Open Data Journal for Agricultural Research
Open Data Journal for Agricultural Research publishes data sets derived from agricultural research as open data resources with an open license, where agricultural research is here interpreted in the broad sense of research on agricultural systems from plant growth and animal development to processing of agricultural products into consumer products. Agricultural systems depend on natural systems, hence data sets capturing environmental or spatial resources are crucial for agricultural research.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Dataset for multi-model comparison and ensemble simulations of canola growth and yield across global sites
This paper describes the dataset that was used to test the reliability of eight crop models in simulating growth and yield of canola in response to sowing dates, nitrogen inputs and climate variability across five countries. The dataset includes four spring cultivars and three winter cultivars across six sites, which represents a diverse range of canola production areas around the world. Model calibration and validation were conducted in the framework of the Agricultural Model Intercomparison and Improvement Project for canola (AgMIP-Canola). Field experimental datasets include site characterization, soil profile characterization, initial soil conditions (soil water and mineral nitrogen contents), in-season and end-season crop measurements (phenology, LAI, biomass, and nitrogen content in leaves, stems and pods, some with seed oil content), and daily weather data. Simulation datasets include the simulation results generated by ten individual model frameworks (eight crop models, APSIM and DSSAT respectively by two groups) for the experimental periods, and scenario simulations using 30 years historical weather data (1981 – 2010) together with a full multi-factorial combinations of temperature (-3, 0, +3, +6, +9 oC), rainfall (-25%, -10%, 0, +10%, +25%), CO2 concentrations (360, 450, 540, 630, 720 ppm) and nitrogen input rates (0, +25%, +50%, +100%, +150%).
Copyright (c) 2025 Di He, Jing Wang, Julianne Lilley, Brendan Christy, Munir Hoffmann, Garry O'Leary, Jerry Hatfield, Luigi Ledda, Paola Deligios, Brian Grant, Qi Jing, Claas Nendel, Henning Kage, Budong Qian, Ehsan Eyshi Rezaei, Ward Smith, Wiebke Weymann, Frank Ewert, Enli Wang https://creativecommons.org/licenses/by/4.0
Sugar beet (Beta vulgaris L.) yield and quality data at different harvest dates from a multi-environmental study in Germany
This paper presents data from a two-year study with sugar beet in Germany (2000 and 2001). A total of 27 field trials were conducted in a wide range of environmental conditions including trials with and without irrigation. Sequential harvests were made every 14-28 days between May and October. Root yield and quality, leaf yield, leaf area index and soil water content were determined in four replicates at each harvest date. Soil characteristics were assessed in the field and daily weather data were collected for each trial site. The dataset is suitable for validating sugar beet growth models.
Copyright (c) 2025 Christine Kenter, Christa M. Hoffmann https://creativecommons.org/licenses/by/4.0
Agronomic dataset on potato growth and yield in the Netherlands
Data was collected at two field locations in the Netherlands: on the clayey soils in Lelystad and the sandy soils of Vredepeel during 2019 and 2020. The original aim of this dataset was to calibrate and evaluate crop growth models for estimating potential, water-limited and nitrogen-limited yield levels for modern potato cultivars. Therefore, treatments include different cultivars, irrigation regimes, nitrogen fertilization, and light interception. During the two seasons extensive data was collected either passively, via non-destructive or via destructive measurements. Passive measurements consisted of weather and soil moisture data and were taken continuously throughout the season. Non-destructive data was collected on photosynthesis, reflection/light intensity, SPAD chlorophyll values, plant height, crop phenology, and groundwater level roughly every other week throughout the seasons. Destructive measurements were taken on biomass (leaves, stems and tubers), leaf area, NPK content (leaves), and the number of tubers between five and seven times per season. Additionally, during the final harvest data was collected on the tuber size distribution, marketability, and NPK content of the tubers.
