Pro.For more info:https://www.gov.mb.ca/agriculture/soil/soil-survey/importance-of-soil-survey-mb.html#

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 1,659 observations with 37 variables; Profiles Layer Field = 2,373 observations with 64 variables; Profiles Layer Lab= 2,373 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template , adapted from Batjes 2022; Leenaars et al, 2014, from the below source: Ministry of Agriculture (MOA) Sustainable Land Management (SLM) program watershed-based soil profile data. Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. https://hdl.handle.net/10568/110868 Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020.

Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014.

TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission and is a generalized landcover database designed for Regional Planning with a landuse component used for forecasts and modeling at ARC. LandPro2010 should not be taken out of its Regional context, though county-level or municipal-level analysis may be useful for transportation, environmental and landuse planning. LandPro2010 is ARC's landuse/landcover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2010 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography. The primary source for this GIS database were the local parcels and the 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc. 2010 is the first year we have used parcel data to help more accurately delineate the LandPro categories.For ArcGIS 10 users: See full metadata by enabling FGDC metadata in ArcCatalog Customize > ArcCatalog Options > Metadata (tab)Though the terms are often used interchangeably, landuse and landcover are not synonymous. Landcover generally refers to the natural or cultivated vegetation, rock, or water covering the land, as well as the developed surface which can be identified on aerial photography. Landuse generally refers to the way that humans use or will use the land, regardless of its apparent landcover. Collateral data for the landcover mapping effort included the Aero Surveys of Georgia street atlas, the Georgia Department of Community Affairs (DCA) Community Facilities database and the USGS Digital Raster Graphics (DRGs) of 1:24,000 scale topographic maps. The landuse component of this database was added after the landcover interpretation was completed, and is based primarily on ownership information provided by the 21 counties and the City of Atlanta for larger tracts of undeveloped land that meet the landuse definition of "Extensive Institutional" or "Park Lands" (refer to the Code Descriptions and Discussion section below). Although some of the boundaries of these tracts may align with visible features from the aerial photography, these areas are generally "non-photo-identifiable," thus require other sources for accurate identification. The landuse/cover classification system is adapted from the USGS (Anderson) classification system, incorporating a mix of level I, II and III classes. There are a total of 25 categories in ARC's landuse/cover system (described below), 2 of which are used only for landuse designations: Park Lands (Code 175) and Extensive Institutional (Code 125). The other 23 categories can describe landuse and/or landcover, and in most cases will be the same. The LU code will differ from the LC code only where the Park Lands (Code 175) and Extensive Institutional (Code 125) land holdings have been identified from collateral sources of land ownership.Although similar to previous eras of ARC landuse/cover databases developed before 1999 (1995, 1990 etc.), "LandPro" differs in many significant ways. Originally, ARC's landuse and landcover database was built from 1975 data compiled by USGS at scales of 1:100,000 and selectively, 1:24,000. The coverage was updated in 1990 using SPOT satellite imagery and low-altitude aerial photography and again in 1995 using 1:24,000 scale panchromatic aerial photography. Unlike these previous 5-year updates, the 1999, 2001, 2003, 2005 2007, 2008 and 2009 LandPro databases were compiled at a larger scale (1:14,000) and do not directly reflect pre-1999 delineations. In addition, all components of LandPro were produced using digital orthophotos for on-screen photo-interpretation and digitizing, thus eliminating the use of unrectified photography and the need for data transfer and board digitizing. As a result, the positional accuracy of LandPro is much higher than in previous eras. There have also been some changes to the classification system prior to 1999. Previously, three categories of Forest (41-deciduous, 42-coniferous, and 43-mixed forest) were used; this version does not distinguish between coniferous and deciduous forest, thus Code 40 is used to simply designate Forest. Likewise, two categories of Wetlands (61-forested wetland, and 62-non-forested wetland) were used before; this version does not distinguish between forested and non-forested wetlands, thus Code 60 is used to simply designate Wetlands. With regard to Wetlands, the boundaries themselves are now based on the National Wetlands Inventory (NWI) delineations along with the CIR imagery. Furthermore, Code 51 has been renamed "Rivers" from "Streams and Canals" and represents the Chattahoochee and Etowah Rivers which have been identified in the landuse/cover database. In addition to these changes, Code 52 has been dropped from the system as there are no known instances of naturally occurring lakes in the Region. Finally, the landuse code for Park Lands has been changed from 173 to 175 so as to minimize confusion with the Parks landcover code, 173. There has been a change in the agriculture classification for LandPro2005 and any LandPro datasets hereafter. Previously, four categories of agriculture (21- agriculture-cropland and pasture, 22 - agriculture - orchards, 23 - agriculture - confined feeding operations and 24 - agriculture - other) were used; this version does not distinguish between the different agricultural lands. Code 20 is now used to designate agriculture. Due to new technology and the enhancements to this database, direct comparison between LandPro99, LandPro2001, LandPro2003 and landPro2005 and all successive updates are now possible, with the 1999 database serving as ARC's new baseline. Please note that as a result of the 2003 mapping effort, LandPro2001 has been adjusted for better comparison to LandPro2003 and is named "LandPro01_adj." Likewise, LandPro99 was previously adjusted when LandPro2001 was completed, but was not further adjusted following the 2003 update. Although some adjustments were originally made to the 1995 landuse/cover database for modeling applications, direct comparisons to previous versions of ARC landuse/cover before 1999 should be avoided in most cases.The 2010 update has gone away from using the (1:14,000) scale, as will any future updates. Do to the use of local parcels, we have begun to snap LandPro boundaries to the parcel data, making a more accurate dataset. The major change in this update was to make residential areas reflect modern zoning codes more closely. Do to these changes you will no longer be able to compare this dataset to previous years. High density (113) has change from lots below .25 to lots .25 and smaller. Medium density (112) has changed from .25 to 2 acre lots, to .26 to 1 acre lots. Low density has changed from 2 to 5 acre lots to 1.1 to 2 acre lots. It must be noted that in the 2010 update, you still have old acreage standards reflected in the low density. This will be corrected in the 2011 and 2012 updates. The main focus of the 2010 update was to make sure the LandPro' residential areas reflected the local parcels and change LandPro based on the parcel acreage. DeKalb is the only county not corrected at this time because no parcels were available. The future updates will consist of but are not limited to, reclassifying areas in 111 that do not meet the new acreage standards, delineating and reclassifying Cell Towers, substations and transmission lines/power cuts from TCU (14) to a subset of this (142), reclassifying airports as 141 form TCU, and reclassifying landfills form urban other (17) to 174. Other changes are delineating more roads other than just Limited Access Highways, making sure parks match the already existing Landuse parks layer, and beginning to differentiate office from commercial and commercial/industrial.

