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

2020 ◽  
Vol 9 (4) ◽  
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

scholarly journals Assessment of Climate Change Impact on Crop yield and Evaluation of Coping Strategy using Crop Growth Simulation Model

2020 ◽  
Vol 15 (1) ◽  
pp. 106-122
Author(s):  
J. Alam ◽  
R. K. Panda
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Simulation Model ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Co2 Concentration ◽  
Maize Yield ◽  
Crop Growth ◽  
Climate Change Scenarios ◽  
Area Index

 Any change in climate will have implications for climate-sensitive systems such as agriculture, forestry and some other natural resources. Changes in solar radiation, temperature and precipitation will produce changes in crop yields and hence economics of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Maize model of DSSAT v4.0 was used to simulate the maize yield of the region under climate change scenarios using the historical weather data at Kharagpur (1977-2007), Damdam (1974-2003) and Purulia (1986-2000), West Bengal, India. The model was calibrated using the crop experimental data, climate data and soil data for two years (1996-1997) and was validated by using the data of the year 1998 at Kharagpur. The change in values of weather parameters due to climate change and its effects on the maize crop growth and yield was studied. It was observed that increase in mean temperature and leaf area index have negative impacts on maize yield. When the maximum leaf area index increased, the grain yield was found to be decreased. Increase in CO2 concentration with each degree incremental temperature decreased the grain yield but increase in CO2 concentration with fixed temperature increased the maize yield. Adjustments were made in the date of sowing to investigate suitable option for adaptation under the future climate change scenarios. Highest yield was obtained when the sowing date was advanced by a week at Kharagpur and Damdam whereas for Purulia, the experimental date of sowing was found to be beneficial.

Global contribution of climate variability and trends to maize yield change in observations and crop models during 1980-2010

2020 ◽  
Author(s):  
Xiaomeng Yin ◽  
Guoyong Leng
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Statistical Model ◽  
Yield Loss ◽  
Climate Change Impacts ◽  
Maize Yield ◽  
Climate Impacts ◽  
Future Climate Change ◽  
Crop Models ◽  
Process Based Models

<p>Understanding historical crop yield response to climate change is critical for projecting future climate change impacts on yields. Previous assessments rely on statistical or process-based crop models, but each has its own strength and weakness. A comprehensive comparison of climate impacts on yield between the two approaches allows for evaluation of the uncertainties in future yield projections. Here we assess the impacts of historical climate change on global maize yield for the period 1980-2010 using both statistical and process-based models, with a focus on comparing the performances between the two approaches. To allow for reasonable comparability, we develop an emulator which shares the same structure with the statistical model to mimic the behaviors of process-based models. Results show that the simulated maize yields in most of the top 10 producing countries are overestimated, when compared against FAO observations. Overall, GEPIC, EPIC-IIASA and EPIC-Boku show better performance than other models in reproducing the observed yield variations at the global scale. Climate variability explains 42.00% of yield variations in observation-based statistical model, while large discrepancy is found in crop models. Regionally, climate variability is associated with 55.0% and 52.20% of yield variations in Argentina and USA, respectively. Further analysis based on process-based model emulator shows that climate change has led to a yield loss by 1.51%-3.80% during the period 1980-1990, consistent with the estimations using the observation-based statistical model. As for the period 1991-2000, however, the observed yield loss induced by climate change is only captured by GEPIC and pDSSAT. In contrast to the observed positive climate impact for the period 2001-2010, CLM-Crop, EPIC-IIASA, GEPIC, pAPSIM, pDSSAT and PEGASUS simulated negative climate effects. The results point to the discrepancy between process-based and statistical crop models in simulating climate change impacts on maize yield, which depends on not only the regions, but also the specific time period. We suggest that more targeted efforts are required for constraining the uncertainties of both statistical and process-based crop models for future yield predictions. </p>

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

1999 ◽  
Vol 104 (D6) ◽  
pp. 6623-6646 ◽  
Author(s):  
L. O. Mearns ◽  
T. Mavromatis ◽  
E. Tsvetsinskaya ◽  
C. Hays ◽  
W. Easterling
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Climate Change Scenarios ◽  
Crop Models

scholarly journals Prediction of Maize Yield Response to Climate Change with Climate and Crop Model Uncertainties

