Expected effects of climate change on the production and water use of crop rotation management reproduced by crop model ensemble for Czech Republic sites

https://doi.org/10.1016/j.eja.2021.126446Get rights and content

Highlights

  • Models were applied for 2 rotations, 4 crops, 2 soils, 7 scenarios, 3 locations.

  • The potential for yield increase in the future is higher within good soils.

  • A higher annual actual evapotranspiration is expected in the future.

  • Results could be used in a wider context also for Central Europe.

Abstract

Crop rotation, fertilization and residue management affect the water balance and crop production and can lead to different sensitivities to climate change. To assess the impacts of climate change on crop rotations (CRs), the crop model ensemble (APSIM,AQUACROP, CROPSYST, DAISY, DSSAT, HERMES, MONICA) was used. The yields and water balance of two CRs with the same set of crops (winter wheat, silage maize, spring barley and winter rape) in a continuous transient run from 1961 to 2080 were simulated. CR1 was without cover crops and without manure application. Straw after the harvest was exported from the fields. CR2 included cover crops, manure application and crop residue retention left on field. Simulations were performed using two soil types (Chernozem, Cambisol) within three sites in the Czech Republic, which represent temperature and precipitation gradients for crops in Central Europe. For the description of future climatic conditions, seven climate scenarios were used. Six of them had increasing CO2 concentrations according RCP 8.5, one had no CO2 increase in the future. The output of an ensemble expected higher productivity by 0.82 t/ha/year and 2.04 t/ha/year for yields and aboveground biomass in the future (2051–2080). However, if the direct effect of a CO2 increase is not considered, the average yields for lowlands will be lower. Compared to CR1, CR2 showed higher average yields of 1.26 t/ha/year for current climatic conditions and 1.41 t/ha/year for future climatic conditions. For the majority of climate change scenarios, the crop model ensemble agrees on the projected yield increase in C3 crops in the future for CR2 but not for CR1. Higher agreement for future yield increases was found for Chernozem,while for Cambisol, lower yields under dry climate scenarios are expected. For silage maize, changes in simulated yields depend on locality. If the same hybrid will be used in the future, then yield reductions should be expected within lower altitudes. The results indicate the potential for higher biomass production from cover crops, but CR2 is associated with almost 120 mm higher evapotranspiration compared to that of CR1 over a 5-year cycle for lowland stations in the future, which in the case of the rainfed agriculture could affect the long-term soil water balance. This could affect groundwater replenishment, especially for locations with fine textured soils, although the findings of this study highlight the potential for the soil water-holding capacity to buffer against the adverse weather conditions.

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)

  • T. Palosuo et al.

    Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models

    Eur. J. Agron.

    (2011)
  • A. Rodríguez et al.

    Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations

    Agric. For. Meteorol.

    (2019)
  • R.P. Rötter et al.

    Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models

    Field Crops Res.

    (2012)
  • M. Ruiz-Ramos et al.

    Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment

    Agric. Syst.

    (2018)
  • C.O. Stöckle et al.

    CropSyst, a cropping systems simulation model

    Eur. J. Agron.

    (2003)
  • M. Trnka et al.

    European corn borer life stage model: regional estimates of pest development and spatial distribution under present and future climate

    Ecol. Model

    (2007)
  • M. Trnka et al.

    Simple snow cover model for agrometeorological applications

    Agric. For. Meteorol.

    (2010)
  • O. Urban et al.

    Combined effects of drought and high temperature on photosynthetic characteristics in four winter wheat genotypes

    Field Crops Res.

    (2018)
  • J.W. White et al.

    Methodologies for simulating impacts of climate change on crop production

    Field Crops Res.

    (2011)
  • S. Asseng et al.

    Uncertainty in simulating wheat yields under climate change

    Nat. Clim. Chang.

    (2013)
  • S. Asseng et al.

    Rising temperatures reduce global wheat production

    Nat. Clim. Chang.

    (2015)
  • B. Basso et al.

    Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized?

    PLOS One

    (2015)
  • S. Bassu et al.

    How do various maize crop models vary in their responses to climate change factors?

    Glob. Chang. Biol.

    (2014)
  • G.D. Behnke et al.

    Cover crop rotations affect greenhouse gas emissions and crop production in Illinois, USA

    Field Crop Res.

    (2019)
  • M. Dubrovský et al.

    Effect of climate change and climate variability on crop yields

  • M. Dubrovský et al.

    High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling

    Clim. Chang.

    (2004)
  • M. Dubrovský et al.

    Uncertainties in climate change scenarios for the Czech Republic

    Clim. Res.

    (2005)
  • J. Eitzinger et al.

    Regional climate change impacts on agricultural crop production in Central and Eastern Europe–hotspots, regional differences and common trends

    J. Agric. Sci.

    (2013)
  • G.J. Fitzgerald et al.

    Elevated atmospheric [CO2] can dramatically increase wheat yields in semi‐arid environments and buffer against heat waves

    Glob. Change Biol.

    (2016)
  • M. Foulkes et al.

    Genetic progress in yield potential in wheat: recent advances and future prospects

    Can. J. Agric. Sci.

    (2007)
  • A. Gobin

    Impact of heat and drought stress on arable crop production in Belgium

    Nat. Hazards Earth Syst. Sci.

    (2012)
  • A. Gobin et al.

    Variability in the water footprint of arable crop production across European regions

    Water

    (2017)
  • Cited by (5)

    View full text