Classification System:

111: Low Density Single Family Residential - Houses on 1.1 - 2 acre lots. Though 2010 still reflects the old standard of lots up to 5 acres.

112: Medium Density Single Family Residential - These areas usually occur in urban or suburban zones and are generally characterized by houses on .26 to 1 acre lots. This category accounts for the majority of residential landuse in the Region and includes a wide variety of neighborhood types.

113: High Density Residential - Areas that have predominantly been developed for concentrated single family residential use. These areas occur almost exclusively in urban neighborhoods with streets on a grid network, and are characterized by houses on lots .25 acre or smaller but may also include mixed residential areas with duplexes and small apartment buildings.

117: Multifamily Residential - Residential areas comprised predominantly of apartment, condominium and townhouse complexes where net density generally exceeds eight units per acre. Typical apartment buildings are relatively easy to identify, but some high rise structures may be interpreted as, or combined with, office buildings, though many of these dwellings were identified and delineated in downtown and midtown for the first time with the 2003 update. Likewise, some smaller apartments and townhouses may be interpreted as, or combined with, medium- or high-density single family residential. Housing on military bases, campuses, resorts, agricultural properties and construction work sites is not included in this or other residential categories.

119: Mobile Home Parks - Areas that have been developed for single family mobile home use. These residential areas may occur in urban, suburban, or rural

