611 Global export shipment records of Harvest,track with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Brazil Harvest Area: Oranges data was reported at 575.437 ha th in 2023. This records an increase from the previous number of 568.296 ha th for 2022. Brazil Harvest Area: Oranges data is updated yearly, averaging 727.572 ha th from Dec 1974 (Median) to 2023, with 50 observations. The data reached an all-time high of 1,027.079 ha th in 1999 and a record low of 349.591 ha th in 1974. Brazil Harvest Area: Oranges data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIA001: Agricultural Area.

Estimates of crop nutrient removal (as crop products and crop residues) are an important component of crop nutrient balances. Crop nutrient removal can be estimated through multiplication of the quantity of crop products or crop residues (removed) by the nutrient concentration of those crop products and crop residue components respectively. Data for quantities of crop products removed at a country level are available through FAOSTAT (https://www.fao.org/faostat/en/), but equivalent data for quantities of crop residues are not available at a global level. However, quantities of crop residues can be estimated if the relationship between quantity of crop residues and crop products is known. Harvest index (HI) provides one such indication of the relationship between quantity of crop products and crop residues. HI is the proportion of above-ground biomass as crop products and can be used to estimate quantity of crop residues based on quantity of crop products. Previously, meta-analyses or surveys have been performed to estimate nutrient concentrations of crop products and crop residues and harvest indices (collectively known as crop coefficients). The challenges for using these coefficients in global nutrient balances include the representativeness of world regions or countries. Moreover, it may be unclear which countries or crop types are actually represented in the analyses of data. In addition, units used among studies differ which makes comparisons challenging. To overcome these challenges, data from meta-analyses and surveys were collated in one dataset with standardised units and referrals to the original region and crop names used by the sources of data. Original region and crop names were converted into internationally recognised names, and crop coefficients were summarised into two Tiers of data, representing the world (Tier 1, with single coefficient values for the world) and specific regions or countries of the world (Tier 2, with single coefficient values for each country). This dataset will aid both global and regional analyses for crop nutrient balances.

