The impacts of projected climate change on yields and soil nitrogen dynamics from winter wheat and spring barley in Denmark

Transcription

1 The impacts of projected climate change on yields and soil nitrogen dynamics from winter wheat and spring barley in Denmark M.Sc. thesis (45 ECTS) By Alfred Othmany Magehema (Student number: ) Master degree programme in Agro-Environmental Management. Department of Agroecology-Faculty of Science and Technology, Aarhus University-Denmark. 1 st July2015 Supervisor: Christen Duus Børgesen- Senior Researcher, Department of Agroecology - Climate and Water

2 The impacts of projected climate change on yields and soil nitrogen dynamics from winter wheat and spring barley crops in Denmark By Alfred Othmany Magehema (Student nr ) Department of Agroecology-Faculty of Science and Technology, Aarhus University 1 st July 2015 Supervisor: Christen Duus Børgesen- Senior Researcher, Department of Agroecology - Climate and Water

3 Table of Contents List of Figures... i List of Tables... ii Declaration... iv Acknowledgement... v Acronyms/ Abbreviations... vi Abstract... vii 1. Introduction Problem statement and study justification Objective (s) Research Hypothesis Scope and Delimitations Theoretical Background Observed climate change in Northern Europe Climate change projections in Northern Europe Temperature projection Precipitation projection Extreme events Projected Climate change effect on crop production northern Europe Winter wheat and spring barley production in Denmark Climate change impacts on crop yields Carbon dioxide (CO2) Temperature and rainfall Extreme events and climatic variability Factors affecting crop productivity under climate change Climate change impacts on Nitrate leaching and soil N cycling Materials and Methods Crop model SimDen Model Study sites and climate conditions a

4 3.4. Approach Modeling process Sensitivity analysis in calibrating grain yields Daisy Model Calibration and validation Calibration of Soil hydraulic parameters Calibration of Soil Organic Matter (SOM) turnover Calibration of denitrification Calibration of crop growth Model set-up for scenario simulations Soil types and Crop management Climate models and Meteorological data Simulations set-up Daisy Model output variables and analysis Statistical analysis Results Calibration results Calibration of DM yields Calibration for N leaching Calibration for denitrification Calibration for Change in organic N pool Simulation results Grain yields response Changes in Julian harvest days Responses on changes in organic N pool Responses on nitrate leaching Responses on denitrification Discussion Climate change effects on grain yield Climate change effect on soil Nitrogen dynamics Changes in organic N pool Nitrogen Leaching b

5 Denitrification Changes in crop management practices under climate change Relevancy, Efficiency and Limitations (uncertainties) of the Daisy Model Conclusion and Perspectives Conclusion Perspectives References Appendixes A. Table 1. Mean seasonal and annual air temperature (degc) and mean precipitation (mm y 1) for the baseline period ( ), and for the projected climatic periods obtained from four climate models in Denmark B. Water stress days C. Sample run file in Daisy D. Example of management files for spring barley crop used in Daisy modeling E. Example of soil column F. Calibrated crop module for spring barley and winter wheat c

6 List of Figures Figure 2.1: Average DM yields (t/ha) of winter wheat and spring barley in Denmark. Source: Statistics Denmark (DST, 2015) Figure 2.2: Average DM yields (t/ha) of winter wheat and spring barley for province Fyn in Denmark. Source: Statistics Denmark (DST, 2015) Figure 3.1: Schematic overview of the agro-ecosystem model Daisy Figure 3.2: Then overview of the Daisy water balance (a), crop (b), nitrogen balance (c) and organic matter turnover (d). AOM = added organic matter, SMB = soil microbial biomass, and SOM = native soil organic matter; fx = portioning coefficient. The inert SOM3 pool does not interact with the rest of the system Solid lines represent flows of matter, and dashed lines represent flows of information. Source: (Hansen et al., 2012) Figure 3.3: An overview of various steps used in this study (Daisy modelling) Figure 3.4: Mean seasonal and annual changes in temperature (degc) for two projected climatic periods; (CP2) and (CP3) from baseline period (CP1) based on four climate models in Denmark Figure 3.5: Mean seasonal and annual changes in precipitation (mm) for two projected climatic periods; (CP2) and (CP3) from baseline period (CP1) based on four climate models in Denmark Figure 4.1: Observed and simulated mean sorg DM yields for winter wheat and spring barley Figure 4.2: Mean DM yields (kg/ha) from observed climate and model simulations for winter wheat and spring barley based on baseline climatic period ( ) Figure 4.3: Mean N leaching (kg N/ha) from Observed climate and model simulations for winter wheat and spring barley based on baseline climatic period ( ) Figure 4.4: Mean denitrification (kg N/ha under three soil types based on SimDen Model and simulated for baseline period ( ) Figure 4.5: Mean predicted and simulated change in Organic N Pool (kg N/ha) under three soil types and for climatic period i

