Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios

A regional climate change model (PRECIS) for China, developed by the UK's Hadley Centre, was used to simulate China's climate and to develop climate change scenarios for the country. Results from this project suggest that, depending on the level of future emissions, the average annual temperature increase in China by the end of the twenty-first century may be between 3 and 4 °C. Regional crop models were driven by PRECIS output to predict changes in yields of key Chinese food crops: rice, maize and wheat. Modelling suggests that climate change without carbon dioxide (CO 2 ) fertilization could reduce the rice, maize and wheat yields by up to 37% in the next 20–80 years. Interactions of CO 2 with limiting factors, especially water and nitrogen, are increasingly well understood and capable of strongly modulating observed growth responses in crops. More complete reporting of free-air carbon enrichment experiments than was possible in the Intergovernmental Panel on Climate Change's Third Assessment Report confirms that CO 2 enrichment under field conditions consistently increases biomass and yields in the range of 5–15%, with CO 2 concentration elevated to 550 ppm Levels of CO 2 that are elevated to more than 450 ppm will probably cause some deleterious effects in grain quality. It seems likely that the extent of the CO 2 fertilization effect will depend upon other factors such as optimum breeding, irrigation and nutrient applications.

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Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa

Agriculture & Food Security ◽

10.1186/s40066-020-00283-5 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Priscilla Ntuchu Kephe ◽

Kingsley Kwabena Ayisi ◽

Brilliant Mareme Petja

Keyword(s):

Climate Change ◽

South Africa ◽

Crop Production ◽

Input Data ◽

Production Management ◽

Hybrid Approach ◽

Climate Change Scenarios ◽

Crop Models ◽

Crop Simulation ◽

Data Limitations

AbstractA broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. We advocate a hybrid approach for obtaining input data for model calibration and validation. Recommended methods depending on the intended outputs and end use of model results include remote sensing, field, and greenhouse experiments, secondary data, engaging with farmers to model actual on-farm conditions. Thus, employing more than one method of data collection for input data for models can reduce the challenges faced by crop modellers due to the unavailability of data. The future of modelling depends on the goodness and availability of the input data, the readiness of modellers to cooperate on modularity and standardization, and potential user groups’ ability to communicate.

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Evolution of Köppen-Geiger's climate classification in the Basque Country in the context of climate change

10.5194/ems2021-248 ◽

2021 ◽

Author(s):

Roberto Hernandez ◽

Maialen Martija ◽

Jose Daniel Gomez de Segura ◽

Santiago Gaztelumendi

Keyword(s):

Climate Change ◽

Spatial Resolution ◽

Climate Models ◽

Basque Country ◽

Delta Method ◽

Historical Period ◽

Climate Change Scenarios ◽

Climate Data ◽

Adaptation To Climate Change ◽

Climate Classification

The K&#246;ppen-Geiger system classifies climate into five main classes and thirty sub-types, based on threshold values and seasonality of monthly air temperature and precipitation. Its aim is to map empirically biome distributions around the world. In this paper, we analyze the evolution of this climate classification in the Basque Country for the historical period 1971-2000 and for three periods of future conditions: 2011-2040, 2041-2070, 2071-2100 projected by climate change scenarios.The baseline data consists of high spatial resolution climate data available in the Basque Country. Different results from the KLIMATEK project on Adaptation to Climate Change are used (promoted by IHOBE -Basque Government-). These results have been generated for the RCP4.5 and RCP8.5 experiments, based on simulations carried out with regional climate models within the framework of the Euro-CORDEX project. Once the indicators were obtained at a spatial resolution of 12km x 12km, they were also obtained at a resolution of 1km x 1km, using the delta method. This process is carried out for each of the Euro-CORDEX models, so that an averaged result is finally provided, together with a statistic on its dispersion.The evolution of the K&#246;ppen-Geiger maps is accompanied by other bioclimatic indices (aridity/continentality) and diagrams, which reinforce the pre-diagnosis of future climate conditions in the Basque Country. In addition, the dispersion of the models used is taken into account in the analysis of results, showing the most and least unfavorable scenarios.These indices are applicable in impact and vulnerability analysis studies in sectors such as agriculture and landscape. Although they have received less attention than other indices of climate extremes, they nevertheless reflect concepts that are relatively simple to understand by the general public and are therefore also useful in the task of disseminating the consequences of climate change.

