Mirek Trnka

Changes in time of sowing, flowering and maturity of cereals in Europe under climate change

The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985–2009. Data for spring wheat and spring oats covered latitudes from 46 to 64°N, winter wheat from 46 to 61°N, and maize from 47 to 58°N. The number of observations (site–year–variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1°C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1–3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1–3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied.

The Challenge of Lignocellulosic Bioenergy in a Water-Limited World

It is hoped that lignocellulosic sources will provide energy security, offset carbon dioxide enrichment of the atmosphere, and stimulate the development of new economic sectors. However, little is known about the productivity and sustainability of plant cell-wall energy industries. In this study, we used 16 global circulation models to project the global distribution of relative water availability in the coming decades and summarized the available data on the water-use efficiency of tree- and grass-based bioenergy systems. The data on bioenergy water use were extremely limited. Productivity was strongly correlated with water-use efficiency, with C4 grasses having a distinct advantage in this regard. Our analysis of agro climatic drivers of bioenergy productivity suggests that relative water availability will be one of the most important climatic changes to consider in the design of bioenergy systems.

Modelling shifts in agroclimate and crop cultivar response under climate change.

This paper aims: (i) to identify at national scale areas where crop yield formation is currently most prone to climate-induced stresses, (ii) to evaluate how the severity of these stresses is likely to develop in time and space, and (iii) to appraise and quantify the performance of two strategies for adapting crop cultivation to a wide range of (uncertain) climate change projections. To this end we made use of extensive climate, crop, and soil data, and of two modelling tools: N-AgriCLIM and the WOFOST crop simulation model. N-AgriCLIM was developed for the automatic generation of indicators describing basic agroclimatic conditions and was applied over the whole of Finland. WOFOST was used to simulate detailed crop responses at four representative locations. N-AgriCLIM calculations have been performed nationally for 3829 grid boxes at a 10 × 10 km resolution and for 32 climate scenarios. Ranges of projected shifts in indicator values for heat, drought and other crop-relevant stresses across the scenarios vary widely – so do the spatial patterns of change. Overall, under reference climate the most risk-prone areas for spring cereals are found in south-west Finland, shifting to south-east Finland towards the end of this century. Conditions for grass are likely to improve. WOFOST simulation results suggest that CO2 fertilization and adjusted sowing combined can lead to small yield increases of current barley cultivars under most climate scenarios on favourable soils, but not under extreme climate scenarios and poor soils. This information can be valuable for appraising alternative adaptation strategies. It facilitates the identification of regions in which climatic changes might be rapid or otherwise notable for crop production, requiring a more detailed evaluation of adaptation measures. The results also suggest that utilizing the diversity of cultivar responses seems beneficial given the high uncertainty in climate change projections.

Cereal harvest dates in the Czech Republic between 1501 and 2008 as a proxy for March–June temperature reconstruction

Cereal crop harvests reflect the weather patterns of the period immediately preceding them, and thus the dates at which they begin may be used as a source of proxy data on regional climate. Using systematic phenological observations in the Czech Lands (now known as the Czech Republic) after 1845, together with exploration of further surviving documentary evidence (chronicles, diaries, financial accounts etc.), it has proved possible to create series of winter wheat harvest dates for the period 1501–2008. Employing linear regression, the harvesting dates of the main cereal species (wheat, rye, barley, oats) were first converted to winter wheat harvest days and then normalised to the same altitude above sea level. The next step consisted of using series of winter wheat harvest dates to reconstruct mean March–June temperatures in the Czech Republic, applying standard palaeoclimatological methods. Series reconstructed by linear regression explain 70% of temperature variability. A profound cold period corresponding with late winter wheat harvests was noted between 1659 and 1705. In contrast, warm periods (i.e. early winter wheat harvests) were found for the periods of 1517–1542, 1788–1834 and 1946–2008. The period after 1951 is the warmest of all throughout the entire 1501–2008 period. Comparisons with other European temperature reconstructions derived from documentary sources (including grape harvest dates), tree-rings and instrumental data reveal generally close agreement, with significant correlations. Lower correlations around A.D. 1650 and 1750 may be partly related to deterioration of socio-economic conditions in the Czech Lands resulting from prolonged wars. The results obtained demonstrate that it is possible to use widely-available cereal harvest data for climate analysis and also that such data constitute an independent proxy data series for the region of Central Europe crucial to further studies of the potential impact of climatic variability and climate change on agriculture.

Estimating the impact of climate change on the occurrence of selected pests at a high spatial resolution: a novel approach

The present study is focused on the potential occurrence of the Colorado potato beetle ( Leptinotarsa decemlineata , Say 1824), an important potato pest, and the European corn borer ( Ostrinia nubilalis , Hubner 1796), the most important maize pest, during climate change. Estimates of the current potential distribution of both pest species as well as their distribution in the expected climate conditions are based on the CLIMEX model. The study covers central Europe, including Austria, the Czech Republic, Hungary, and parts of Germany, Poland, Romania, Slovakia, Switzerland, Ukraine, Slovenia, the northern parts of Serbia, parts of Croatia and northern Italy. The validated model of the pests’ geographical distribution was applied within the domain of the regional climate model (RCM) ALADIN, at a resolution of 10 km. The weather series that was the input for the CLIMEX model was prepared by a weather generator (WG) which was calibrated with the RCM-simulated weather series (for the period of 1961–90). To generate a weather series for two future time periods (2021–50 and 2071–2100), the WG parameters were modified according to 12 climate change scenarios produced by the pattern scaling method. The standardized scenarios derived from three global climate models (HadCM, NCAR-PCM and ECHAM) were scaled by low, middle and high values of global temperature change estimated by the Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) model (assuming three combinations of climatic sensitivity and emission scenarios). The results of present study suggest the likely widening of the pests’ habitats and an increase in the number of generations per year. According to the HadCM-high scenario, the area of arable land affected by a third generation per season of Colorado potato beetle in 2050 is c . 45% higher, and by a second generation of the European corn borer is nearly 61% higher, compared to present levels.