Copyright (c) 2024 Tamara ten Den, Inge van de Wiel, Martin van Ittersum, Frits van Evert, Allard de Wit, Pytrik Reidsma https://creativecommons.org/licenses/by/4.0
Experimental and simulated data for crop and grassland production and carbon-nitrogen fluxes
Multi-year datasets from field experiments and simulations at five agricultural sites in the Northern Hemisphere were developed for three cropland sites in Ottawa (Canada), Grignon (France) and Delhi (India) and two grassland sites at Laqueuille (France) and Easter Bush (UK). The cropland sites have rotations with wheat, triticale, maize, rapeseed, soybean, phacelia and rice, as well as periods of bare fallow. Cattle (Laqueuille) or mixed cattle and sheep (Easter Bush) graze in the two grassland sites. Field data were collected between 2003 and 2012 for three to eight years, including grain yield/above‐ground net primary productivity, nitrous oxide emissions, carbon fluxes (gross primary production, net ecosystem exchange, ecosystem respiration), together with daily weather data (solar radiation, maximum and minimum temperatures, precipitation, wind speed, relative humidity, vapour pressure), soil properties, and records of crop and grassland management. Simulated outputs are from 23 models: 11 crop models, eight grassland models and four models simulating both systems.
Copyright (c) 2025 Renáta Sándor, Fiona Ehrhardt, Peter Grace, Val Snow, Sylvie Recous, Pete Smith, Jean-Francois Soussana, Bruno Basso, Arti Bathia, Lorenzo Brilli, Jordi Doltra, Chris Dorich, Luca Doro, Nuala Fitton, Brian Grant, Matthew Harrison, Ute M. Skiba, Miko Kirschbaum, Katja Klumpp, Patricia Laville, Joël Léonard, Raphaël Martin, Raia-Silvia Massad, Andrew Moore, Vasilis Myrgiotis, Elizabeth Pattey, Susanne Rolinski, Joanna Sharp, Ward Smith, Lianhai Wu, Qing Zhang, Gianni Bellocchi https://creativecommons.org/licenses/by/4.0
A daily time-step observed and scenario climate dataset on a European grid for crop modelling applications
This paper describes a daily time-step observed and scenario climate dataset on a European grid with 25 km x 25 km spatial resolution. The dataset was developed for crop modelling applications in the MACSUR project. The dataset covers the period 1980-2010 for observations; this is, for a baseline period of 1981-2010 and the year 1980 for crop model simulations with sowing dates in the autumn. It also covers the periods 2040-2069 and 2070-2099 for 5 Global Climate Models (GCMs) x 2 forcing scenarios (RCP4.5 and RCP8.5) and 2 GCMs with RCP2.6. The Joint Research Centre's (JRC) Agri4Cast gridded dataset was used for the baseline. The scenarios have been calculated using an enhanced delta change method that applies changes in aspects of temperature and precipitation variability in addition to changes in mean climate.
Copyright (c) 2024 Stefan Fronzek, Heidi Webber, Reimund Rötter, Alex C. Ruane, Frank Ewert https://creativecommons.org/licenses/by/4.0
Winter Wheat Experiments to Optimize Sowing Dates and Densities in a High-Yielding Environment in New Zealand: Field Experiments and AgMIP-Wheat Multi-Model Simulations
This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. The data includes one winter wheat cultivar (Wakanui) grown during six consecutive years, from 2012-2013 to 2017-2018, at two farms located in Leeston and Wakanui in Canterbury, New Zealand. The simulations were carried out in the framework of the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat). Data include local daily weather data, soil profile characteristics and initial conditions, crop measurements at maturity (grain, stem, chaff and leaf dry weight, ear number and grain number, grain unit dry weight), and at stem elongation and anthesis (total above ground dry biomass, leaf number per stem and leaf area index). Several in-season measurements of the normalized difference vegetation index (NDVI) and the fraction of intercepted photosynthetically active radiation (FIPAR) are also available. The crop model simulations include both daily in-season and end-of-season results from twenty-nine wheat models.
