Grain legume crops are a significant component of the human diet and animal feed and have an important role in the environment, but the global diversity of agricultural legume species is currently underexploited. Experimental assessments of grain legume performances are required, to identify potential species with high yields. Here, we introduce a dataset including results of field experiments published in 173 articles. The selected experiments were carried out over five continents on 39 grain legume species. The dataset includes measurements of grain yield, aerial biomass, crop nitrogen content, residual soil nitrogen content and water use. When available, yields for cereals and oilseeds grown after grain legumes in the crop sequence are also included. The dataset is arranged into a relational database with nine structured tables and 198 standardized attributes. Tillage, fertilization, pest and irrigation management are systematically recorded for each of the 8,581 crop*field site*growing season*treatment combinations. The dataset is freely reusable and easy to update. We anticipate that it will provide valuable information for assessing grain legume production worldwide.
Machine-accessible metadata file describing the reported data (ISA-tab format)
Background & Summary
The 68th United Nations General Assembly has proclaimed 2016 as the International Year of Pulses. The Food and Agriculture Organization of the United Nations defines ‘pulses’ as plant species from the Fabaceae family cropped annually, and harvested only for dry grain (hereafter ‘grain legume’ for unambiguous use1). As part of this initiative, grain legumes are being promoted for use as nutritional protein-rich grains, and for their environmental and economic impacts2,
Fabaceae is one of the largest families of plants worldwide, with 20,000 species growing across a wide range of climatic conditions and soil types20,21. Grain legume crops play significant roles in the human diet and animal feed and the environment, but only a fraction of the species in this diverse group of plants is currently exploited in agriculture. From 1961 to 2014, 75 and 90% of the area under legumes was allocated to soybean (Glycine max) in South America and North America, respectively22. Over the same period, 70, 76 and 78% of the area under legumes was covered by only three species each in Europe, Oceania and Africa: garden pea (Pisum sativum), soybean and beans (Phaseolus spp. and Vigna spp.) in Europe; lupins (Lupinus spp.), chickpea (Cicer arietinum) and garden pea in Oceania, and groundnut (Arachis hypogaea), cowpea (Vigna unguiculata) and beans in Africa22. In Asia, 76% of the area under legumes was allocated to four species (i.e., soybean, beans, groundnut and chickpea)22.
Experimental comparisons of grain legumes can help researchers and decision-makers to identify high-performance species with high yields. Over the last 50 years, many field experiments have assessed the agronomic and environmental performances of grain legumes. These performances vary between field sites and growing seasons, as a function of the climatic conditions and soil types. It would therefore be misleading to draw general conclusions from individual experiments considered separately. A global dataset would provide us with a unique opportunity to analyze variability in grain legume performances across a large spectrum of environmental conditions, and to rank legume species of agricultural and economic interest according to several criteria.
We introduce here a global dataset including the results of field experiments comparing 39 grain legume species grown as sole crops. Most of grain legume species included in the database correspond to species of significant agricultural and economic importance. We have selected only experiments comparing at least two grain legume species grown at the same field site during the same growing season, to prevent any confusion between species characteristics and environmental conditions. We excluded experiments on single grain legume species because, in such experiments, differences between species can be confounded with the effects of environmental factors. Experimental data were extracted from 173 published articles2,
The dataset can be used for two types of quantitative analysis. First, the dataset can be used to compare the crop production of a broad range of grain legume species, on the basis of experimental data with diverse criteria (e.g., grain yield, aerial biomass and crop nitrogen content). Second, the dataset can be used to assess the crop production of cereal and oilseed species following grain legume species cultivated as preceding crops in the same crop sequences, based on a consideration of field data for various criteria. The dataset is freely available to facilitate such analyses. It could easily be updated in the future, by adding the results of new experiments not originally included in the dataset. It might also be interesting to expand the dataset to include legumes grown for purposes other than grain production (e.g., forage production) or legumes grown in intercropping systems. The global dataset should prove to be a useful support for experimental assessments of the agronomic and environmental performances of a large diversity of grain legumes.
