Winter wheat yield response to climate variability in Denmark

2010 ◽  
Vol 149 (1) ◽  
pp. 33-47 ◽  
Author(s):  
K. KRISTENSEN ◽  
K. SCHELDE ◽  
J. E. OLESEN
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Grain Yield ◽  
Inflection Point ◽  
Soil Types ◽  
Weather Data ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Final Model ◽  
Winter Temperatures

SUMMARYData on grain yield from field trials on winter wheat under conventional farming, harvested between 1992 and 2008, were combined with daily weather data available for 44 grids covering Denmark. Nine agroclimatic indices were calculated and used for describing the relation between weather data and grain yield. These indices were calculated as average temperature, radiation and precipitation during winter (1 October–31 March), spring (1 April–15 June) and summer (16 June–31 July), and they were included as linear and quadratic covariates in a mixed regression model. The model also included an effect of year to describe the change in yield caused by unrecorded variables such as management changes. The final model included all effects that were significant for at least one of the two soil types (sandy and loamy soils). Seven of the nine agroclimatic indices were included in the final model that was used to predict the wheat grain yield under five climate scenarios (a baseline for 1985 and two climate change projections for 2020 and 2040) for two soil types and two locations in Denmark.The agroclimatic index for summer temperature showed the strongest effect causing lower yields with increasing temperature, whereas yield increased with increasing radiation during summer and spring. Winter precipitation and spring temperature did not affect grain yield significantly. Grain yield responded non-linearly to mean winter temperature with the highest yield at 4·4°C and lower yields both below and above this inflection point.The application of the model predicted that the average yield would decrease under projected climate change. The average decrease varied between 0·1 and 0·8 t/ha (comparable to a relative reduction of 1·6–12.3%) depending on the climate projection, location and soil type. On average, the grain yield decreased by about 0·25 t/ha (c. 3.6%) from 1985 to 2020 and by about 0·55 t/ha (c. 8·0%) from 1985 to 2040. The predicted yield decrease depended on climate projection and was larger for wheat grown in West Zealand than in Central Jutland and in most cases also larger for loamy soils than for sandy soils.The inter-annual variation in grain yield varied greatly between climate projections. The coefficient of variation (CV) varied between 0·16 and 0·46 and was smallest for wheat grown on loamy soils in Central Jutland in the baseline climate and largest for winter wheat grown under one of the 2040 climate projections. The increase in CV is not so much an effect of increased climatic variability under the climate change projections, but more an effect of increased winter temperature, where more extreme winter temperatures (lower or higher than the inflection point at 4·4°C) increased the effect of winter temperatures.

scholarly journals A probabilistic assessment of climate change impacts on yield and nitrogen leaching from winter wheat in Denmark

2011 ◽  
Vol 11 (9) ◽  
pp. 2541-2553 ◽  
Author(s):  
C. D. Børgesen ◽  
J. E. Olesen
Keyword(s):  
Climate Change ◽  
Sandy Soils ◽  
Climate Change Impacts ◽  
Soil Types ◽  
Weather Data ◽  
N Leaching ◽  
Climate Projections ◽  
Minor Effect ◽  
Climate Change Projections ◽  
Temperature And Precipitation

Abstract. Climate change will impact agricultural production both directly and indirectly, but uncertainties related to likely impacts constrain current political decision making on adaptation. This analysis focuses on a methodology for applying probabilistic climate change projections to assess modelled wheat yields and nitrate leaching from arable land in Denmark. The probabilistic projections describe a range of possible changes in temperature and precipitation. Two methodologies to apply climate projections in impact models were tested. Method A was a straightforward correction of temperature and precipitation, where the same correction was applied to the baseline weather data for all days in the year, and method B used seasonal changes in precipitation and temperature to correct the baseline weather data. Based on climate change projections for the time span 2000 to 2100 and two soil types, the mean impact and the uncertainty of the climate change projections were analysed. Combining probability density functions of climate change projections with crop model simulations, the uncertainty and trends in nitrogen (N) leaching and grain yields with climate change were quantified. The uncertainty of climate change projections was the dominating source of uncertainty in the projections of yield and N leaching, whereas the methodology to seasonally apply climate change projections had a minor effect. For most conditions, the probability of large yield reductions and large N leaching losses tracked trends in mean yields and mean N leaching. The impacts of the uncertainty in climate change were higher for loamy sandy soil than for sandy soils due to generally higher yield levels for loamy sandy soils. There were large differences between soil types in response to climate change, illustrating the importance of including soil information for regional studies of climate change impacts on cropping systems.