Copyright (c) 2024 Pierre Martre, Sibylle Dueri, Hamish Brown, Senthold Asseng, Frank Ewert, Heidi Webber, Mike George, Rob Craigie, Jose Guarin, Diego Pequeno, Tommaso Stella, Mukhtar Ahmed, Phillip Alderman, Bruno Basso, Andres Berger, Gennady Bracho Mujica, Davide Cammarano, Yi Chen, Benjamin Dumont, Ehsan Rezaei, Elias Fereres, Roberto Ferrise, Thomas Gaiser, Yujing Gao, Margarita Garcia-Vila, Sebastian Gayler, Zvi Hochman, Gerrit Hoogenboom, Kurt Kersebaum, Claas Nendel, Jørgen Olesen, Gloria Padovan, Taru Palosuo, Eckart Priesack, Johannes Pullens, Alfredo Rodríguez , Reimund Rötter, Margarita Ruiz Ramos, Mikhail Semenov, Nimai Senapati, Stefan Siebert, Amit Srivastava, Claudio Stöckle, Iwan Supit, Fulu Tao, Peter Thorburn, Enli Wang, Tobias Weber, Liujun Xiao, Chuang Zhao, Jin Zhao, Zhigan Zhao, Yan Zhu https://creativecommons.org/licenses/by/4.0
Climate and soil input data aggregation effects in crop models
This dataset contains interpolated and aggregated soil and climate data of the region of North Rhine-Westphalia (Germany). The data is provided for grids of 1, 10, 25, 50 and 100 km resolutions. These data grids represent spatial aggregations of the climate of approximately 1 km resolution and soil data of approximately 300 m resolution raster. The purpose of this data is the use as input for crop models. It thus contains the key relevant soil and climate variables for running crop models. Additionally, the data is specifically designed to analyze effects of scale and resolution in crop models, e.g. data aggregation effects. It has been used for several studies on spatial scales with regard to different scaling approaches, crops, crop models, model output variables, production situations and crop management among others.
Copyright (c) 2022 Andreas Enders, Holger Hoffmann, Stefan Siebert, Thomas Gaiser, Frank Ewert https://creativecommons.org/licenses/by/4.0
A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations
Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.
Copyright (c) 2023 Jose Guarin, Pierre Martre, Frank Ewert, Heidi Webber, Sibylle Dueri, Daniel Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, Gustavo Slafer, Francesco Giunta, Diego Pequeno, Tommaso Stella, Mukhtar Ahmed, Phillip Alderman, Bruno Basso, Andres Berger, Marco Bindi, Gennady Bracho-Mujica, Davide Cammarano, Yi Chen, Benjamin Dumont, Ehsan Eyshi Rezaei, Elias Fereres, Roberto Ferrise, Thomas Gaiser, Yujing Gao, Margarita Garcia-Vila, Sebastian Gayler, Zvi Hochman, Gerrit Hoogenboom, Leslie Hunt, Kurt Kersebaum, Claas Nendel, Jorgen Olesen, Taru Palosuo, Eckart Priesack, Johannes Pullens, Alfredo Rodriguez, Reimund Rotter, Margarita Ruiz Ramos, Mikhail Semenov, Nimai Senapati, Stefan Siebert, Amit Srivastava, Claudio Stockle, Iwan Supit, Fulu Tao, Peter Thorburn, Enli Wang, Tobias Weber, Liujun Xiao, Zhao Zhang, Chuang Zhao, Jin Zhao, Zhigan Zhao, Yan Zhu, Senthold Asseng https://creativecommons.org/licenses/by/4.0
AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat
The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (http://doi.org/10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.