We carried out a systematic search of peer-reviewed journals for articles comparing grain legume yields. We defined a grain legume species as a plant from the Fabaceae family, based on the United States Department of Agriculture Plants Database (http://plants.usda.gov/java/), and cropped for grain production. The literature search was completed on February 15, 2016. The equation search was: ‘crop* AND (legum* OR pulse*) AND (yield* OR ‘dry matter’ OR biomass) AND (compar* OR assessment OR product* OR performance*) AND (trial* OR factorial OR experiment* OR treatment* OR condition*) NOT (intercrop* OR catch OR cover OR ‘green manure’ OR forage OR fodder)’. The search terms were used to query the Institute for Scientific Information Web of Science (http://wokinfo.com/), with no restrictions concerning the date and language of publication in the article title, abstract and author keywords.
The initial literature search identified 8,386 articles as of potential interest (Fig. 2). Each article title and article abstract were screened for eligibility according to six criteria: (1) article title and/or article abstract reporting one or several annual grain legume species grown as sole crops, (2) article title and/or article abstract reporting at least two grain legume species grown at the same field site during the same growing season, (3) article title and/or article abstract reporting at least one experiment conducted during one or several growing seasons, from the seeding stage to the harvest stage, (4) article title and article abstract referring to an article published in a peer-reviewed journal, (5) article title or article abstract written in English and (6) full-text article available. We selected 223 eligible full-text articles that met these first six criteria (Fig. 2).
Eligible full-text articles were then examined according to three additional criteria: (7) full-text article reporting raw data not duplicated in other articles or raw data that could be obtained by contacting authors, (8) full-text article reporting individual grain yield for each species and (9) full-text article reporting one or several experiments for which field site location or soil characteristics were precisely stated. We selected 60 full-text articles that met all nine criteria. This search was supplemented by screening the references cited in these 60 full-text articles. We also screened the references included in one meta-analysis about drought effects on food legume production187 for eligibility. When reviewing the full-text articles identified from references screening, all nine selection criteria defined above had to be met for the new article to be considered eligible. Note that, according to the criterion (2), experiments reporting data for single grain legume species were excluded. This selection criterion was used to ensure the direct comparability of different grain legume species, and avoid confounding effects between species characteristics and environmental factors. Experiments testing single species cannot be used to compare several species due to the effects of field site and growing season characteristics (e.g., climate conditions, soil types and plant diseases) on the growth and development of grain legumes.
We finally selected 173 full-text articles2,
All data are recorded in a relational database (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f). The Structured Query Language (SQL) system is used to query and maintain the database. We used the open-access application Sequel Pro version 1.0.2 (http://www.sequelpro.com/). The data collected are grouped into nine related tables including 198 standardized attributes of five types: class, numerical, index, binary and date (Fig. 3 and Table 2). Within the database, the tables are organized according to a cascade path: each ‘child’ table is related to a ‘mother’ table. For instance, the ‘Article’ table is the ‘mother’ table for the ‘child’ ‘Site’ table (Fig. 3). The cascade path from each ‘mother’ table to each ‘child’ table is structured by a ‘primary key’ and a ‘secondary key’ (Fig. 3). A ‘primary key’ assigns an index to each row of the table, whether the table is a ‘mother’ table or a ‘child’ table. A ‘secondary key’ assigns the ‘primary key’ of a ‘mother’ table to each row of a ‘child’ table. The cardinality from each ‘mother’ table to each ‘child’ table is based on ‘one-to-one’ and ‘one-to-many’ relationships (Fig. 3).
The database is structured into nine separate but related tables, stored as CSV-formatted files (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f). Tables are related to each other via primary and secondary keys, as explained in Fig. 3. The names, types and definitions of attributes included in the nine tables are listed in Table 3 (available online only).
The ‘Literature_Search’ table describes each step in the literature search at which each original article was selected (e.g., selection from the initial literature search or from references screening). The corresponding file is entitled ‘Literature_Search.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 2 columns and 3 rows (including the row header for the names of attributes).
The ‘Article’ table describes the references of the 173 selected articles (e.g., the name of the first author and the name of the journal). The corresponding file is entitled ‘Article.csv’ file (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 8 columns and 174 rows (including the row header for the names of attributes).
The ‘Site’ table describes the characteristics of each field site considered in each article (e.g., latitude and longitude coordinates, soil texture, precipitation and temperature). The corresponding file is entitled ‘Site.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 29 columns and 361 rows (including the row header for the names of attributes).
The ‘Crop_Sequence_Trt’ table describes each combination of crop sequences and management practices into the treatments studied at each field site (e.g., names of the species and their order in each crop sequence). The corresponding file is entitled ‘Crop_Sequence_Trt.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 8 columns and 4,560 rows (including the row header for the names of attributes).
