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AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations

This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations

This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations

This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

opencc-by-sa-4.0Oct 2023View details →

C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.

There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.

opencc-by-4.0May 2024View details →

Soil C models for evaluating the effect of cover crops

Excel implementation and dataset for three C cycling models:  <ul> <li>Yasso20: monthly simulation version</li> <li>SOMIC 1.0: excel implementation with both simple time step and Euler-Heng iteration</li> <li>Single pool simulation model</li> <li>In addition input files for DNDC.Can.9.5.8 for one of the farms. </li> </ul> There are three versions of the SOMIC model: <ol> <li>The Zip file contains the operational version of the files for the cover crop experiment example and an </li> <li>_Euler-Heng version is an alternative version of the numerical simulation, which can be unstable (feel free to improve, do not use for simulation as such)</li> <li>The _MC version is a Monte Carlo uncertainty analysis implementation for one farm. (Using Simulacion 4.0 <a href="https://ucema.edu.ar/~jvarela/index_eng.htm">https://ucema.edu.ar/~jvarela/index_eng.htm</a>) </li> </ol> The models are applied to an cover crop experiment, where four farms tested cover crops for 5 years. The corresponding article is submitted to Soil Use and Management. The C input estimation zip is used to translate recorded yield and cover crop NDVI data to time series of C inputs used for the models.  <ul> <li>The Yasso20 model implementation is based on Yasso20 model code: <a href="https://github.com/YASSOmodel/Yasso20/tree/main">https://github.com/YASSOmodel/Yasso20/tree/main</a></li> <li>The SOMIC model implementation is based on: <a href="https://github.com/domwoolf/somic1">https://github.com/domwoolf/somic1</a></li> <li>The single pool model is as described in: <a href="https://doi.org/10.1016/j.still.2021.105204">https://doi.org/10.1016/j.still.2021.105204</a></li> <li>The DNDC.Can model version can be downloaded from: <a href="https://github.com/BrianBGrant/DNDCv.CAN">https://github.com/BrianBGrant/DNDCv.CAN</a></li> </ul> All the models are capable of simulating time series of soil C development over time, as influenced by C inputs, starting SOC and soil temperature and moisture. They are presented here for the purpose of further model development and comparison, not for making accurate forecasts.

opencc-by-4.0Aug 2021View details →

Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop

This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function \(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\), where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW).

opencc-by-4.0Jul 2022View details →

A Harmonised Dataset for Modelling Select Underutilised Crops Across EU

Version 2: the data was checked and refined against issues that were found in the columns bulk density and treatments.  Datasets are a compilation of information that are collected from various sources including books, research articles, databases, website articles, experts and local communities growing underutilised crops, etc. The focus was on extracting data from literature sources that were mainly peer reviewed, credible, and are primarily published in English language.  Taxonomy data: crop, variety or landrace Publication information: author, journal, year Geographic data: continent, country, site, latitude, longitude Soil data: Lower Depth, Clay (%), Sand (%), Silt (%), Texture, S.O., Bulk Density, Total carbon (%) Experimental data: Experiment duration (years), Seeding rate, Sowing depth, dates of each treatment /intervention. Production: yield, yield date, Above Ground Biomass (Agb), Agb date Phenology data: Growing Degree Days (sowing-to-harvest), emergence date, flowering date, maturity date Metadata: Study number, DOI, link  Credible sources containing experimental data, either from agronomy trials or meta-analysis containing experimental data were selected through literature search. As the focus of the work was to collate as much information as possible about agronomy trials of select underutilised crops, all data were collected and inserted into a shareable template on Google Docs.  List of crops:   Ceratonia siliqua, Carob, Underutilised legume tree Cichorium endivia, Endive, Underutilised vegetable Eragrostis tef, Teff, Underutilised cereal Ficus carica, Fig, Underutilised fruit tree Helianthus tuberosus, Jerusalem artichoke, Underutilised starchy roots/tubers Lentil Culinaris, Lentil, Pulse Lupinus albus, White lupin, Underutilised legume Malus domestica, Apple, Fruit Malus pumila, Apple, Fruit Medicago sativa, Alfaalfa, Underutilised legume Panicum miliaceum , Proso millet, Underutilised minor millet Pisum sativum, Pea, Legume Prunus avium, Cherry, Fruit Prunus domestica, Plum, Fruit Pyrus communis, Pear, Fruit Rheum rhaponticum, Rhubarb, Underutilised vegetable Setaria italic, Foxtail millet, Underutilised minor millet Trifolium repens , Clover, Underutilised legume Vicia faba, Faba bean, Underutilised legume Vigna unguiculata, Cowpea, Underutilised legume

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations

This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations

This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

opencc-by-4.0May 2017View details →

How does STICS crop model simulate crop growth and productivity under shade conditions ?

