Global data on crop nutrient concentration and harvest indices

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.