The ‘Crop’ table provides information about each crop (e.g., names of the species, seeding and harvest dates, number of replicates, grain yield, aerial biomass, crop nitrogen content, residual soil nitrogen content, water use, error terms and error types). The main attributes included in this central table are described below in the Data Records section. The corresponding file is entitled ‘Crop.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 106 columns and 8,582 rows (including the row header for the names of attributes).
The ‘Tillage’ table describes tillage management for each crop (e.g., tillage tools, incorporation of preceding crop residues, seeding density and legume inoculation). The corresponding file is entitled ‘Tillage.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 19 columns and 8,582 rows (including the row header for the names of attributes).
The ‘Fertilization’ table describes nitrogen, phosphate and potassium fertilizer management for each crop (e.g., names and doses of fertilizers). Only the total fertilizer dose is reported for each type of nutrient. The corresponding file is entitled ‘Fertilization.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 7 columns and 25,744 rows (including the row header for the names of attributes).
The ‘Weed_Insect_Fungi’ table describes weeds, insects, and fungi management for each crop (e.g., mechanical treatment, names and doses of pesticides). The corresponding file is entitled ‘Weed_Insect_Fungi.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 13 columns and 45,002 rows (including the row header for the names of attributes).
The ‘Irrigation’ table describes irrigation management for each crop (e.g., quantity of water applied and irrigation method). The corresponding file is entitled ‘Irrigation.csv’ (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), and includes 6 columns and 8,582 rows (including the row header for the names of attributes).
In addition to the nine CSV-formatted files (tables), downloadable from Dryad Digital Repository (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f), the entire content of the database is also stored in a SQL-formatted file. The corresponding file is entitled ‘Database.sql’, and is also downloadable from Dryad Digital Repository (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f). Examples of SQL queries for extracting data for each table are stored in a TXT-formatted file. The corresponding file is entitled ‘Examples_SQL_Queries.txt’, and is also downloadable from Dryad Digital Repository (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f).
The names, types, and definitions of the 198 attributes included in the nine tables are reported in Table 3 (available online only).
The values (including error terms) and dates reported in graphics were digitized manually with the open-access application WebPlotDigitizer (http://arohatgi.info/WebPlotDigitizer/). The maximum error was estimated at 5.0% for the digitization of low-resolution images, generally from articles published before 1990. ‘NA’ indicates that data were ‘Not Available’ for the cell concerned. ‘NULL’ indicates a logical absence of data for attributes included in the ‘Crop’, ‘Tillage’, ‘Fertilization’, ‘Weed_Insect_Fungi’, and ‘Irrigation’ tables. For example, for the ‘Fertilization’ table, if no nitrogen fertilizer was applied to the crop (i.e., ‘0.00’ was reported in the ‘Fertilization_NPK_Dose’ attribute), then ‘NULL’ was reported for the ‘Fertilization_NPK_Dose_Product_Name’ attribute.
We describe below the main attributes of the ‘Crop’ table because this table includes most of the experimental data extracted from the 173 selected articles. Information on other attributes (e.g., articles, field sites, combinations of crop sequences and management practices) is defined in Table 3 (available online only).
In the ‘Crop’ table, grain yield is by far the attribute including the highest number of data. This high reporting rate reflects the explicit requirement for presence of grain yield data during the article selection process (i.e., criterion 8). Reporting rates are lower for aerial biomass, grain nitrogen content, aerial nitrogen content, fixed aerial nitrogen content, residual soil nitrogen content and water use. Table 1 presents the total number (percentage) of available and missing data for these attributes over all crop*field site*growing season*treatment combinations.
When data were not reported for some attributes (e.g., aerial biomass or water use) in the selected articles, we systematically collected data for related attributes (e.g., harvest index or grain water use efficiency) in order to retrieve the missing data. For examples, aerial biomass can be deduced from grain yield and harvest index, and water use can be deduced from grain yield and grain water use efficiency. When data were not available for any related attributes, we contacted the authors of the selected articles, and we asked them to provide us with additional raw data when available.