scholarly journals Winter Wheat Adaptation to Climate Change in Turkey

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 689
Author(s):  
Yuksel Kaya
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Grain Yield ◽  
Spring Wheat ◽  
Control Treatment ◽  
Climate Change Scenarios ◽  
Combined Effects ◽  
Adaptation To Climate Change ◽  
Wheat Genotypes ◽  
Plant Characteristics

Climate change scenarios reveal that Turkey’s wheat production area is under the combined effects of heat and drought stresses. The adverse effects of climate change have just begun to be experienced in Turkey’s spring and the winter wheat zones. However, climate change is likely to affect the winter wheat zone more severely. Fortunately, there is a fast, repeatable, reliable and relatively affordable way to predict climate change effects on winter wheat (e.g., testing winter wheat in the spring wheat zone). For this purpose, 36 wheat genotypes in total, consisting of 14 spring and 22 winter types, were tested under the field conditions of the Southeastern Anatolia Region, a representative of the spring wheat zone of Turkey, during the two cropping seasons (2017–2018 and 2019–2020). Simultaneous heat (>30 °C) and drought (<40 mm) stresses occurring in May and June during both growing seasons caused drastic losses in winter wheat grain yield and its components. Declines in plant characteristics of winter wheat genotypes, compared to those of spring wheat genotypes using as a control treatment, were determined as follows: 46.3% in grain yield, 23.7% in harvest index, 30.5% in grains per spike and 19.4% in thousand kernel weight, whereas an increase of 282.2% in spike sterility occurred. On the other hand, no substantial changes were observed in plant height (10 cm longer than that of spring wheat) and on days to heading (25 days more than that of spring wheat) of winter wheat genotypes. In general, taller winter wheat genotypes tended to lodge. Meanwhile, it became impossible to avoid the combined effects of heat and drought stresses during anthesis and grain filling periods because the time to heading of winter wheat genotypes could not be shortened significantly. In conclusion, our research findings showed that many winter wheat genotypes would not successfully adapt to climate change. It was determined that specific plant characteristics such as vernalization requirement, photoperiod sensitivity, long phenological duration (lack of earliness per se) and vulnerability to diseases prevailing in the spring wheat zone, made winter wheat difficult to adapt to climate change. The most important strategic step that can be taken to overcome these challenges is that Turkey’s wheat breeding program objectives should be harmonized with the climate change scenarios.

Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan

2012 ◽  
Vol 92 (3) ◽  
pp. 421-425 ◽  
Author(s):  
Hong Wang ◽  
Yong He ◽  
Budong Qian ◽  
Brian McConkey ◽  
Herb Cutforth ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Short Communication ◽  
Weather Data ◽  
Yield Reduction ◽  
Climate Change Scenarios ◽  
Communication Climate ◽  
Stochastic Weather Generator ◽  
Ipcc Sres

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

Take-all distribution and soil type on Chalky Boulder Clay

1986 ◽  
Vol 106 (1) ◽  
pp. 61-66 ◽  
Author(s):  
J. A. Catt ◽  
R. J. Gutteridge ◽  
D. B. Slope
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Soil Type ◽  
Soil Characteristics ◽  
Soil Types ◽  
Exchangeable Potassium ◽  
Boulder Clay ◽  
Extractable Phosphorus ◽  
Take All ◽  
Partial Immunity

SUMMARYIn 1980 a field of winter wheat at Little Raveley, Cambridgeshire (U.K.) suffered a bad attack of take-all, which was confined mainly to areas dominated by Ragdale series, one of five soil types on the field. Take-all and yield were assessed on experimental areas within the field in the three subsequent years (1981–3). On a strip 50 m wide, which was sown with wheat in each of these years, take-all was in decline and although slight differences in take-all occurred between some of the soil types, they had no effect on grain yield. Following a break crop of beans in 1981, the remainder of the field carried wheat in 1982 and 1983. Here take-all was generally less and yields generally greater in the area that suffered the 1980 attack, probably because it had developed a natural partial immunity to the disease. However, measurements of takeall in relation to soil characteristics on 1 m2 plots suggested that the disease was becoming more prevalent on soils that are less well drained in winter, are more deeply decalcified, and contain less extractable phosphorus or more exchangeable potassium.

