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193 results for “crop modelling”
Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.
AWESOME Water and food demand (WATNEEDS crop model)
<p><span>In this record, we provide the results in terms of crop water requirements, calculated by the hydrological model WATNEEDS, and crop harvested area under the implemented crop replacement scenarios, that have been developed to optimize water use, crop production and agricultural value in present and future climate scenarios in the Nile Basin. These scenarios were designed, modeled and generated for the deliverable D2.3 of AWESOME WP2: <em>Dynamic and spatially distributed crop water use modeling.</em></span></p> <p><span>The analyses have been conducted at the mesoscale, with a specific focus has been reserved for Egypt, as it is particularly relevant for the AWESOME project because the demo sites of the innovative technologies are being build there. </span>In addition, we investigated present and future reallocation scenarios also for Ethiopia and Sudan, to cover the main cropland along the Nile River Basin.</p> <p><span>A baseline scenario is evaluated to quantify blue and green crop water requirements for the current climate conditions and current crop distribution (averaged for the period 2011-2016). </span>Future scenarios have been implemented using future climatic data for the average period around year 2050 (2048-2052) and year 2100 (2096-2100) under three different Representative Concentration Pathways (RCPs): RCP 4.5, RCP 2.6 and RCP 8.5.</p> <p> </p> <p><span>This record contains:</span></p> <p><span>- The Deliverable D2.3, where the optimization model and the calculation of the crop water requirements under different climatic scenarios are presented.</span></p> <p><span>- The results for each implemented scenario, in terms of harvested area and irrigation water consumption (.tif files and excel files), for the Nile River Basin and at the national level for each focus country (Ethiopia, Sudan and Egypt).</span></p> <p><span>- Description of the data (excel file and pdf file)</span></p> <p><span> </span></p>
MAgPIE model runs outputs: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>Each folder contains the fulldata.gdx and the configuration files for each MAgPIE run based on the nine crop impact models and 5 gcms used in the paper.</p>
Distillation of crop models to learn plant physiology theories using machine learning
<p>Full Paper available for download at LINK</p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Cover crops effect on global croplands modelled by LPJ-GUESS
<p>This file contains the input and output data for cover crop simulations on global scale by LPJ-GUESS. The site-level observations collected from the existing literature for model evaluation are also included. More details can be found in our Earth's Future (EF) paper: <a href="https://doi.org/10.1029/2022EF003142">https://doi.org/10.1029/2022EF003142</a></p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
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Data from: Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
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Data from: Quantifying temporal isolation: a modelling approach assessing the effect of flowering time differences on crop-to-weed pollen flow in sunflower
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GGCMI Phase 2: Crop model emulators of irrigation water demand (IWD)
<p>Polynomial emulators of irrigation water demand for globally gridded crop models from the GGCM phase 2 project. A0: no growing season adaptation. A1: with growing season adaptation. See: https://doi.org/10.5194/gmd-2019-365 and https://doi.org/10.5194/gmd-13-2315-2020 for more details. </p> <p>Note: the GEPIC emulator for maize and soy (A0) is not available. </p>
Data from: Evaluating predictive performance of statistical models explaining wild bee abundance in a mass-flowering crop
<p>Wild bee populations are threatened by current agricultural practices in many parts of the world, which may put pollination services and crop yields at risk. Loss of pollination services can potentially be predicted by models that link bee abundances with landscape-scale land-use, but there is little knowledge on the degree to which these statistical models are transferable across time and space. This study assesses the transferability of models for wild bee abundance in a mass-flowering crop across space (from one region to another) and across time (from one year to another). The models used existing data on bumblebee and solitary bee abundance in winter oilseed rape fields, together with high-resolution land-use crop-cover and semi-natural habitats data, from studies conducted in five different regions located in four countries (Sweden, Germany, Netherlands, and the UK), in three different years (2011, 2012, 2013). We developed a hierarchical model combining all studies and evaluated the transferability using cross-validation. We found that both the landscape-scale cover of mass-flowering crops and permanent semi-natural habitats, including grasslands and forests, are important drivers of wild bee abundance in all regions. However, while the negative effect of increasing mass-flowering crops on the density of the pollinators is consistent between studies, the direction of the effect of semi-natural habitat is variable between studies. The transferability of these statistical models is limited, especially across regions, but also across time. Our study demonstrates the limits of using statistical models in conjunction with widely available land-use crop-cover classes for extrapolating pollinator density across years and regions, likely in part because input variables such as cover of semi-natural habitats poorly capture variability in pollinator resources between regions and years.</p>
Data from: Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits
The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.