The name of each combination of crop sequences and management practices was based on the common names of the species, such as for both ‘Crop_Sequence_Trt_Name’ and ‘Crop_Sequence_Trt_Species_Order' attributes in the ‘Crop_Sequence_Trt’ table. For instance, the name of a legume-cereal sequence without application of nitrogen fertilizer (0N) could be ‘Garden pea-Common wheat, 0N’ where ‘Garden pea’ and ‘Common wheat’ are the common names listed in the United States Department of Agriculture Plants Database (http://plants.usda.gov/java/) for Pisum sativum and Triticum aestivum, respectively. Malik et al.105 and McEwen et al.108 described several crop sequences including grain legumes and crop sequences including barrelclover (Medicago truncatula) or common oat (Avena sativa), both preceding common wheat. For these two articles, we excluded the crop sequences including barrelclover and common oat because these crops were grown for forage production.
This attribute is an index identifying each species grown at a given field site during one or several growing seasons. Identical raw data were found to have been duplicated in two pairs of articles: Muchow et al.114 and Sinclair et al.153 on the one hand, and Heenan et al.71 and Armstrong et al.2 on the other. The duplicated raw data from Sinclair et al.153 and Heenan et al.71 were excluded because the number of crop*field site*growing season*treatment combinations was smaller in these two articles than in their duplicates.
‘Crop_Species_Scientific_Name’ and ‘Crop_Species_Common_Name’ attributes
These attributes give the scientific and common names of the species. The scientific name of each species was related to the common name listed in the United States Department of Agriculture Plants Database (http://plants.usda.gov/java/), to avoid confusion due to the use of different common names for the same species. In the absence of a common name for Brassica campestris, Lupinus atlanticus and Triticum sativum, the scientific names of these species were used as common names. In the presence of fallow period, it was not possible to give a scientific name and a common name, and ‘Fallow’ was reported.
We calculated the number of days from seeding date to harvest date, with the open-access application Time and Date (http://www.timeanddate.com/). For data averaged across multiple growing seasons, we calculated the number of days from seeding date to harvest date for each growing season and then obtained the average by dividing by the total number of growing seasons.
Some articles approximated seeding date and harvest date by describing these events as occurring in the ‘early’, ‘middle’ or ‘late’ part of the month. We defined ‘early’ as the first 15-day period of the month (1st–15th), ‘middle’ as the 15th day of the month and ‘last’ as the second 15-day period of the month (15th–30th or 15th–31st). In these cases, the number of days from seeding to harvest was calculated by selecting the last day of the period concerned, i.e., the 15th day of the month for ‘early’ and ‘middle’ and the 30th or 31st day of the month for ‘late’.
Some articles reported only the number of days from seeding to harvest, without indicating precise dates or months. In these cases, we reported only the number of days from seeding to harvest. We used the expression ‘NA NA NA’ (i.e., ‘Day Month Year’ formatted expression) for both seeding and harvest dates.
This attribute is used to distinguish preceding crops from following crops in the crop sequence. It takes three values: ‘0’ (i.e., the main crop or the preceding crop, mostly grain legumes), ‘1’ (i.e., the following crop, mostly cereals and oilseeds) and ‘2’ (i.e., the crop after the following crop, mostly cereals and oilseeds).
Some studies reported results for many different crops and management practices following the same preceding crop. The binary ‘Crop_Multiple_Following_For_Same_Preceding’ attribute was used to identify data associated with the same preceding crop.
‘Crop_Across_Treatment_Averaged_Value’ and ‘Crop_Across_Treatment_Averaged_Value_Type’ attributes
For species grown at the same field site during the same growing season, some articles reported only data averaged over combinations of treatments (e.g., cultivar*seeding date*presence of irrigation). We included these data provided that each type of individual treatment was precisely defined in the article. In all cases, we systematically reported whether or not the data were averaged over combinations of treatments. When data were averaged over combinations of treatments, the total number of replicates was calculated as the sum of the replicates for each of the treatments for which results were averaged.
For articles reporting data for several cultivars of the same species but without data averaging, the data were reported separately for each cultivar. For articles reporting data averaged over several cultivars of the same species, only the averaged data were included in the dataset. The total number of replicates was calculated by multiplying the number of replicates of each cultivar by the total number of cultivars.
‘Crop_Across_Species_Same_Treatment_Value’ and ‘Crop_Across_Species_Same_Treatment_Value_Type’ attributes
In some articles, different types of treatment were applied to species grown at the same site during the same growing season. Each different type of treatment was reported in this case.
As mentioned above, when averaged data were reported in the articles, the number of replicates was equal to the sum of the replicates used to calculate each average.