This service displays polygons for current land use records and features used to inventory land use patterns. Data is updated, maintained and published from the enterprise GIS database to reflect the most recent information for the City of Las Cruces. Data is organized by activity, structure, or function. Layer Type: PolygonData Owner: Community DevelopmentAuthoritative: YesDownloadable: N/AInitial Dataset Creation: UnknownLast update: 2018 Update Frequency: As necessary Status: CurrentReason for Updates: Classify and inventory land use patternsSource data: N/AReference Source: Land Based Classification Standards (LBCS)Projected Coordinate System: N/AReference information: The classification is a snapshot at one particular time. Uses, businesses, and new construction occur on a daily basis. Also, there may be several parcels that make up a particular site. The classification used was the predominant use of that parcel (e.g., a residential condo plat has a parcel for common area that is mostly parking and parcels for each residential unit, the common area parcel was classed as parking and the parcels for the units as residential). It is important to note that parcel information will change if not updated. The Land-Based Classification System (LBCS) is the industry standard for classification developed by the American Planning Association. It is not an ideal system in that classification codes for certain dimensions do not exist, multiple classes may fit for any one dimension, and a level of subjectivity occurs during classification. LBCS consists of five major categories called “dimensions”: Web site can be found at: https://www.planning.org/lbcs/Five Dimensions for Classifying Land-Use DataActivity1000: Residential activities2000: Shopping, business, or trade activities3000: Industrial, manufacturing, and waste-related activities4000: Social, institutional, or infrastructure-related activities5000: Travel or movement activities6000: Mass assembly of people7000: Leisure activities8000: Natural resources-related activities9000: No human activity or unclassifiable activityActivity refers to the actual use of land based on its observable characteristics. It describes what actually takes place in physical or observable terms (e.g., farming, shopping, manufacturing, vehicular movement, etc.). An office activity, for example, refers only to the physical activity on the premises, which could apply equally to a law firm, a nonprofit institution, a court house, a corporate office, or any other office use. Similarly, residential uses in single-family dwellings, multi-family structures, manufactured houses, or any other type of building, would all be classified as residential activity.Activity Note:The five fields of Activ_20, Activ_40, Activ_60, Activ_80, and Activ_100 were used to identify different actual uses noted on a parcel. The intent was when multiple activity classes exist to determine visually the area taken up by each use (e.g., a parcel has a restaurant and an office, the office takes up 60% of the building to class the restaurant under Activ_40 and the office under Activ_60). This worked for some parcels, but many parcels had more than five possible classes or determination of square footage was difficult to determine because floor plan-site plan information was not readily available. Some pointers on using the activity field include:>On parcels having multiple activity classes an overall activity class was put under Activ_100 in order to extract data more readily. The ‘12’ class represents mixed use. Mixed use for this inventory meant a residential use existed on the same parcel with a non-residential use. It does not assess non-residential mixed use or the type of mixed use (e.g., vertical in same building or different uses in different locations on same parcel). The Activ_100 class for multiple activities used was the highest percentage class by area, except for undeveloped (9990) where the highest percentage class by area was used if 9990 area appeared to be less than 50% of the parcel area. >For contractor yards the 2013 Inventory used either 3000, Industrial-Manufacturing, as a catch-all if the activity was not very clear. It used 3300, Construction Activities, for activities related to construction contractors which is different than the APA Classification. 