Generating test reference years from the UKCP09 projections and their application in building energy simulations

2011 ◽  
Vol 33 (4) ◽  
pp. 387-406 ◽  
Author(s):  
H Du ◽  
CP Underwood ◽  
JS Edge
Keyword(s):  
Climate Change ◽  
Carbon Emission ◽  
Building Energy ◽  
Weather Data ◽  
Research Groups ◽  
Future Time ◽  
Climate Change Projections ◽  
Time Horizons ◽  
Energy Simulations ◽  
Operative Temperatures

In this study, test reference year (TRY) data for three UK cities are generated from the new UKCP09 climate change projections 1 for a variety of future time horizons and carbon emission scenario assumptions. The data are applied to the energy simulation of three commercial buildings and one house for the three city locations (London, Manchester and Edinburgh), three future time horizons in this century and three carbon emission scenarios. Results are compared with those generated using alternative TRYs from two other research groups who used UKCP09 1 as well as with the existing TRY data sets which form the CIBSE Future Weather Years 2 in order to produce robust results. Results of future simulations of peak summer operative temperatures, peak cooling demand, annual cooling energy, peak heating demand and annual heating energy are presented for the four building case studies benchmarked against control weather data for the period 1960–1989. The results show increasing internal operative temperatures (non-air-conditioned) and increasing air-conditioning demands (air-conditioned) throughout this century and though peak heating demands remain similar to control data, annual heating energy consumptions can be expected to fall sharply. Practical applications: Currently, practitioners can use Test Reference Years for use in building energy simulations. In 2009, the CIBSE released Future Weather Years, which go further by allowing practitioners to explore the thermal and comfort behaviour of buildings at future time horizons thus helping to ‘future proof’ a design. In 2009, the United Kingdom Climate Impacts Programme released a new generation of climate change scenario data (the UKCP09 climate change projections) using probabilistic methods. These are the most comprehensive data yet and provides a greater degree of detail than was available to generate the CIBSE Future Weather Years. It is therefore likely that the new data will gradually become the normal basis for investigating future building thermal and comfort response. In this study, a sample of TRY is generated from the UKCP09 data and applied to the simulation of a sample of ‘real’ buildings. The results are compared with both the existing CIBSE Future Weather Years as well as with Test Reference Years generated using UKCP09 by two other research groups. The results provide a robust way forward for simulating building thermal and comfort response using future weather data.

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

Planning for climate change without climate projections?

2017 ◽  
Author(s):  
Joshua J. Lawler ◽  
Julia Michalak
Keyword(s):  
Climate Change ◽  
Climate Modeling ◽  
Ecological Impacts ◽  
Climate Projections ◽  
Relative Uncertainty ◽  
Climate Change Projections ◽  
Climate Forecasts ◽  
The Face ◽  
Climate Change Concern ◽  
Conservation Plans

This chapter explores the relative uncertainty associated with popular approaches to conservation planning in the face of climate change. Concern about uncertainties inherent in climate-change projections and associated ecological impacts have led many in the conservation community to avoid climate modeling, and instead favor forecast-free approaches that involve increasing connectivity and protecting “nature’s stage” (geophysical settings) to produce climate-smart conservation plans. A comparison of each of these approaches reveals that the uncertainties associated with connectivity modeling and mapping geophysical settings can be as large, if not larger than, the uncertainties associated with climate-change projections. Whereas the uncertainties of climate forecasts are widely appreciated, the same cannot be said for the approaches that avoid climate forecasts. It is not the case that there is one best approach. The answer to uncertainty is to seek robust conservation plans that work regardless of which approach is taken.

Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway

2016 ◽  
Vol 155 (3) ◽  
pp. 361-377 ◽  
Author(s):  
T. PERSSON ◽  
S. KVÆRNØ
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Crop Yield ◽  
21St Century ◽  
Spring Wheat ◽  
Input Data ◽  
Greenhouse Gas Emission ◽  
Wheat Yield ◽  
Soil Conditions ◽  
Climate Projections

SUMMARYThe effects of soil variability on regional crop yield under projected climate change are largely unknown. In Southeastern Norway, increased temperature and precipitation are projected for the mid-21st century. Crop simulation models in combination with scaling techniques can be used to determine the regional pattern of crop yield. In the present paper, the CSM-CROPSIM-CERES-Wheat model was applied to simulate regional spring wheat yield for Akershus and Østfold counties in Southeastern Norway. Prior to the simulations, parameters in the CSM-CROPSIM-CERES-Wheat model were calibrated for the spring wheat cvars Zebra, Demonstrant and Bjarne, using cultivar trial data from Southeastern Norway and site-specific weather and soil information. Weather input data for regional yield simulations represented the climate in 1961–1990 and projections of the climate in 2046–2065. The latter were based on four Global Climate Models and greenhouse gas emission scenario A1B in the IPCC 4th Assessment Report. Data on regional soil particle size distribution, water-holding characteristics and organic matter data were obtained from a database. To determine the simulated grain yield sensitivity to soil input, the number of soil profiles used to describe the soilscape in the region varied from 76 to 16, 5 and 1. The soils in the different descriptions were selected by arranging them into groups according to similarities in physical characteristics and taking the soil in each group occupying the largest area in the region to represent other soils in that group. The simulated grain yields were higher under all four projected future climate scenarios than the corresponding average yields in the baseline conditions. On average across the region, there were mostly non-significant differences in grain yield between the soil extrapolations for all cultivars and climate projections. However, for sub-regions grain yield varied by up to 20% between soil extrapolations. These results indicate how projected climate change could affect spring wheat yield given the assumed simulated conditions for a region with similar climate and soil conditions to many other cereal production regions in Northern Europe. The results also provide useful information about how soil input data could be handled in regional crop yield determinations under these conditions.

Evaluating planting date and variety management strategies for adapting winter wheat to climate change impacts in arid regions

2017 ◽  
Vol 9 (6) ◽  
pp. 846-863 ◽  
Author(s):  
Iman Hesam Arefi ◽  
Mehri Saffari ◽  
Rooholla Moradi
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Grain Yield ◽  
Arid Regions ◽  
Climate Change Impacts ◽  
Planting Date ◽  
Adaptation Strategies ◽  
Wheat Grain ◽  
Content Type ◽  
Early Maturing

Purpose The purpose of this study is to simulate the climate change impacts on winter wheat production and evaluate the possibilities of using various varieties and shifting planting date as two climate change adaptation strategies in Kerman Province, Iran. Design/methodology/approach Two types of global circulation model and three scenarios for three periods were used. Daily climatic parameters were generated by LARS-WG (Long Ashton Research Station-Weather Generator). The CERES-wheat model was used to simulate future winter wheat growth, development and production. Findings The results showed that CO2 had no effect on the phenology of winter wheat, and the negative impact of temperature on the grain yield was higher than the positive effect of CO2 enrichment. The length of the reproductive growth period of the winter wheat was significantly shortened as affected by the negative impacts of rise in temperature. The simulated results indicated that the grain yield of common (medium maturing) variety of winter wheat will decline, ranging from −0.27 to −18.71 per cent according to future climate changes. Adaptation strategies showed that the early maturing variety had a higher and more stable grain yield under climate change conditions than medium and delayed maturing varieties. Earlier planting date (20 October) increased wheat grain yield under future climatic conditions than common (November 5) planting date. In reverse, later planting (November 20) would accelerate harmful effects of climate change on wheat grain yield. Originality/value The results highlighted the potential of early maturing variety and early planting date as the appropriate agronomical approaches for mitigating harmful impacts of climate change on winter wheat production in arid regions.

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