This attribute corresponds to grain yield data, with a few exceptions. For Brassica chinensis (pak choi), Citrullus lanatus (watermelon), Gossypium hirsutum (upland cotton), Ipomoea batatas (sweet potato) and Solanum lycopersicum (garden tomato), the yields reported are the economic yields. For Arachis hypogaea (peanut), pods are included in grain yields. In all other situations, the yield data given correspond to grain yields. Mutant non-nodulating legume cultivars, shading treatment and under-sowing treatment were excluded from the database. When grain yield data of following crops were confounded between the effect of preceding species and the effect of nitrogen fertilizer dose, these data were also excluded. Data were reported in 96% of all crop*field site*growing season*treatment combinations. Grain yield varied strongly both between grain legume species and between articles for a given species (Fig. 4a). Median grain yield was lowest for Vigna subterranea (bambarra groundnut) and highest for Trigonella foenum-graecum (sicklefruit fenugreek).
This attribute corresponds to aerial biomass data. Data were reported in 27% of all crop*field site*growing season*treatment combinations. Aerial biomass varied considerably both between grain legume species and between articles for a given species (Fig. 4b). Median aerial biomass was lowest for Vigna aconitifolia (moth bean) and highest for Trifolium repens (white clover).
‘Crop_Yield_Grain_DM_Percentage’ and ‘Crop_Biomass_Aerial_DM_Percentage’ attributes
These two attributes correspond to the percentage of dry matter to which grain yield and aerial biomass correspond, respectively. When only the percentage of dry matter corresponding to aerial biomass was available and grains were included in aerial biomass, we assumed that the grains accounted for the same percentage of dry matter as the aerial biomass.
This attribute was reported in the database to calculate aerial biomass at physiological maturity from grain yield. Data were reported in 4% of all crop*field site*growing season*treatment combinations (Fig. 4c). Median harvest index was lowest for Vicia villosa (winter vetch) and highest for Vicia faba (fababean).
‘Crop_N_Quantity_Grain’ and ‘Crop_N_Quantity_Aerial’ attributes
These two attributes correspond to the quantity of nitrogen in grains and aerial components, respectively. For the ‘Crop_N_Quantity_Grain’ attribute, data were reported in 10% of all crop*field site*growing season*treatment combinations. For the ‘Crop_N_Quantity_Aerial’ attribute, data were reported in 10% of all crop*field site*growing season*treatment combinations. As previous attributes, grain and aerial nitrogen quantities varied both between grain legume species and between articles for a given species (Fig. 5a,b). Median grain nitrogen quantity was lowest for Vigna subterranea (bambarra groundnut) and highest for Lupinus albus (white lupine). Median aerial nitrogen quantity was lowest for Vicia narbonensis (purple broad vetch) and highest for Lupinus mutabilis (sweet tarwi).
This attribute corresponds to the percentage of aerial nitrogen fixed by legume species. ‘NA’ was systematically reported for non-legume species. Data were reported in 3% of all crop*field site*growing season*treatment combinations (Fig. 5c). Median fixed aerial nitrogen percentage was lowest for Cajanus cajan (pigeonpea) and highest for Trifolium repens (white clover).
‘Crop_N_Fixed_Percentage_Aerial_Method’ and ‘Crop_N_Fixed_Percentage_Aerial_Reference_Species’ attributes
These two attributes correspond to the method used to determine the percentage of aerial nitrogen fixed by legume species (e.g., the 15N isotope dilution method or the A-value method), and the scientific name of the non-fixing reference species. Some articles used a legume reference species rather than a non-legume reference species. In all cases, the legume reference species was a mutant non-nodulating legume cultivar that did not fix atmospheric nitrogen.
‘Crop_Biomass_Aerial_Stage_Detailed’, ‘Crop_Biomass_Aerial_Stage_Simplified’, ‘Crop_N_Fixed_Percentage_Aerial_Stage_Detailed’ and ‘Crop_N_Fixed_Percentage_Aerial_Stage_Simplified’ attributes
These attributes correspond to the phenological stages at which aerial biomass and the percentage of fixed aerial nitrogen (or the quantity of fixed aerial nitrogen with the ‘Crop_N_Fixed_Quantity_Aerial’ attribute) were determined. The ‘Crop_Biomass_Aerial_Stage_Detailed’ and ‘Crop_N_Fixed_Percentage_Aerial_Stage_Detailed’ attributes correspond to the detailed phenological stage originally stated in the article. The ‘Crop_Biomass_Aerial_Stage_Simplified’ and ‘Crop_N_Fixed_Percentage_Aerial_Stage_Simplified’ attributes correspond to a simplified phenological stage divided into ‘Before physiological maturity’ and ‘Physiological maturity’.