Methods Data acquisition Data were primarily collated from meta-analyses found in scientific literature. Terms used in Ovid (https://ovidsp.ovid.com/), CAB Abstracts (https://www.cabdirect.org/) and Google Scholar (https://scholar.google.com/) were: (crop) AND (“nutrient concentration” OR “nutrient content” OR “harvest index”) across any time. This search resulted in over 245,000 results. These results were refined to include studies that purported to represent crop nutrient concentration and/or harvest index of crops for geographic regions of the world, as opposed to site-specific field experiments. Given the range in different crops grown globally, preference was given to acquiring datasets that included multiple crops. In some cases, authors of meta-analyses were asked for raw data to aid the standardisation process. In addition, the International Fertilizer Association (IFA), and the Food and Agriculture Organization of the United Nations (UN FAO) provided data used for crop nutrient balances (FAOSTAT 2020). The request to UN FAO yielded phosphorus and potassium crop nutrient concentrations in addition to their publicly available nitrogen concentration values (FAOSTAT 2020). In total the refined search resulted in 26 different sources of data. Data files were converted to separate comma-delimited CSV files for each source of data, whereby a unique ‘source’ was a dataset from an article from the scientific literature or a dataset sent by the UN FAO or IFA. Crop nutrient concentrations were expressed as a percentage of dry matter and/or the percentage of fresh weight depending on which units were reported and whether dry matter percentages of crop fresh weight were reported. Meta-data text files were written to accompany each standardized CSV file. The standardized CSV files for each source of data included information on the name of the original region, the crop coefficients it purported to represent, as well as the original names of the crops as categorised by the authors of the data. If the data related to a meta-analysis of multiple sources, information was included for the primary source of data when available. Data from the separate source files were collated into one file named ‘Combined_crop_data.csv’ using R Studio (version 4.1.0) (hereafter referred to as R) with the scripts available at https://github.com/ludemannc/Tier_1_2_crop_coefficients.git. Processing of data When transforming the combined data file (‘Combined_crop_data.csv’) into representative crop coefficients for different regions (available in ‘Tier_1_and_2_crop_coefficients.csv’), crop coefficients that were duplicates from the same primary source of data were excluded from processing. For instance, Zhang et al. (2021) referred to multiple primary sources of data, and the data requested from the UN FAO and the IFA referred (in many cases) to crop coefficients from IPNI (2014). Duplicate crop coefficient data that came from the same primary source were therefore excluded from the summarised dataset of crop coefficients. Two tiers of data The data were sub-divided into two Tiers to help overcome the challenge of using these data in a global nutrient balance when data are not available for every country. This follows the approach taken by the Intergovernmental Panel for Climate Change-IPCC (IPCC 2019). Data were assigned different ‘Tiers’ based on complexity and data requirements. · Tier 1: crop coefficients at the world level. · Tier 2: crop coefficients at more granular geographic regions of the world (e.g. at regional, country or sub-country levels).
Crop coefficients were summarised as means for each crop item and crop component based on either ‘Tier 1’ or ‘Tier 2’. One could also envision a more detailed site-specific level (Tier 3). The data in this dataset did not meet the required level of complexity or data requirements for Tier 3, unlike, say, the site-specific data being collected as part of the Consortium for Precision Crop Nutrition (CPCN) (www.cropnutrientdata.net)-which could be described as being Tier 3. No data from the current dataset were therefore assigned to Tier 3. It is expected that in the future, site-specific data will be used to improve the crop coefficients further with a Tier 3 approach. The ‘Tier_1_and_2_crop_coefficients.csv’ file includes mean crop coefficients for the Tier 1 data, and mean crop coefficients for the Tier 2 data. The Tier 1 estimates of crop coefficients were mean values across Tier 1 data that purported to represent the World. Crop coefficients found in the data sources represent quite different geographic areas or regions. To enable combining data with different spatial overlaps for Tier 2, data were disaggregated to the country level. First, each region was assigned a list of countries (which the regional averages were assumed to represent, as listed in the ‘Original_region_names_and_assigned_countries.csv’ file). Countries were assigned alpha-3 country codes following the ISO 3166 international standards (https://www.iso.org/publication/PUB500001.html). Second, for each country mean, crop coefficients were calculated based on coefficients from regions listed for each country. For Australia for example, the mean values for each crop coefficient were calculated from values that represented sub-country (e.g. Australia New South Wales South East), country (Australia), and multi-country (e.g. Oceania) regions. For instance, if there was a harvest index value of 0.5 for wheat for the original region ‘Australia New South Wales South East’, a value of 0.51 for the original region named ‘Australia’ and a value of 0.47 for the original region named ‘Oceania’, then the mean Tier 2 harvest index for wheat for the country Australia would be 0.493, the unweighted mean. Using our dataset, a user can assign different weights to each entry. To aid analysis, the names of the original categories of crop were converted into UN FAO crop ‘item’ categories, following UN FAO standards (FAOSTAT 2022) (available in the ‘Original_crop_names_in_each_item_category.csv’ file). These item categories were also assigned categorical numeric codes following UN FAO standards (FAOSTAT 2022). Data related to crop products (e.g. grain, beans, saleable tubers or fibre) were assigned the category “Crop_products” and crop residues (eg straw, stover) were assigned the category “Crop_residues”. Dry and fresh matter weights In some cases nutrient concentration values from the original sources were available on a dry matter or a fresh weight basis, but not both. Gaps in either the nutrient concentration on a dry matter or fresh weight basis were given imputed values. If the data source mentioned the dry matter percentage of the crop component then this was preferentially used to impute the other missing nutrient concentration data. If dry matter percentage information was not available for a particular crop item or crop component, missing data were imputed using the mean dry matter percentage values across all Tier 1 and Tier 2 data. Global means for the UN FAO Cropland Nutrient Budget. Data were also summarised as means for nitrogen (N), elemental phosphorus (P) and elemental potassium (K) nutrient concentrations of crop products using data that represented the world (Tier 1) for the 2023 UN FAO Cropland Nutrient Budget. These data are available in the file named World_crop_coefficients_for_UN_FAO.csv.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