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Same soil - different climate: crop model inter-comparison with lysimeter data of translocated monoliths

10.5194/ismc2021-45 ◽

2021 ◽

Author(s):

Jannis Groh ◽

Horst H. Gerke ◽

Keyword(s):

Climate Change ◽

Model Performance ◽

Predictive Ability ◽

Crop Model ◽

Climatic Conditions ◽

Climate Change Scenarios ◽

Soil Ecosystem ◽

Site Specific ◽

Climate Conditions ◽

Crop Models

Crop model comparisons have mostly been carried out to test predictive ability under previous climate conditions and for soils of the same location. However, the ability of individual agricultural models to predict the effects of changes in climatic conditions on soil-ecosystems beyond the range of site-specific variability is unknown. The objective of this study was to test the predictive ability of agroecosystem models using weighable lysimeter data for the same soil under changing climatic conditions and to compare simulated plant growth and soil-ecosystem response to climate change between these models. To achieve this, data from the TERENO-SOILCan lysimeters-network for a soil-ecosystem at the original site (Dedelow) and data from the lysimeters with Dedelow soil monoliths transferred to Bad Lauchst&#228;dt and Selhausen were analysed. The transfer of the soils took place to a drier and warmer location (Bad Lauchst&#228;dt) and to a warmer and wetter location (Selhausen) compared to the original location of the soils in Dedelow with the same crop rotation. After model calibration for data from the original Dedelow site, crop growth and soil water balances of transferred Dedelow soil monoliths were predicted using the site-specific boundary conditions and compared with the observations at Selhausen and Bad Lauchst&#228;dt. The overall simulation output of the models was separated into a plant-related part, ecosystem-productivity (grain yield, biomass, LAI) and an environmental part, ecosystem-fluxes (evapotranspiration, net-drainage, soil moisture). The results showed that when the soil was transferred to a drier region, the agronomic part of the crop models predicted well, and when the soil was moved to wetter regions, the environmental flow part of the models seemed to predict better. The results suggest that accounting for climate change scenarios, more consideration of soil properties and testing model performance for conditions outside the calibrated range and site-specific variability will help improve the models.

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Projecting maize yield under local‐scale climate change scenarios using crop models: Sensitivity to sowing dates, cultivar, and nitrogen fertilizer rates

Food and Energy Security ◽

10.1002/fes3.231 ◽

2020 ◽

Vol 9 (4) ◽

Cited By ~ 1

Author(s):

Charles B. Chisanga ◽

Elijah Phiri ◽

Vernon R. N. Chinene ◽

Lydia M. Chabala

Keyword(s):

Climate Change ◽

Nitrogen Fertilizer ◽

Maize Yield ◽

Local Scale ◽

Climate Change Scenarios ◽

Crop Models ◽

Sowing Dates ◽

Fertilizer Rates

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The application of agricultural land rating and crop models to CO2 and climate change issues in Northern regions: the Mackenzie Basin case study

Agricultural and Food Science ◽

10.23986/afsci.72740 ◽

1996 ◽

Vol 5 (3) ◽

pp. 351-365 ◽

Cited By ~ 3

Author(s):

Michael Brklacich

Keyword(s):

Climate Change ◽

Agricultural Sector ◽

Agricultural Land ◽

Climate Change Scenarios ◽

Commercial Agriculture ◽

Crop Models ◽

Physical Constraints ◽

Mackenzie Basin ◽

Northern Regions

The Mackenzie Basin in northwestern Canada covers approximately 1.8 million km2 and extends from 52°N to 70°N. Much of the Basin is currently too cool and remote from markets to support a viable agricultural sector, but the southern portion of the Basin has the physical potential to support commercial agriculture. This case study employed agricultural land rating and crop models to estimate the degree to which a CO2 -induced global warming might alter the physical potential for commercial agriculture throughout the Basin. The two climate change scenarios considered in this analysis would relax the current constraints imposed by a short and cool frost-free season, but without adaptive measures, drier conditions and accelerated crop development rates were estimated to offset potential gains stemming from elevated CO2 levels and warmer temperatures. In addition to striving for a better understanding of the extent to which physical constraints on agriculture might be modified by climate change, there is a need to expand the research context and to consider the capacity of agriculture to adapt to altered climates.

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