2015 ◽  
Vol 54 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Yi Zhang ◽  
Yanxia Zhao ◽  
Sining Chen ◽  
Jianping Guo ◽  
Enli Wang
Keyword(s):  
Climate Change ◽  
Crop Yields ◽  
Maize Yield ◽  
Crop Model ◽  
Global Climate Models ◽  
Yield Response ◽  
Yield Reduction ◽  
Climate Projections ◽  
Crop Models ◽  
Climate Change Uncertainty

AbstractProjections of climate change impacts on crop yields are subject to uncertainties, and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes. This work analyzes the uncertainties in maize yield predictions using two crop models together with three climate projections downscaled with one regional climate model nested with three global climate models under the A1B emission scenario in northeast China (NEC). Projections were evaluated for the Zhuanghe agrometeorological station in NEC for the 2021–50 period, taking 1971–2000 as the baseline period. The results indicated a yield reduction of 13% during 2021–50, with 95% probability intervals of (−41%, +12%) relative to 1971–2000. Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period, and climate change uncertainty generally had a larger impact on projections than did crop model uncertainty during the 2021–50 period. In addition, downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections. Therefore, they have to be bias corrected before use.

scholarly journals Climate Change Impact Assessment and Adaptation Strategies for Rainfed Wheat in Contrasting Climatic Regions of Iran

2021 ◽  
Vol 3 ◽  
Author(s):  
Meisam Nazari ◽  
Behnam Mirgol ◽  
Hamid Salehi
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Nitrogen Fertilizer ◽  
Climate Change Impact ◽  
Yield Reduction ◽  
Climate Change Scenarios ◽  
Planting Dates ◽  
Change Impact ◽  
Rainfed Wheat ◽  
Semi Arid

This is the first large-scale study to assess the climate change impact on the grain yield of rainfed wheat for three provinces of contrasting climatic conditions (temperate, cold semi-arid, and hot arid) in Iran. Five integrative climate change scenarios including +0.5°C temperature plus−5% precipitation, +1°C plus−10%, +1.5°C plus−15%, +2°C plus−20%, and +2.5°C plus−25% were used and evaluated. Nitrogen fertilizer and shifting planting dates were tested for their suitability as adaptive strategies for rainfed wheat against the changing climate. The climate change scenarios reduced the grain yield by −6.9 to −44.8% in the temperate province Mazandaran and by −7.3 to −54.4% in the hot arid province Khuzestan but increased it by +16.7% in the cold semi-arid province Eastern Azarbaijan. The additional application of +15, +30, +45, and +60 kg ha−1 nitrogen fertilizer as urea at sowing could not, in most cases, compensate for the grain yield reductions under the climate change scenarios. Instead, late planting dates in November, December, and January enhanced the grain yield by +6 to +70.6% in Mazandaran under all climate change scenarios and by +94 to +271% in Khuzestan under all climate change scenarios except under the scenario +2.5°C temperature plus−25% precipitation which led to a grain yield reduction of −85.5%. It is concluded that rainfed wheat production in regions with cold climates can benefit from the climate change, but it can be impaired in temperate regions and especially in vulnerable hot regions like Khuzestan. Shifting planting date can be regarded as an efficient yield-compensating and environmentally friendly adaptive strategy of rainfed wheat against the climate change in temperate and hot arid regions.

scholarly journals Climate change impacts on crop yield and quality with CO 2 fertilization in China

2005 ◽  
Vol 360 (1463) ◽  
pp. 2149-2154 ◽  
Author(s):  
Lin Erda ◽  
Xiong Wei ◽  
Ju Hui ◽  
Xu Yinlong ◽  
Li Yue ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Change ◽  
Limiting Factors ◽  
Climate Change Scenarios ◽  
Growth Responses ◽  
Intergovernmental Panel ◽  
Crop Models ◽  
Average Annual Temperature

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.

scholarly journals ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan

2012 ◽  
Vol 370 (1962) ◽  
pp. 1121-1139 ◽  
Author(s):  
Toshichika Iizumi ◽  
Mikhail A. Semenov ◽  
Motoki Nishimori ◽  
Yasushi Ishigooka ◽  
Tsuneo Kuwagata
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Local Scale ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Emission Scenarios ◽  
Model Ensemble ◽  
Daily Weather Data

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.

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