This attribute corresponds to the percentage or the quantity of protein in grains. In the selected articles, these protein contents were often calculated by multiplying the percentage or the quantity of nitrogen in grains by a constant. However, this constant differed between articles. Note that only a few articles referred to the percentage or the quantity of protein. We reported the percentage or the quantity of protein in grains independently of the percentage or the quantity of nitrogen in grains.
This attribute corresponds to the simplified nitrogen balance originally calculated in the articles (e.g., the difference between the quantity of nitrogen in grains and the quantity of fixed aerial nitrogen). Nitrogen balance data were only reported if the attributes used to calculate them were not directly available from raw data (e.g., the quantity of nitrogen in grains and the quantity of fixed aerial nitrogen). This was the case for only three articles.
‘Crop_N_Soil_Quantity_Percentage_Seeding’ and ‘Crop_N_Soil_Quantity_Percentage_Harvest’ attributes
These two attributes correspond to the percentage or the quantity of soil nitrogen at seeding and at harvest, respectively.
‘Crop_N_Soil_Quantity_Percentage_Seeding_Type’, ‘Crop_N_Soil_Quantity_Percentage_Seeding_Depth’, ‘Crop_N_Soil_Quantity_Percentage_Seeding_Date’, ‘Crop_N_Soil_Quantity_Percentage_Harvest_Type’, ‘Crop_N_Soil_Quantity_Percentage_Harvest_Depth’ and ‘Crop_N_Soil_Quantity_Percentage_Harvest_Date’ attributes
These attributes correspond to (i) the type of nitrogen (e.g., nitrogen or nitrate or mineral), (ii) the depth of soil used to determine the percentage or the quantity of soil nitrogen and (iii) the date at which soil measurements were made. These attributes were reported at both seeding and harvest.
This attribute corresponds to the water use or the water balance, according to the equation given in the selected articles. Data were reported in 6% of all crop*field site*growing season*treatment combinations. Water use (or water balance) varied both between grain legume species and between articles for a given species (Fig. 6). Median water use (or water balance) was lowest for Vigna aconitifolia (moth bean) and highest for Lablab purpureus (hyacinthbean).
‘Crop_Harvest_Index’, ‘Crop_N_Percentage_Grain’, ‘Crop_N_Percentage_Aerial’, ‘Crop_N_Harvest_Index’, ‘Crop_N_Fixed_Quantity_Aerial’, ‘Crop_Water_Use_Balance_Efficiency_Grain’ and ‘Crop_Water_Use_Balance_Efficiency_Aerial’ attributes
These seven attributes were reported in the database to calculate missing data: aerial biomass, quantity of nitrogen in grains, quantity of nitrogen in aerial components, percentage of fixed aerial nitrogen, and water use.
‘Crop_Biomass_Aerial_Definition’, ‘Crop_N_Percentage_Aerial_Definition’, ‘Crop_N_Quantity_Aerial_Definition’, ‘Crop_N_Fixed_Quantity_Aerial_Definition’ and ‘Crop_Water_Use_Balance_Efficiency_Aerial_Definition’ attributes
Different aerial components were included in the aerial biomass, the percentage or the quantity of aerial nitrogen, and the efficiency of aerial water use or aerial water balance. These five attributes were used to determine the aerial components originally reported in the articles. When the ‘shoot’, ‘straw’ and ‘stubble’ terms were used to define the aerial components in the articles, we assumed that the grains were not included in the aerial components. This information was reported for (i) the aerial biomass in the ‘Crop_Biomass_Aerial_Definition’ attribute, (ii) the percentage of aerial nitrogen in the ‘Crop_N_Percentage_Aerial_Definition’ attribute, (iii) the quantity of aerial nitrogen in the ‘Crop_N_Quantity_Aerial_Definition’ attribute, (iv) the quantity of fixed aerial nitrogen in the ‘Crop_N_Fixed_Quantity_Aerial_Definition’ attribute, and (v) the efficiency of aerial water use or aerial water balance in the ‘Crop_Water_Use_Balance_Efficiency_Aerial_Definition’ attribute.