2274 Global import shipment records of Harvest with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Inland recreational fisheries, found in lakes, rivers, and other landlocked waters, are important to livelihoods, nutrition, leisure, and other societal ecosystem services worldwide. Although recreationally-caught fish are frequently harvested and consumed by fishers, their contribution to food and nutrition has not been adequately quantified due to lack of data, poor monitoring, and under-reporting, especially in developing countries. Beyond limited global harvest estimates, few have explored species-specific harvest patterns, although this variability has implications for fisheries management and food security. Given the continued growth of the recreational fishery sector, understanding inland recreational fish harvest and consumption rates represents a critical knowledge gap. Based on a comprehensive literature search and expert knowledge review, we quantified multiple aspects of global inland recreational fisheries for 81 countries spanning 198 species. For each country, we as ...

China Industrial Production: Harvest Machine data was reported at 59,819.000 Unit in Oct 2015. This records a decrease from the previous number of 70,294.000 Unit for Sep 2015. China Industrial Production: Harvest Machine data is updated monthly, averaging 73,912.500 Unit from Jan 2008 (Median) to Oct 2015, with 90 observations. The data reached an all-time high of 151,819.000 Unit in Oct 2012 and a record low of 14,172.000 Unit in Feb 2008. China Industrial Production: Harvest Machine data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BA: Industrial Production.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors.

Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).

The World Bank Group works in every major area of development. We provide a wide array of financial products and technical assistance, and we help countries share and apply innovative knowledge and solutions to the challenges they face.

The Global Smart Harvest Market Size Was Worth USD 12.81 Billion in 2022 and Is Expected To Reach USD 31.84 Billion by 2030, CAGR of 12.06%.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This data set is the result of digitizing and georeferencing existing observations of crop planting and harvesting dates. Climate statistics (e.g., the average temperature at which planting occurs in each region) were derived by merging the crop calendar maps with monthly climatologies from CRU.

The gridded maps of planting dates, harvesting dates, etc., for 19 crops are available at two different resolutions (5 minute and 0.5 degree), and in two different formats (netCDF and ArcINFO ASCII). For each region where there is a crop calendar observation, investigators applied that observation to every grid cell in the region (i.e., a simple paint-by-number approach).

This dataset is described in the following publication:

Sacks, W.J., D. Deryng, J.A. Foley, and N. Ramankutty (2010). Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography 19, 607-620. DOI: 10.1111/j.1466-8238.2010.00551.x

If you download this dataset, please email the PI to let him know how our data are being used. He can keep you informed of any updates to the dataset.

Also see the MIRCA2000 data set [at http://www.geo.uni-frankfurt.de/ipg/ag/dl/forschung/MIRCA/index.html and http://webmap.ornl.gov/wcsdown/dataset.jsp?ds_id=10015] which contains much of the same crop calendar data as this data set, but in a different format.

A dataset from the World Bank with date-diverse updates to the number of agricultural machines on a per-country basis.

Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

Brazil Harvest Area: Bananas data was reported at 456.522 ha th in 2023. This records a decrease from the previous number of 458.489 ha th for 2022. Brazil Harvest Area: Bananas data is updated yearly, averaging 479.189 ha th from Dec 1974 (Median) to 2023, with 50 observations. The data reached an all-time high of 532.745 ha th in 1997 and a record low of 310.125 ha th in 1974. Brazil Harvest Area: Bananas data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIA001: Agricultural Area.

186 Global export shipment records of Harvest Track with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.