Copyright (c) 2023 Bing Liu, Pierre Martre, Frank Ewert, Heidi Webber, Katharina Waha, Peter J. Thorburn, Alex C. Ruane, Pramod K. Aggarwal, Mukhtar Ahmed, Juraj Balkovič, Bruno Basso, Christian Biernath, Marco Bindi, Davide Cammarano, Weixing Cao, Andy J. Challinor, Giacomo De Sanctis, Benjamin Dumont, Mónica Espadafor, Ehsan Eyshi Rezaei, Elias Fereres, Roberto Ferrise, Margarita Garcia-Vila, Sebastian Gayler, Yujing Gao, Heidi Horan, Gerrit Hoogenboom, Roberto C. Izaurralde, Mohamed Jabloun, Curtis D. Jones, Belay T. Kassie, Kurt C. Kersebaum, Christian Klein, Ann-Kristin Koehler, Andrea Maiorano, Sara Minoli, Manuel Montesino San Martin, Christoph Müller, Soora Naresh Kumar, Claas Nendel, Garry J. O’Leary, Jørgen Eivind Olesen, Taru Palosuo, John R. Porter, Eckart Priesack, Dominique Ripoche, Reimund P. Rötter, Mikhail A. Semenov, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Marijn Van der Velde, Enli Wang, Joost Wolf, Liujun Xiao, Zhao Zhang, Zhigan Zhao, Yan Zhu, Senthold Asseng https://creativecommons.org/licenses/by/4.0
Cotton Response to CO2, Water, Nitrogen and Plant Density - A Repository of FACE, AgIIS and FISE Experiment Data
Several cotton experiments have been conducted at the University of Arizona's Maricopa Agricultural Center from which datasets have been obtained documenting cotton responses to elevated CO2 concentrations, water supply, nitrogen fertilizer, and planting density. In particular, these experiments included FACE (free-air CO2 enrichment; CO2, water; 10 treatment-years), AgIIS (Agricultural Irrigation Imaging System, pronounced Ag Eyes; nitrogen fertilizer, water supply; 4 treatment-years), and FISE (FAO-56 Irrigation Scheduling Experiments; irrigation scheduling method, planting density, nitrogen fertilizer; 24 treatment-years). Besides achieving the experimental objectives of determining cotton's response to the several variables, as well as testing remote sensing techniques, the comprehensive datasets are suitable for validating plant growth models because they include weather, soils, management, growth, yield and other data.
Copyright (c) 2022 Bruce Arnold Kimball, Kelly Thorp, Edward Barnes, Christopher Choi, Thomas Clarke, Paul Colaizzi, Glenn Fitzgerald, Julio Haberland, George Hendrey, Douglas Hunsaker, Michael Kostrzewski, Robert LaMorte, Steven Leavitt, Keith Lewin, Jackson Mauney, John Nagy, Paul Pinter, Peter Waller https://creativecommons.org/licenses/by/4.0
AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal
The Agricultural Model Intercomparison and Improvement Project (AgMIP) developed protocol-based methods for Regional Integrated Assessment (RIA) of agricultural systems. These methods have been applied by teams of scientists working with regional and national stakeholders across Sub-Saharan Africa and South Asia. This paper describes the data sets that were used to implement the AgMIP RIA methods for the Nioro region of Senegal. The goal of the RIA is to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin comprised of peanut, millet, maize and other minor crops and livestock, and to assess adaptations of that system to climate change, under current as well as future climate and socio-economic conditions. The data sets include: the Representative Agricultural Pathways (RAPs) developed for Nioro from 2000-2050; climate data used to implement crop yield simulations; the data used to parameterize the Agricultural Production Systems sIMulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop models, which include historical climate data and future climate scenarios; and the data used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOA-MD) economic simulation model. The analysis is structured around four AgMIP “core questions'' of climate impact assessment.
Copyright (c) 2022 Ibrahima Hathie, Dilys MacCarthy, Bright Freduah, Mouhamed Ly, Ahmadou Ly, Cheryl Porter, Roberto Valdivia, Alexander Ruane, John Antle, Carolyn Mutter, Gerrit Hoogenboom https://creativecommons.org/licenses/by/4.0
Data from the Arizona FACE (Free-Air CO2 Enrichment) Experiments on Sorghum at Ample and Limiting Levels of Water Supply
Two free-air CO2 enrichment (FACE) experiments were conducted on sorghum (Sorghum bicolor (L.) Möench, a C4 grain crop) at Maricopa, Arizona, U.S.A. during the 1998 and 1999 summer growing seasons. They were conducted at ample and limited (50% of ample) supplies of water. A large and varied set of data on plant, soil, and microclimatic responses to the elevated CO2 and its interactions with the water supply was collected. The dataset assembled herein consists of many of these data, including those generally used for model validation, such as management, soils, daily and 15-minute weather, physiology (net photosynthesis, plant water potential, relative water content), phenology, biomass growth, leaf area, yield, canopy temperatures, energy balance, evapotranspiration, soil moisture, reflectance and vegetation indices, and absorption of photosynthetically active radiation. Partitioning of nitrogen between nitrate and cyanide, which affects toxicity of the sorghum forage to livestock, was measured. In addition, isotopic methods were used to trace carbon flows in both FACE and Control plots from atmosphere to plant material to soil carbon pools. The dataset is useful for validation of sorghum growth models, which get used to predict likely future sorghum productivity given projected global climate change.
