Expected effects of climate change on the production and water use of crop rotation management reproduced by crop model ensemble for Czech Republic sites
Introduction
The design and management of cropping systems play important roles in water and nutrient dynamics, resource use efficiency and crop production (e.g., Lopez-Bellido et al., 2000; Berzsenyi et al., 2000) as well as in the long-term evolution of soil carbon and nitrogen stocks, thus affecting greenhouse gas emissions (e.g., Malhi and Lemke, 2007; Behnke and Villamil, 2019). Although crop rotation (CR) management is seen as an important measure to adapt to and mitigate climate change (e.g., Olesen et al., 2011), most studies on climate change impact so far use single-year simulations and/or single-crop assessments (White et al., 2011, Webber et al., 2018). However, if simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies cannot be properly evaluated (Basso et al., 2015). Therefore, the integrated assessment of impacts, adaptation and mitigation options under current and future climatic conditions requires a continuous long-term analysis (e.g., simulations) of CRs or crop sequences (e.g., Kollas et al., 2015; Ewert et al., 2015). In this way, carry-over effects at both short-term (annual) and long-term (decadal) scales (Öztürk et al., 2018) could be taken into account. Such insight into the simulated soil-crop-atmosphere system is crucial for the design and assessment of various optimization measures (e.g., fertilization, cover crop strategy, crop and cultivar selection), and this is valid for both current and projected future climates. Simulation could be done by the so-called in silico regime using crop growth models, but it is time and technically demanding (e.g., for the necessary calibration and validation for each of the included crops/cultivars).
Another important feature of results based on crop simulation models is seen in the inherent differences among outputs from individual crop growth models. Such variability is caused by many factors, including model complexity, parameterization, and calibration (Palosuo et al., 2011, Rötter et al., 2012, Kostková et al., 2021), which contribute to some level of uncertainty (e.g., Rötter et al., 2012). To assess, handle and/or reduce such uncertainty, several crop models can be applied simultaneously as an ensemble (e.g., Asseng et al., 2013, Asseng et al., 2015; Bassu et al., 2014; Ruiz-Ramos et al., 2018; Wallach et al., 2018; Webber et al., 2018; Rodríguez et al., 2019; Liu et al., 2019). Building an ensemble of crop models to simulate CRs is a particular challenge since all models have to be able to simulate all included crops. This constrains the number of suitable models for certain crop combinations in comparison to that of large ensembles applied just for a single major staple crop (e.g., wheat or maize). To achieve a reasonable crop model ensemble size, large teams and international cooperation among modelers are usually required.
The main objective of this study was to quantify the effects of projected climate change on crop production and water balance for two selected CRs representing intensive vs. conservation agricultural production systems within the Czech Republic. Namely, to evaluate the results from continuous uninterrupted simulations of CRs until 2080 by the ensemble of seven crop growth models with the assessment of agreement or uncertainty for predicted results. Particular aims were to assess the impacts within particular crops (winter wheat, silage maize, spring barley and winter oilseed rape) but especially for contrasting CRs as a whole, with a focus on yields, total aboveground biomass production (both average levels and variability) and the expected aspects of water balance, water stress and water use efficiency for contrasting soils and representative stations for the Czech Republic and in a wider context also for Central Europe. The simulated effects on soil organic carbon and nutrient dynamics will be separately analyzed in an upcoming paper. Since changes in soil hydraulic properties due to changes in soil organic matter were not considered by any of the participating models and effects of modified nitrogen dynamics were compensated by automatic nitrogen fertilization algorithms, the effect of climate change and crop rotation on crop yield and water balance could be analyzed independently in this study.
Section snippets
Crop model ensemble and simulation scheme
The ensemble used within this study is based on seven crop growth models (APSIM, AQUACROP, CROPSYST, DAISY, DSSAT, HERMES and MONICA). The list of versions used and relevant references are summarized in Table 1. The ensemble was applied for two CRs, including four field crops (winter wheat, spring barley, silage maize, winter oilseed rape) in combination with two soil types (Chernozem, Cambisol), seven climate scenarios and three experimental locations (Lednice, Věrovany, Domanínek). A scheme
Expected yields and productivity
The results show a higher agreement for future yield increase (expectations for 2051–2080 against 1962–1990) with good soils (Chernozem versus Cambisol, Fig. 4). For CR1 (i.e., without cover crops and manure application), there is low confidence about possible higher yields of simulated crops in the future (for all stations and the majority of scenarios). For CR2 (i.e., with cover crops and manure application) in connection with Chernozems and the majority of climate change scenarios used, the
Discussion
Increasing projections for C3 crop yields under climate change across all included stations and silage maize in Domanínek (with higher confidence under CR2) by using the crop model ensemble could be based on several aspects. One reason could be the effect of higher temperatures, including its connection with the shift in the timing of agronomic operations and phenology. The separate quantification of this agronomic timing and phenology was not the goal of this study, but Hlavinka et al. (2015)
Conclusions
The expected yields and water balance aspects of four important field crops in connection with two example crop rotations in the Czech Republic were evaluated under future climatic conditions. The strength of this study is based on using uninterrupted crop rotation (CR) simulations by a crop model ensemble composed of seven members.