The STICS crop model has been used to predict the response of winter wheat to different shade conditions from an artificial shade treatment. Detailed information on the modeling procedure will be available in the following paper: “ How does STICS crop model simulate crop growth and productivity under shade conditions ” in Field Crops Research Journal. To launch a simulation with STICS, several input data files and parameters are required. The files used in this study are available below: The different inputs files required to launch a simulation: <ul> <li>The complete plant parameters file “Winter_wheat_adjusted_plt.txt”</li> <li>The weather data used “Weather_tot.txt”</li> </ul> The data are compiled at a daily time scale for each treatment: CS constant shade; PS periodic shade; NS no shade. In this data frame, “Temp_min” and “Temp_max” are the minimal and maximal air temperature in degree celcius; “Global_radiation” is the daily cumulated global radiation in MJ/m²; “Rainfall” is the daily cumulated rainfall in mm; “Wind” is the mean wind speed in m/s; and “Relative_humidity” is the mean air relative humidity in %. <ul> <li>The initial soil parameters “INI_2014-2015_ini.txt” and “INI_2015-2016_ini.txt” respectively for the growing season 2014-15 and 2015-16</li> <li>The general soil parameters for both growing season : “Soil_sols.txt”</li> <li>The technical itinerary “TEC_2015_tec.txt” and “TEC_2016_tec.txt” respectively for the growing season 2014-15 and 2015-16.</li> </ul>

opencc-by-4.0Dec 2023View details →

Fig. 2 in Spatial modeling of red spider mite Oligonychus punicae (Acari: Tetranychidae) in avocado crop

Fig. 2. Density maps of Oligonychus punicae Hirst, in avocado crop, by sampling month in plots of Temascaltepec municipality (Mexico). Red to orange to yellow to white indicates a gradual transition from high density of O. punicae to an absence of the species.

opencc-by-4.0Dec 2023View details →

Fig. 1 in Spatial modeling of red spider mite Oligonychus punicae (Acari: Tetranychidae) in avocado crop

Fig. 1. Density maps of Oligonychus punicae Hirst, in avocado crop, by sampling month in plots of Tenancingo municipality (Mexico). Red to orange to yellow to white indicates a gradual transition from high density of O. punicae to an absence of the species.

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Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale

This is the input dataset to a Python script (<a href="https://github.com/oskeng/MF-bio-grass">https://github.com/oskeng/MF-bio-grass</a>) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts. For more information about the dataset and the study, see the original article: Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy

opencc-zeroNov 2022View details →

opencc-by-4.0Jan 2014View details →

Output of the Global Crop Water Model GCWM

Crop water requirements of irrigated crops and crop water use of rainfed crops is calculated by the Global Crop Water Model (GCWM) for the period 1998-2002 based on a soil water balance performed in daily time steps.Crop yields of rainfed and irrigated crops are distinguished by downscaling yields reported in national and subnational agricultural statistics (Monfreda et al., 2008) by considering drought stress simulated for rainfed agriculture. Virtual water contents are calculated as ratio between crop water use and crop yield. Domestic virtual water flows are determined by calculating a balance between crop production and crop consumption at grid cell level and establishing flows from surplus cells (major production areas) to deficit cells (highly populated regions). Crop growing areas, sowing and harvest dates were derived from the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). Consequently, crop water use (blue and green) is computed for the crops wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee, cotton and unspecified other crops (other perennial crops, other annual crops, fodder grasses). For the calculation of crop yields, virtual water contents and virtual water flows we distinguish in addition the usage of maize, rye and sorghum for grain or forage. Virtual water flows are computed for crops traded mainly as primary commodities (all specific crops except of sugar cane, sugar beet and cotton). The data set has a spatial resolution of 5 arc-minutes by 5 arc-minutes. Format: ASCII-grids (global, 5 arc-minute) or text files (country data).

opencc-by-4.0Mar 2023View details →

Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Files used in initial submission of manuscript to Geoscientific Model Development. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.

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Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale

Open the record for dataset details and reuse information.

publicNov 2022View details →

opencc-by-4.0Dec 2019View details →

AgMIP's GGCMI Phase II: Crop model Emulators at 0.5 degree global resolution

GGCMI Phase II crop model polynomial emulator parameters. See https://www.geosci-model-dev.net/special_issue1062.html for more details. A0: no growing season adaptation. A1: with growing season adaptation.

opencc-by-sa-4.0Oct 2017View details →

Dataset of cropping cycles possibilities considered by the MERLIN model for urban microfarms in the London context

This dataset is related to the article “Reconciling economic viability and socio-ecological aspirations is possible but challenging for London urban microfarms” submitted to the journal Agronomy for Sustainable Development. The table presents the 274 cropping possibilities for the 36 crops considered by the MERLIN model for crop planning in London microfarms. For each crop and location (outdoors or in tunnel), the different possibilities of cropping cycles are described with month of planting the crop, the month of first harvest and the month of last harvest (some vegetables can be harvested various times during their production cycle).

opencc-by-4.0Sep 2018View details →

AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS potato

This is model output from LPJ-GUESS for potato as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set. The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019). References: Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015 Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017 Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8