Copyright (c) 2021 Bruce Arnold Kimball, Michael Ottman, Jim, Gerard Wall, Steven Leavitt, Li Cheng, Matthew Conley, Robert LaMorte, Jonathan Triggs, Roslyn Gleadow https://creativecommons.org/licenses/by/4.0
Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations
The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario. Data access via DOI 10.17026/DANS-ZB6-6FVQ.
Coherent multi-variable field data set of an intensive cropping system for agro-ecosystem modelling from Müncheberg, Germany
A six-year (1993-1998) multivariable data set for a four-plot intensive crop rotation (sugar beet - winter wheat - winter barley - winter rye - catch crop) located at Leibniz Centre for Agricultural Landscape Research (ZALF) Experimental Station, Müncheberg, Germany, is documented in detail. The experiment targets crop response to water supply on sandy soils (Eutric Cambisol), applying rain-fed and irrigated treatments. Weather as well as soil and crop processes were intensively monitored and management actions were consistently recorded. The data set contains coherent data for soil (water, nitrogen contents), crop (ontogenesis, plant, tiller and ear numbers, above-ground and root biomasses, yield, carbon and nitrogen content in biomass and their fractions, sugar content in beet), weather (all standard meteorological variables) and management (soil tillage, sowing, fertilisation, irrigation, harvest). In addition, observation methods are briefly described. The data set is available via the Open Research Data Portal at ZALF Müncheberg and is published under doi:10.4228/ZALF.1992.271. The data set was used for model intercomparison within the crop modelling part (CropM) of the international FACCE MACSUR project. Data access via DOI 10.4228/ZALF.1992.271.
Kenya public weather processed by the Global Yield Gap Atlas project
The Global Yield Gap Atlas project (GYGA - http://yieldgap.org) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et al. (2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers daily weather data for 12 locations in Kenya for the years 1998-2012. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate bias corrected weather data from a combination of observed data and other external weather data. The bias corrected weather data consist of daily TRMM rain data and NASA POWER Tmax, Tmin, and Tdew data. These data are corrected based on calibrations with short-term (<10 years) observed weather data. The data can be found via DOI 10.17026/DANS-ZYU-XKHC.
Long-term soil hydrological data of a Pleistocene region in North-East Germany
Long-term soil hydrological studies were carried out at the ZALF Muencheberg Experimental Station. Muencheberg is located in a Pleistocene end moraine landscape in NE Germany about 50 km east of Berlin and 40 km west of the Oder river, the border with Poland. The soils are formed on Pleistocene parent material, classified as Haplic Albeluvisol with a Bt horizon mostly beginning at about 70 cm depth. From 1994-1999 the site was used as arable land managed by different farming systems (integrated, integrated with irrigation, ecological and low input). From 2000 to 2005 the effect of different tillage systems (plough and no till) was investigated. Furthermore, soil water was extracted by use of suction cups for analysing the soil water nitrate concentrations. The data were used for quantifying deep drainage rates and nitrate losses as described in (Schindler and Mueller, 2008 and Schindler et al., 2010). All basic data (soil water content, tension, precipitation, potential evapotranspiration and soil water nitrate concentrations) are accessible via DOI 10.4228/ZALF.1995.282.
Soil hydraulic functions of international soils measured with the Extended Evaporation Method (EEM) and the HYPROP device
Abstract. The paper presents a data base of 173 soil hydrological data (raw data) from 71 sites all over the world (Asia, Africa, Australia America and Europe). The samples were mainly collected and measured as part of research projects. The soils cover a wide range of texture classes and dry bulk densities. The data base consists of water retention and unsaturated hydraulic conductivity data. Additional data of the particle size distribution, the dry bulk density, the organic matter content and other variables are included. The hydraulic data were measured with the Extended Evaporation Method (EEM) and the HYPROP device in the range between saturation and close to the wilting point. Information to the geo reference, the soil type, the sampling depth and the horizon are given. Data access via DOI 10.4228/ZALF.2003.273.