3300 in the APA Classification is actually describing the stage the parcel would be in physical construction.Function1000: Residence or accommodation functions2000: General sales or services3000: Manufacturing and wholesale trade4000: Transportation, communication, information, and utilities5000: Arts, entertainment, and recreation6000: Education, public admin., health care, and other inst.7000: Construction-related businesses8000: Mining and extraction establishments9000: Agriculture, forestry, fishing and huntingFunction refers to the economic function or type of enterprise using the land. Every land use can be characterized by the type of enterprise it serves. Land-use terms, such as agricultural, commercial, industrial, relate to enterprises. The type of economic function served by the land use gets classified in this dimension; it is independent of actual activity on the land. Enterprises can have a variety of activities on their premises, yet serve a single function. For example, two parcels are said to be in the same functional category if they belong to the same enterprise, even if one is an office building and the other is a factory.Function Note:The five fields of Function, Funct_40, Funct_60, Funct_80, and Funct_100 were used to identify different economic types noted on a parcel. The intent was to indicate the percentage of the building on the parcel related to that function. The function field chosen mimics the activity field in most cases. Unlike Active_100, an overall function class was not put under Funct_100 on parcels with multiple functions. Structural Character1000: Residential buildings2000: Commercial buildings and other specialized structures3000: Public assembly structures4000: Institutional or community facilities5000: Transportation-related facilities6000: Utility and other non-building structures7000: Specialized military structures8000: Sheds, farm buildings, or agricultural facilities9000: No structureStructural character refers to the type of structure or building on the land. Land-use terms embody a structural or building characteristic, which suggests the utility of the space (in a building) or land (when there is no building). Land-use terms, such as single-family house, office building, warehouse, hospital building, or highway, also describe structural characteristic. Although many activities and functions are closely associated with certain structures, it is not always so. Many buildings are often adapted for uses other than its original use. For instance, a single-family residential structure may be used as an office.Structural Note:The predominant structural type class was selected when multiple structures existed on a parcel. Some pointers on using the structural field include:>1130, Accessory Units, in the APA Classification is for secondary units. The 2013 Inventory used this class to identify accessory structures like sheds, etc. Secondary units on the same parcel are noted in the Units field.>1140, townhouse, and 1121, duplex, were sometimes used interchangeably. Townhouse for the APA classification is three or more attached dwelling units. Efforts were made to correct errors, but several likely were not caught. >1150, manufactured home, should be fairly accurate. NM does allow a double-wide manufactured home set on a foundation in a single-family zone. Several instances in the 2008 inventory classed this as 1100 or 1110, single-family site built unit. Efforts were made to class these as 1150 in the 2013 inventory. >1350, Temporary Structures, was used for RV Parks that appear to be more transitory. Otherwise, 1150, Manufactured Home, was used. Site Development Character1000: Site in natural state2000: Developing site3000: Developed site -- crops, grazing, forestry, etc.4000: Developed site -- no buildings and no structures5000: Developed site -- non-building structures6000: Developed site -- with buildings7000: Developed site -- with parks8000: Not applicable to this dimension9000: Unclassifiable site development characterSite development character refers to the overall physical development character of the land. It describes "what is on the land" in general physical terms. For most land uses, it is simply expressed in terms of whether the site is developed or not. But not all sites without observable development can be treated as undeveloped. Land uses, such as parks and open spaces, which often have a complex mix of activities, functions, and structures on them, need categories independent of other dimensions. This dimension uses categories that describe the overall site development characteristics.Site Note:All efforts were made to follow the site classification. Some pointers on using the site field include:>2000, Developing Site, was used if the site was under construction. The entire Metro Verde South Phase 1C plat was used for this class. A lot of home building activity was occurring in this area, but many lots were not under construction at time of site check. Ownership1000: No constraints--private ownership2000: Some constraints--easements or other use restrictions3000: Limited restrictions--leased and other tenancy restrictions4000: Public restrictions--local, state, and federal ownership5000: Other public use restrictions--regional, special districts, etc.6000: Nonprofit ownership restrictions7000: Joint ownership character--public entities8000: Joint ownership character--public, private, nonprofit, etc.9000: Not applicable to this dimensionOwnership refers to the relationship between the use and its land rights. Since the function of most land uses is either public or