‘Crop_N_Balance_Simplified_Equation’ and ‘Crop_Water_Use_Balance_Equation’ attributes
For these two attributes, we reported the equations used to calculate simplified nitrogen balance and water use or water balance, respectively.
Attributes relating to error terms and error types
When available, we systematically reported error terms and error types associated with data about grain yield, aerial biomass, crop nitrogen content, residual soil nitrogen content and water use. For the ‘Crop_Yield_Grain’ attribute, the ‘Crop_Yield_Grain_Error’ attribute indicates the error term and the ‘Crop_Yield_Grain_Error_Type’ attribute indicates the error type for a given item of grain yield data for a given crop in the ‘Crop’ table. Error terms and error types were reported as raw data. For instance, when an article reported the error type as Fisher's Least Significant Difference, the data were directly reported as Fisher's Least Significant Difference. Unidentified error bars digitized from graphs were assumed to represent standard errors. When available, the numbers of replicates were also reported. For 48% of grain yields, both error terms and the numbers of replicates were reported. For 47% of grain yields, only the number of replicates was reported.
Each article was read carefully at least three times by the same person, to determine the type and the quantity of data reported by the authors. Once the data had been extracted, all the data reported in the tables were checked at least three times by the same person, to identify possible mistakes. SQL subset queries were systematically performed, to check the structural validity and coherence of class, numerical, index, binary and date attributes within each table, and to check the relationships between ‘mother’ and ‘child’ tables. Once the set of data was complete, SQL queries were carried out, to compare the entire content of the database with the original data reported in the selected articles. We systematically and manually checked for outliers in order to detect possible mistakes made during data extraction. We returned to the original articles as many times as needed to check the accuracy of the data. We checked the qualitative and quantitative contents of all class, numerical, index, binary and date attributes by importing each table in turn into the R software (version 3.2, https://cran.r-project.org/), and by visualizing data distribution for each attribute in turn. When the meaning of the data reported in the articles was unclear, authors were directly contacted and asked to provide additional information about their experimental protocols. Authors were also asked to provide additional data, particularly if large numbers of treatments had been averaged in their articles. Overall, 17 authors provided us with additional information and raw data (see the Acknowledgements section).
The dataset is based on a compilation of experimental data published in 173 articles over the last 50 years. To our knowledge, this dataset is unique and constitutes the most comprehensive agronomic dataset for grain legume crops worldwide.
The dataset can be analyzed to assess performances for a broad diversity of grain legume species, and to provide global rankings for these species in terms of grain yield, aerial biomass, harvest index, aerial nitrogen fixation, nitrogen content in aerial components, nitrogen balance, and water use. It can also be used to assess the effect of including different grain legumes as preceding crops, before cereals and oilseeds in the same crop sequences. Global species rankings were recently estimated for energy crops188, but never for grain legumes. Rankings of grain legume species could be directly derived from our dataset by using standard meta-analysis methods based on random-effect models188. Attributes describing environmental factors (e.g., climate conditions and soil types) and management practices (e.g., tillage, fertilization, pest management and irrigation) can be used to analyze the variability of grain legume performances over field sites, growing seasons, and management practices.
Our dataset covers several contrasted geographical areas. It can be used to target suitable grain legume species for cultivation in particular pedoclimatic conditions. In the context of climate change, the database represents a useful resource to assess comparatively the production of grain legume species in drought-prone environments, or to identify innovative agricultural techniques for improving grain legume cultivation under yield-limiting abiotic and biotic stresses.
Subsets of the dataset can be used to address regional issues. Figure 7 presents six regional networks including the pairs of grain legume species frequently compared at the same field sites during the same growing seasons, and the grain legume species that were not frequently compared with each other. Such networks can be used to identify the species for which reliable comparisons are feasible, and those for which limited data are available. A quantitative analysis can then be computed to determine regional rankings of grain legume species. This approach could be used to identify highly productive species, and to compare them with major regional grain legume crops (e.g., garden pea in Europe or soybean in North America). Our dataset could thus shed new light on the potential value of as yet underused grain legumes from regional to global scales.
As geographical coordinates of the experiments were systematically reported, our dataset can be connected to large-scale climate and soil maps, and to Geographic Information Systems. An example is shown in Fig. 1 where the Köppen-Geiger climatic classification was indicated for field sites included in the database. Similar maps could be easily produced using other global classification of agroecological zones (e.g., the Global Agro-Ecological Zones Data Portal, http://gaez.fao.org/Main.html#), or soil typology (e.g., the Soils Portal of the Food and Agriculture Organization of the United Nations, http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/).