Hydraulic properties of horticultural substrates
One main criterion for evaluating the suitability of horticultural substrates for horticultural applications is their capability to store and transport water. The basic substrate hydraulic variables which provide this information are the water retention curve and the unsaturated hydraulic conductivity function. Substrate shrinkage and water repellency are of similar importance. A set of 36 commercial horticultural substrates was selected and the hydraulic properties (water retention curve, unsaturated hydraulic conductivity function, capillary rise and shrinkage) were measured with the Extended Evaporation Method (EEM). Additionally, the water drop penetration time was determined as a measure of wettability. Here the raw data are presented. Data access under DOI 10.4228/ZALF.2015.278.
Data from the Arizona FACE (Free-Air CO2 Enrichment) Experiments on Wheat at Ample and Limiting Levels of Water and Nitrogen
Four free-air CO2 enrichment (FACE) experiments were conducted on wheat (Triticum aestivum L. cv. Yecora Rojo) at Maricopa, Arizona, U.S.A. from December, 1992 through May, 1997. The first two were conducted at ample and limited (50% of ample) supplies of water, and second two at ample (350 kg N ha-1) and limited (70 and 15 kg N ha-1) supplies of fertilizer nitrogen. More than 50 scientists participated, and they collected a large and varied set of data on plant, soil, and microclimatic responses to the elevated CO2 and its interactions with the water and N treatments. The dataset has been popular with wheat growth modelers who have utilized the growth, yield, and other data to validate their models, which get used to predict likely future wheat productivity with projected global change. The dataset assembled herein contains many of these data, including management, soils, weather, physiology, phenology, biomass growth, leaf area, yield, quality, canopy temperatures, energy balance, soil moisture, nitrogen assimilation, and other data. Data access via DOI 10.7910/DVN/YRTQ3Z.
The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations
The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. All data are available via DOI 10.7910/DVN/CJJBSR.
Copyright (c) 2023 Pierre Martre, Matthew P. Reynolds, Senthold Asseng, Frank Ewert, Phillip D. Alderman, Davide Cammarano, Andrea Maiorano, Alexander C. Ruane, Pramod K. Aggarwal, Jakarat Anothai, Bruno Basso, Christian Biernath, Andrew J. Challinor, Giacomo De Sanctis, Jordi Doltra, Benjamin Dumont, Elias Fereres, Margarita Garcia-Vila, Sebastian Gayler, Gerrit Hoogenboom, Leslie A. Hunt, Roberto C. Izaurralde, Mohamed Jabloun, Curtis D. Jones, Belay T. Kassie, Kurt C. Kersebaum, Ann-Kristin Koehler, Christoph Müller, Soora Naresh Kumar, Bing Liu, David B. Lobell, Claas Nendel, Garry O'Leary, Jørgen E. Olesen, Taru Palosuo, Eckart Priesack, Ehsan Eyshi Rezaei, Dominique Ripoche, Reimund P. Rötter, Mikhail A. Semenov, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Peter Thorburn, Katharina Waha, Enli Wang, Jeffrey W. White, Joost Wolf, Zhigan Zhao, Yan Zhu https://creativecommons.org/licenses/by/4.0
Field experimental data for crop modeling of wheat growth response to nitrogen fertilizer, elevated CO2, water stress, and high temperature
Field experimental data of five experiments covering a wide range of growing conditions are assembled for wheat growth and cropping systems modeling. The data include an experiment on interactive effects of elevated CO2 by water and elevated CO2 by nitrogen fertilizer application from a Free-Air Carbon Dioxide Enrichment experiment (FACE) at Arizona in USA; a nitrogen rate fertilizer experiment from three locations and two years in The Netherlands; water deficit experiments at Lincoln in New Zealand and at Cunderdin in Australia; and a temperature sensitivity experiment at Obregon in Mexico. The data consist of 65 experimental treatments with more than 1000 detailed observations, with time series of development and growth, soil water and soil nitrogen dynamics, yield and yield components, daily weather, soil characteristics, and cultivar descriptions. These data have been used in various previous agronomic and crop modeling studies. Assembled data are quality checked and supplied in Agricultural Model Inter-comparison and Improvement (AgMIP) format. Data access via DOI 10.7910/DVN/V4P6PU.