Interactively modelling land profitability to estimate European agricultural and forest land use under future scenarios of climate, socio-economics and adaptation

Studies of climate change impacts on agricultural land use generally consider sets of climates combined with fixed socio-economic scenarios, making it impossible to compare the impact of specific factors within these scenario sets. Analysis of the impact of specific scenario factors is extremely difficult due to prohibitively long run-times of the complex models. This study produces and combines metamodels of crop and forest yields and farm profit, derived from previously developed very complex models, to enable prediction of European land use under any set of climate and socio-economic data. Land use is predicted based on the profitability of the alternatives on every soil within every 10’ grid across the EU. A clustering procedure reduces 23,871 grids with 20+ soils per grid to 6,714 clusters of common soil and climate. Combined these reduce runtime 100 thousand-fold. Profit thresholds define land as intensive agriculture (arable or grassland), extensive agriculture or managed forest, or finally unmanaged forest or abandoned land. The demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. An iteration adjusts prices to meet these constraints. A range of measures are derived at 10’ grid-level such as diversity as well as overall EU production. There are many ways to utilise this ability to do rapid What-If analysis of both impact and adaptations. The paper illustrates using two of the 5 different GCMs (CSMK3, HADGEM with contrasting precipitation and temperature) and two of the 4 different socio-economic scenarios (“We are the world”, “Should I stay or should I go” which have contrasting demands for land), exploring these using two of the 13 scenario parameters (crop breeding for yield and population) . In the first scenario, population can be increased by a large amount showing that food security is far from vulnerable. In the second scenario increasing crop yield shows that it improves the food security problem.

Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley ( Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

Direct and indirect impacts of climate and socio-economic change in Europe: a sensitivity analysis for key land- and water-based sectors

Integrated cross-sectoral impact assessments facilitate a comprehensive understanding of interdependencies and potential synergies, conflicts, and trade-offs between sectors under changing conditions. This paper presents a sensitivity analysis of a European integrated assessment model, the CLIMSAVE integrated assessment platform (IAP). The IAP incorporates important cross-sectoral linkages between six key European land- and water-based sectors: agriculture, biodiversity, flooding, forests, urban, and water. Using the IAP, we investigate the direct and indirect implications of a wide range of climatic and socio-economic drivers to identify: (1) those sectors and regions most sensitive to future changes, (2) the mechanisms and directions of sensitivity (direct/indirect and positive/negative), (3) the form and magnitudes of sensitivity (linear/non-linear and strong/weak/insignificant), and (4) the relative importance of the key drivers across sectors and regions. The results are complex. Most sectors are either directly or indirectly sensitive to a large number of drivers (more than 18 out of 24 drivers considered). Over twelve of these drivers have indirect impacts on biodiversity, forests, land use diversity, and water, while only four drivers have indirect effects on flooding. In contrast, for the urban sector all the drivers are direct. Moreover, most of the driver–indicator relationships are non-linear, and hence there is the potential for ‘surprises’. This highlights the importance of considering cross-sectoral interactions in future impact assessments. Such systematic analysis provides improved information for decision-makers to formulate appropriate adaptation policies to maximise benefits and minimise unintended consequences.

Climate change is predicted to alter the current pest status of Globodera pallida and G. rostochiensis in the United Kingdom

Abstract The potato cyst nematodes Globodera pallida and G. rostochiensis are economically important plant pathogens causing losses to UK potato harvests estimated at £50 m/ year. Implications of climate change on their future pest status have not been fully considered. Here, we report growth of female G. pallida and G. rostochiensis over the range 15 to 25°C. Females per plant and their fecundity declined progressively with temperatures above 17.5°C for G. pallida, whilst females per plant were optimal between 17.5 and 22.5°C for G. rostochiensis. Relative reproductive success with temperature was confirmed on two potato cultivars infected with either species at 15, 22.5 and 25°C. The reduced reproductive success of G. pallida at 22.5°C relative to 15°C was also recorded for a further seven host cultivars studied. The differences in optimal temperatures for reproductive success may relate to known differences in the altitude of their regions of origin in the Andes. Exposure of G. pallida to a diurnal temperature stress for one week during female growth significantly suppressed subsequent growth for one week at 17.5°C but had no effect on G. rostochiensis. However, after two weeks of recovery, female size was not significantly different from that for the control treatment. Future soil temperatures were simulated for medium‐ and high‐emission scenarios and combined with nematode growth data to project future implications of climate change for the two species. Increased soil temperatures associated with climate change may reduce the pest status of G. pallida but benefit G. rostochiensis especially in the southern United Kingdom. We conclude that plant breeders may be able to exploit the thermal limits of G. pallida by developing potato cultivars able to grow under future warm summer conditions. Existing widely deployed resistance to G. rostochiensis is an important characteristic to retain for new potato cultivars.