<scp>CERES</scp>‐Maize model‐based simulation of climate change impacts on maize yields and potential adaptive measures in Heilongjiang Province, China

Lin, Yi; Wu, Wenxiang; Ge, Quansheng

doi:10.1002/jsfa.7024

Cited by 37 publications

(36 citation statements)

References 59 publications

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“…Constantin et al [17] with AqYield and STICs models, reported NRMSE of 15% and 18% respectively. Using CERES-Maize, Lin et al referred NRMSE near 11% [18], and Ma et al [69] reported NRMSE ranging 6% to 12% and 5% to 7% when using, respectively, the DSAAT-CERES and the RZWQ-CERES models. Ko et al [70] reported RMSE of 0.5 and 0.87 t¨ha´1 but representing NRMSE of 7% and 11% of the average observed yields.…”

Section: Water-yield Relations and Yield Predictionsmentioning

confidence: 99%

“…These include water-yield functions and crop growth and yield models. Applications of crop yield models to maize are numerous (e.g., [15][16][17][18]). However, these models are very demanding in terms of data and parameterization and may not be better than adopting a combination of a water balance simulation model with a water-yield function.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Maize Transpiration, Water Use and Productivity for Developing Improved Supplemental Irrigation Schedules in Western Uruguay to Cope with Climate Variability

Giménez

Petillo

Paredes

et al. 2016

Water

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Abstract:Various maize irrigation treatments including full and deficit irrigation were used to calibrate and validate the soil water balance and irrigation scheduling model SIMDualKc at Paysandú, western Uruguay. The model adopts the dual crop coefficient approach to partition actual evapotranspiration (ET c act ) into actual transpiration (T c act ) and soil evaporation (E s ). Low errors of estimation were obtained for simulating soil water content (Root mean square errors (RMSE) ď 0.014 cm 3¨c m´3 with calibrated parameters, and RMSE ď 0.023 cm 3¨c m´3 with default parameters). The ratio E s /T c act ranged from 26% to 33% and E s /ET c act varied from 20% to 25%, with higher values when the crop was stressed offering less soil coverage. Due to rainfall regime, runoff and deep percolation were quite large. The Stewarts phasic model was tested and used to predict maize yield from T c act with acceptable errors, in the range of those reported in literature. Water productivity values were high, ranging 1.39 to 2.17 kg¨m´3 and 1.75 to 2.55 kg¨m´3 when considering total water use and crop ET, respectively. Using a 22-year climatic data series, rainfed maize was assessed with poor results for nearly 40% of the years. Differently, alternative supplemental irrigation schedules assessed for the dry and very dry years have shown good results, particularly for mild deficit irrigation. Overall, results show appropriateness for using SIMDualKc to support the irrigation practice.

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Section: Water-yield Relations and Yield Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Maize Transpiration, Water Use and Productivity for Developing Improved Supplemental Irrigation Schedules in Western Uruguay to Cope with Climate Variability

Giménez

Petillo

Paredes

et al. 2016

Water

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“…Similar results were also obtained by Challinor et al and Simelton et al Hence, the use of different sowing dates as an adaptation approach found that delaying the sowing date will be advantageous in order to mitigate the adverse effects of climate change by avoiding thermal stress at the growth period and obtain higher yield in all of the study areas in next decades. There are many studies which have focused on the efficiency of agronomic adaptation and mitigation approaches in relation to climate‐induced yield losses in different locations …”

Section: Discussionmentioning

confidence: 99%

“…Results of many studies suggested that the impacts of climate change without using adaptation and mitigation strategies may create considerable problems related to agricultural production and economies . On the other hand, various studies have been conducted to investigate the role of the agronomic mitigation or adaptation strategies to climate change . However, it should be noted that the efficacy of adaptation and mitigation strategies in each region is different and no single approach can give the required information for adapting agriculture in a changing climate, hence it is possible to use the vulnerability index to identify which areas and/or years have higher or lower response to these strategies and which adaptation and mitigation efforts are likely to be most effective and thus how one may prioritise them .…”

Section: Introductionmentioning

confidence: 99%

Vulnerability of maize production under future climate change: possible adaptation strategies

2016

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“…The results showed that the duration of reproductive phases in maize from emergence to flowering and physiological maturity would be shortened under future climate change, and yield would reduce by 11 to 46% from 2011 to 2099 relative to 1981 to 2010 (Lin et al, 2015). Additionally, maize production would not benefit significantly from increased CO 2 fertilization.…”

Section: Impact Of Climate Change On Maize Yield Using Dssat Ceres-mamentioning

confidence: 99%

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

Chisanga¹,

Phiri²,

Chinene³

2017

Sci. Res. Essays

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Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.

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CERES‐Maize model‐based simulation of climate change impacts on maize yields and potential adaptive measures in Heilongjiang Province, China

Cited by 37 publications

References 59 publications

Predicting Maize Transpiration, Water Use and Productivity for Developing Improved Supplemental Irrigation Schedules in Western Uruguay to Cope with Climate Variability

Predicting Maize Transpiration, Water Use and Productivity for Developing Improved Supplemental Irrigation Schedules in Western Uruguay to Cope with Climate Variability

Vulnerability of maize production under future climate change: possible adaptation strategies

Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

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