Field data set of different cropping systems for agro-ecosystem modelling from Müncheberg, Germany
A coherent field dataset (1993-1998) for three different cropping systems (intensive, organic, extensive) under rainfed conditions on the basis of the identical crop rotation (sugar beet - winter wheat - winter barley - winter rye - catch crop) located at the Experimental Station with sandy soils (Eutric Cambisol) of the Leibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg, Germany, is described in detail. Soil and crop processes as well as meteorological parameters were intensively monitored and all agro-management actions were meticulously recorded. The dataset contains data for crop (ontogenesis, plant, tiller and ear numbers, above-ground and root biomasses, yield, carbon and nitrogen content), soil (water and nitrogen contents), weather (all essential meteorological parameters) and management (soil tillage, sowing, fertilization, harvest). Additional the data set contains the meta-information for the observations. The dataset was used within the international workshop “Modelling Water and Nutrient Dynamics in Soil-Water Systemâ€ with the participation of 18 different models from nine countries. The dataset is available via the Open Research Data Portal at ZALF Müncheberg and is published under DOI 10.4228/ZALF.1992.167.
Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment
Temperatures are warming on a global scale, a phenomenon that likely will affect future crop productivity. Crop growth models are useful tools to predict the likely effects of these global changes on agricultural productivity and to develop strategies to maximize the benefits and minimize the detriments of such changes. However, few such models have been tested at the higher temperatures expected in the future. Therefore, a “Hot Serial Cerealâ€ experiment was conducted on wheat (Triticum aestivum L.), the world’s foremost food and feed crop, in order to obtain a dataset appropriate for testing the high temperature performance of wheat growth models. The wheat (Cereal) was planted serially (Serial) about every six weeks for over two years at Maricopa, Arizona, USA, which experiences the whole range of temperatures at which plants grow on Earth. In addition, on six planting dates infrared heaters in a T-FACE (temperature free-air controlled enhancement) system (Hot) were deployed over one-third of the plots to warm the wheat by additional target 1.5°C during daytime and 3.0°C at night. Achieved average degrees of warming were 1.3 and 2.7°C for day and night. Overall, a dataset covering 27 differently treated wheat crops with three replicates each was obtained covering an air temperature range from -2 to 42°C. Herein, the management, soils, weather, physiology, phenology, growth, yield, quality, and other data are presented. Data access via doi 10.7910/DVN/AEFLHB.
A dataset of spectral and biophysical measurements over Russian wheat fields
From 2011 to 2013 the MOCCCASIN project (MOnitoring Crops in Continental Climates through Asimillation of Satellite INformation) was carried out financed by the European Commission 7th Framework Programme. During the project, two field campaigns (2011 and 2012) were carried out at two sites (Odoyev and Plavsk) in the Tula region of Russia. During these two campaigns, observations were made at selected winter-wheat fields consisting of phenological stage, biomass samples, hemispherical photographs, spectral properties of the canopy and the soil as well as ancillary information about the field. Meteorological observations from synoptic and agrometeorological stations were collected from the stations in and surrounding the Tula region. Finally, a large trajectory throughout the whole Tula region was surveyed in order to collect fields with different crop types. Data access via doi 10.7910/DVN/CG0UQB.
The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations
The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F
Global field experiments for potato simulations
A large field potato experimental data set has been assembled for simulation modeling. The data are from temperate, subtropical, and tropical regions across the world and include 87 experiments with 204 treatments. Treatments include nitrogen fertilizer, irrigation, atmospheric CO2 levels, temperature, cultivars, and locations. For all experiments, measurements include tuber fresh and dry weight. For some experiments, measurements include in-season biomass, leaf area index, stem and leaf weight, N uptake, soil water, and soil N contents. Each experiment has soil characteristics and daily data for solar radiation, rainfall, and maximum and minimum temperature. The data have been quality checked and used in a previous simulation exercise. All data are in AGMIP format. Data access via doi 10.7910/DVN/4KJ5ME.