The crop model ensemble projected an increase in average production under climate change with lower confidence for the intensive production system (CR1) than for the
CRediT authorship contribution statement
Pohanková, E.: Conceptualization, model DAISY, Hlavinka, P.: Conceptualization, model HERMES, Kersebaum, K.C.: Conceptualization, model HERMES, Rodríguez, A.: EOA index, model DSSAT, Balek, J.: Programmer, data processing, Bednařík, M.: Model HERMES, Dubrovský M.: Climate scenarios, Gobin, A.: Model AQUACROP, Hoogenboom, G.: model DSSAT, Moriondo, M. - model CROPSYST, Nendel, C.: model MONICA, Olesen, J. E. Conceptualization, Rötter, R. Conceptualization, Ruiz-Ramos, M.: model DSSAT Shelia, V.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The study was supported by the project “SustES - Adaptation Strategies for Sustainable Ecosystem Services and Food Security under Adverse Environmental Conditions” project no. CZ.02.1.01/0.0/0.0/16_019/0000797, the Spanish INIA and AEI agencies (grant MACSUR02- APCIN2016-0005-00-00), and the Comunidad de Madrid (Spain) and Structural Funds (ERDF and ESF) 2014–2020 (project AGRISOST-CM S2018/BAA-4330).
References (64)
- et al.
Daisy: an open soil-crop-atmosphere system model
(2000) - et al.
Effect of crop rotation and fertilisation on maize and wheat yields and yield stability in a long-term experiment
Eur. J. Agron.
(2000) - et al.
Crop modelling for integrated assessment of risk to food production from climate change
Environ. Model. Softw.
(2015) Weather related risks in Belgian arable agriculture
Agric. Syst.
(2018)- et al.
APSIM–evolution towards a new generation of agricultural systems simulation
Environ. Model. Softw.
(2014) - et al.
The DSSAT cropping system model
Eur. J. Agron.
(2003) - et al.
Crop rotation modelling – a European model intercomparison
Eur. J. Agron.
(2015) - et al.
Tillage, crop residue and N fertilizer effects on crop yield, nutrient uptake, soil quality and nitrous oxide gas emissions in a second 4-yr rotation cycle
Soil Tillage Res.
(2007) - et al.
The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics
Ecol. Model.
(2011) - et al.
Impacts and adaptation of European crop production systems to climate change
Eur. J. Agron.
(2011)
Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models
Eur. J. Agron.
Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations
Agric. For. Meteorol.
Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models
Field Crops Res.
Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment
Agric. Syst.
CropSyst, a cropping systems simulation model
Eur. J. Agron.
European corn borer life stage model: regional estimates of pest development and spatial distribution under present and future climate
Ecol. Model
Simple snow cover model for agrometeorological applications
Agric. For. Meteorol.
Combined effects of drought and high temperature on photosynthetic characteristics in four winter wheat genotypes
Field Crops Res.
Methodologies for simulating impacts of climate change on crop production
Field Crops Res.
Uncertainty in simulating wheat yields under climate change
Nat. Clim. Chang.
Rising temperatures reduce global wheat production
Nat. Clim. Chang.
Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?
PLOS One
How do various maize crop models vary in their responses to climate change factors?
Glob. Chang. Biol.
Cover crop rotations affect greenhouse gas emissions and crop production in Illinois, USA
Field Crop Res.
Effect of climate change and climate variability on crop yields
High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling
Clim. Chang.
Uncertainties in climate change scenarios for the Czech Republic
Clim. Res.
Regional climate change impacts on agricultural crop production in Central and Eastern Europe–hotspots, regional differences and common trends
J. Agric. Sci.
Elevated atmospheric [CO2] can dramatically increase wheat yields in semi‐arid environments and buffer against heat waves
Glob. Change Biol.
Genetic progress in yield potential in wheat: recent advances and future prospects
Can. J. Agric. Sci.
Impact of heat and drought stress on arable crop production in Belgium
Nat. Hazards Earth Syst. Sci.
Variability in the water footprint of arable crop production across European regions
Water
Cited by (5)
Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives
2022, Plant CommunicationsCitation Excerpt :Crop growth models (CGMs) such as WOFOST (CWFS&WUR, the Netherlands) (Van Diepen et al., 1989), DSSAT (University of Florida, USA) (Jones et al., 2003), Agricultural Production System Simulator (APSIM) (CSIRO, Australia) (Keating et al., 2003), STICS (INRA, France) (Brisson et al., 2003), and CropGrow (NJAU, China) (Zhu et al., 2020c) have also received widespread attention. By integrating the interactions among crop genetic potential, environmental effects, and cultivation techniques, crop growth models can simulate the growth and development of crops under different conditions, effectively predicting plant responses to stress (Tang et al., 2009), simulating the effects of climate extremes on crop yield (Pohanková et al., 2022), predicting the performance of varieties in target environments (Lamsal et al., 2017), and explaining how G × E interactions affect crop productivity (Messina et al., 2018). Therefore, CGMs may provide decision support for precision agriculture, variety selection, and management optimization (Kherif et al., 2022).
The Agricultural Potential of a Region with Semi-Dry, Warm and Temperate Subhumid Climate Diversity through Agroecological Zoning
2023, Sustainability (Switzerland)THE IMPACT OF CLIMATE CHANGE ON CROP FARMING IN THE DRAINED LANDS OF THE EUROPEAN NON-CHERNOZEM REGION OF RUSSIA: VULNERABILITY AND ADAPTATION ASSESSMENT
2023, Sel'skokhozyaistvennaya Biologiya