The dataset is also useful for comparing productivity levels of native and non-native grain legume species used as raw materials for food and feed across diverse geographic regions. Grain yield data can be converted into crude protein or energy contents metabolizable for livestock animals (e.g., pigs and poultry) using, for example, the Feedipedia Animal Feed Resources Information System (http://www.feedipedia.org/).
In the future, the dataset could be expanded in different ways. Results of new experiments comparing grain legume species can be easily included in our database. So far, we focused on legume species produced for grains, but legume grown for forage can also be included in the database without changing the relational database structure. In many world regions such as Africa, Asia and South America, agricultural grain legumes are frequently intercropped. Data collected in intercropping experiments could be further included in our dataset. Note that the relational structure of the database is relatively coercive, and should be modified with great care. The addition of a new table can have consequences on the relational framework and the cardinality relationships. But new data or new attributes can be easily incremented in existing tables.
The CSV format is well adapted for analyzing data using standard statistical softwares such as the R software (https://cran.r-project.org/). However, because of the cascade path between tables and of the cardinality relationships between attributes (Fig. 3), data extraction can be easily performed using SQL queries. An example of query is presented below for extracting binary data indicating absence (‘0’) or presence (‘1’) of tillage management for grain legume species included in the article indexed ‘29’ in our dataset:
SELECT IDCrop, Crop_Species_Scientific_Name, IDTillage, Tillage_Presence_Tillage
FROM Article, Site, Crop_Sequence_Trt, Crop, Tillage
The result of the SQL query is:
IDCrop, Crop_Species_Scientific_Name, IDTillage, Tillage_Presence_Tillage
853 Cicer arietinum 849 1 854 Vicia faba 851 1 857 Lens culinaris 856 1 858 Pisum sativum 858 1 859 Cicer arietinum 860 1 860 Vicia faba 861 1 861 Lens culinaris 862 1 862 Pisum sativum 863 1 864 Cicer arietinum 864 1 865 Vicia faba 865 1 866 Lens culinaris 866 1 867 Pisum sativum 867 1 869 Cicer arietinum 868 1 870 Vicia faba 870 1 871 Lens culinaris 871 1 872 Pisum sativum 872 1 873 Cicer arietinum 873 0 874 Vicia faba 874 0 875 Lens culinaris 875 0 876 Pisum sativum 876 0 877 Cicer arietinum 877 0 878 Vicia faba 878 0 879 Lens culinaris 879 0 880 Pisum sativum 880 0 881 Cicer arietinum 881 0 882 Vicia faba 882 0 883 Lens culinaris 883 0 884 Pisum sativum 884 0 885 Cicer arietinum 885 0 887 Vicia faba 886 0 888 Lens culinaris 887 0 890 Pisum sativum 889 0
Other examples of SQL queries are shown in the TXT-formatted file entitled ‘Examples_SQL_Queries.txt’, downloadable from Dryad Digital Repository (Data Citation 1: Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f).
How to cite this article: Cernay, C. et al. A global experimental dataset for assessing grain legume production. Sci. Data 3:160084 doi: 10.1038/sdata.2016.84 (2016).
Cernay, C., Pelzer, E., & Makowski, D. Dryad Digital Repository http://dx.doi.org/10.5061/dryad.mf42f (2016)
We thank G. Amato, C. Chen, A. L. Fernandez, Y. T. Gan, K. E. Giller, N. Gogoi, D. Herridge, H.-P. Kaul, G. Kirchhof, M. A. Liebig, P. Miller, R. Neugschwandtner, K. H. M. Siddique, D. Spaner, M. Unkovich, C. S. Wortmann and A. A. Yusuf for providing us with additional information and raw data. We thank A. Bône for assistance with the literature search. We thank D. Beillouin, T. Ben-Ari, G. Corre-Hellou, L. Hossard, M.-H. Jeuffroy, J.-M. Meynard, O. Réchauchère, A. Schneider, J.-M. Teulé and A.-S. Voisin for insightful comments. This work was supported by the French National Research Agency (ANR) under the ‘Investments for the Future’ program (ANR-10-IDEX-0003-02) as part of the LabEx BASC (ANR‐11‐LABX‐0034).
About this article
Acta Physiologiae Plantarum (2017)