A comprehensive data set demonstrating the spatial variability of soil properties and crop growth conditions at field scale
A four-year (1999-2002) multivariable data set for one specific agricultural used field located in North Rhine-Westphalia is documented in detailed. The data set focusses on the small-scale heterogeneity of soil properties varying in the spatial and temporal dimension. Initial soil sampling was conducted at altogether 80 sampling points arranged in a regular and a nested grid along the 20 ha large field. Information about the soil inventory (soil texture, soil organic carbon) exists for three subsequent soil layers to a total depth of 90 cm and for every sampling point. Subsequently, the same points and layers were examined for the soil variables soil moisture and soil nitrogen biannually. Additional information about crop rotation, tillage, site-specific fertilization, yield performance and weather complete the data set was that used for model inter-comparison within the crop modelling part (CropM) of the international FACCE JPI MACSUR2 project.
Landscape diagnostic survey data of wheat production practices and yield in eastern India
Approximately 7,600 wheat plots were surveyed and geo-tagged in the 2017-18 winter or rabi season in Bihar and eastern Uttar Pradesh (UP) in India to capture farmers’ wheat production practices at the landscape level. A two-stage cluster sampling method, based on Census data and electoral rolls, was used to identify 210 wheat farmers in each of 40 districts. The survey, implemented in Open Data Kit (ODK), recorded 226 variables covering major crop production factors such as previous crop, residue management, crop establishment method, variety and seed sources, nutrient management, irrigation management, weed flora and their management, harvesting method and farmer reported yield. Crop cuts were also made in 10% of fields. Data were very carefully checked with enumerators. These data should be very useful for technology targeting, yield prediction and other spatial analyses.
Copyright (c) 2021 Anurag Ajay, Peter Craufurd, Sachin Sharma https://creativecommons.org/licenses/by/4.0
Informal and non-market operations in activity systems
Measuring informal and non-market activities: a representative survey of Kanak households in the indigenous villages of New Caledonia. In 2011, a survey was conducted to examine the role of agricultural activities (agriculture, breeding, fishing and hunting) in the economies of Kanak households living in such villages in New Caledonia. In total, 1429 domestic groups have been interviewed. The survey focused on the activities performed during 2010 and was representative of the indigenous population at national level as well as in 10 areas within the 3 provinces of New Caledonia. In order to allow people to use them for further analysis, this paper describes the conceptual framework, the nature and the quality of the data. A copy of the data collected is available via the following link: https://dataverse.cirad.fr (doi:10.18167/DVN1/VWWVXU). In order to allow people to use them for further analysis, this paper describes the conceptual framework, the nature and the quality of the data.
Copyright (c) 2023 Severine Bouard, Leïla Apithy, Stephane Guyard, Michel Passouant, Jean-Michel Sourisseau, Jean-François Belières https://creativecommons.org/licenses/by/4.0
Multi-variable experimental data set of agronomic data and gaseous soil emissions from maize, oilseed rape and other energy crops at eight sites in Germany
Greenhouse gas emissions (GHG), as well as other gaseous emissions and agronomic variables were continuously measured for three years (2011/2012 – 2014/2015) at eight experimental field sites in Germany. All management activities were consistently documented. The GHG-DB-Thuenen stores these multi-variable data sets of gas fluxes (CO2, N2O, CH4 and NH3), crop parameters (ontogenesis, aboveground biomass, grain and straw yield, N and C content, etc.), soil characteristics (nitrogen content, NH4-N, NO3-N, bulk density etc.), continuously recorded meteorological variables (air and soil temperatures, radiation, precipitation, etc.), management activities (sowing, harvest, soil tillage, fertilization, etc.), and its metadata (methods, further information about variables, etc.). In addition, NOx data were measured and analyzed. Also available are site-specific calculated C and N balances for the respective crops and crop rotations.
Copyright (c) 2021 Janine Mallast, Heinz Stichnothe, Heinz Flessa, Roland Fuß, Antje M. Lucas-Moffat, Ute Petersen-Schlapkohl, Jürgen Augustin, Ulrike Hagemann, Katharina Kesenheimer, Reiner Ruser, Teresa Suárez Quiñones, Annette Prochnow, Klaus Dittert, Vytas Huth, Stephan Glatzel https://creativecommons.org/licenses/by/4.0