Conservation status assessment of banana crop wild relatives using species distribution modelling
<p><strong>Aim: </strong>Crop wild relatives (CWR) are an essential source of genetic material for the improvement of certain traits in related crop species. Despite their importance, increasing public, scientific and political support, large gaps exist in the amount of genetic material collected and conserved of many CWR. Here, we construct a dataset on the distribution of wild banana species (Musa spp.) and assess their risk and conservation status. We deal with the questions (1) what areas are potentially suitable for wild banana species (2) how much of the wild banana diversity is currently at risk or insufficiently conserved ex- and in-situ.</p> <p><span><strong>Location: </strong>Native distribution area of wild banana species, ranging from the northeastern states of India to northeastern Australia.</span></p> <p><span><strong>Methods:</strong> We assessed the potential environmental range of wild species using a species distribution modelling approach with MaxEnt. Extinction risk was evaluated following IUCN criterion B and the <i>ex- </i>and <i>in-situ</i> conservation status was assessed using an indicator for biodiversity and sustainable development targets.</span></p> <p><span><strong>Results:</strong> We found that 11 out of 59 assessed species can be considered as vulnerable and nine as endangered. Highest species richness was found along the border of south China and northern Vietnam, in the northeastern states of India and on the Malayan peninsula. Our distribution modelling approach indicates that the northern Indo-Burmese region has the highest environmental suitability for most wild banana species and that lowland rainforests in general are highly suitable for bananas. Assessment of <i>in</i>- and <i>ex-situ</i> conservation status indicates that 56 out of 59 assessed species are currently insufficiently conserved <i>ex-situ</i> and that 49 are of high priority for further conservation. Additional <i>in-situ</i> conservation is of high priority for six species and of medium priority for 40 species.</span></p> <p><span><strong>Main Conclusions:</strong> To date, little of the banana CWR are sufficiently conserved both <i>in- </i>and <i>ex-situ</i>.</span></p>
MAgPIE model input data sets: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>These MAgPIE input data sets include harmonized crop yield projections from several crop models (9 crop models and 5 climate models). Additionally, regional, validation, and calibration data sets are also reported.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PROMET rice simulations
This data set contains output data from simulations with the model PROMET 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, planting day, maturity day, anthesis day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the ERA-Interim (Dee et al. 2011) 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 (A0= 'none', A1='regain original growing season').
Modelized harvest dates (2005-2023) by crop and UTM 10x10 km grid in peninsular Spain
<p><strong>Predictor data</strong></p> <ul> <li>Meteorological data sets were obtained from CHELSA (www.chelsa-climate.org) and the Spanish Meteorological Agency (www.aemet.es).</li> <li>Cartographic bases (administrative boundaries, elevation model, UTM grid) were obtained through the services provided by the Spatial Data Infrastructure of Spain (www.idee.es).</li> <li>Harvest dates were provided by Agroseguro (www.agroseguro.es), a management entity responsible for the management of agricultural insurance in the name and on behalf of insurance companies that are part of the Spanish co-insurance pool. </li> </ul> <p> A GLMM was generated to predict the harvest week of the year for barley and wheat in mainland Spain by 10x10 km UTM grid.</p> <p><strong>Columns </strong>in the dataset are:</p> <ul> <li>utmcode</li> <li>year</li> <li>wheat_harvest_week</li> <li>barley_harvest_week</li> </ul> <p>Harvest week according to ISO 8601-1:2019 (<a href="https://www.iso.org/iso-8601-date-and-time-format.html">ISO - ISO 8601 — Date and time format</a>)</p>
Data from: Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits
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Data from: Evaluating predictive performance of statistical models explaining wild bee abundance in a mass-flowering crop
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Conservation status assessment of banana crop wild relatives using species distribution modelling
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Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency
GEO Series GSE280344. Zea mays. 57 samples. Type: Expression profiling by high throughput sequencing.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.