7 Figure 4.6: Changes in mean grain yield (t DM ha -1 ) for projected Climatic Period 2 (CP2_ ) and Climatic Period 3 (CP3_ ) from baseline period (CP1_ ) based on three soil types and four climatic models for winter wheat (a) and spring barley (b) Figure 4.7: Changes in simulated mean N yields (kg ha -1 ) for projected Climatic Period 2 (CP2_ ) and Climatic Period 3 (CP3_ ) from baseline period (CP1_ ) based on three soil types and four climatic models for winter wheat (a) and spring barley (b) Figure 4.8: Simulated changes in Julian harvest days of winter wheat and spring barley for the projected climatic periods (CP2_2040, CP3_2080) from baseline period (CP1_ ) based on four climatic models and under Figure 4.9: Simulated average changes in organic N pool (kg N ha -1 ) for winter wheat and spring barley as predicted for the baseline and two projected climatic periods based on four climatic models under Loamy Sand (a), Sandy Loam (b) and Coarse Sand (c) soil Figure 4.10: Simulated mean Nitrate Leaching (kg N/ha) for winter wheat and spring barley crops as predicted in three climatic periods by four different climatic models and three soils; (a) Loamy sand, (b) sandy loam and (c) course sand. Bars indicate the standard error (SE) Figure 4.11: Simulated mean denitrification (kg/ha) for winter wheat and spring barley crops as predicted in three climatic periods by four different climatic models and three soils; (a) Loamy Sand, (b) Sandy Loam and (c) Course Sand. Bars indicate the standard error (SE) Figure 5.1. Mean simulated change in yield of winter wheat, spring barley and ryegrass at increasing temperatures for a site in Denmark. Source: (Olesen 2014) Figure 5.2: Annual nitrogen leaching (NO3-N+NH4-N) in individual crops of each crop rotation for the baseline period and two projected climate scenarios. Source; Doltra et al List of Tables Table 1. The projection of change in temperature and precipitation for Northern Europe base on the AIB scenario (i.e. closest to RCP6.0)... 8 Table 2. Effects of projected climate changes on changes in key agro-climatic indicators in Northern European agro-ecological zones by Table 3. Land area and agricultural land use in Denmark for 2011 (1,000 ha). The data for individual crops is from 2012 (1,000 ha): Source; (Olesen 2014) ii

8 Table 4. Influence of CO2, temperature, rainfall and wind on various components of the agroecosystem Table 5. Description of various parameters used in model calibration for crop modules Table 6.The calibrated soil hydraulic parameter for Mualem-van Genuchten model used to simulate the water flow in all study sites Table 7. The slow and fast decomposing and the inert SOM pools (Fractions) Table 8. Details on Soil properties for three study sites (soil types) Table 9. Applied mineral N fertilizer, dates of ploughing, sowing, fertilization and harvest used in the modelling for winter wheat and spring barley for baseline and projected climatic periods. Dates were obtained from Doltra et al Table 10. Simulation set-up of Daisy modelling for both crops in this study Table 11. Changes in parameters used in Daisy modeling based on baseline and future climatic periods Table 12. Simulated average DM yields (t/ha) presented in value and Standard Deviation (SD) of winter wheat and spring barley as predicted for the baseline and projected climatic periods by four climatic models based on Loamy Sand, Sandy Loam and Coarse Sand soils Table 13. Simulated mean N yields (kg/ha) and Standard Deviation (SD) of winter wheat and spring barley as predicted for the baseline climate and projected climate by different climatic models under Loamy Sand, Sandy Loam and Coarse Sandy soils Table 14. Effects of projected climate changes on crop growing days based on four different climatic models and in three climatic projection periods Table 15. Standard deviation (SD) and standard error (SE) of changes in organic N pool based on different climate models and on three soil types and three projection periods for winter wheat and spring barley Table 16. Standard deviation (SD) of mean N leaching (kg ha - 1) based on different climate models and on three soil types and three projection periods for winter wheat and spring barley Table 17. Standard deviation (SD) of mean denitrification (kg ha - 1) based on different climate models and on three soil types and three projection periods for winter wheat and spring barley Table 18. Illustrates current and proposed future crop rotation in two case areas, Lillebæk and Odderbæk. (Generated from Henriksen et al. 2013) iii

9 Declaration I Alfred Magehema declare that this is a product of my own research work under the supervision of Christen Duus Børgesen, a Senior Researcher from the Department of Agroecology, Aarhus University. All other sources of materials are duly acknowledged. This work has not previously been submitted to any institution for an award of any academic degree... Alfred Othmany Magehema (Student) 1 st July 2015 iv

10 Acknowledgement This thesis is submitted as partial fulfillment of the a degree in Master of Science in Agro- Environmental Management from the Department of Agroecology, Faculty of Science and Technology, Aarhus University (AU). The study is based on Eight months of research period that I conducted, from 19/11/ /07/2015 in AU research centre in Foulum. I would like to extend my sincere thanks to my supervisor, Christen D. Børgesen for his willingness to accept me as his student and his constructive advices, guidance and endless support without which this thesis would not have been completed. I am very grateful to Christen for his valuable support and the help he provided all through. I am also grateful for the support of Kiril Manevski for sharing his experience with using Daisy model and giving me motivation. I am particularly thankful to Jytte Christensen and other staffs in the department of climate and water, Foulum research centre for their cooperation in the whole period of my thesis. I am highly appreciating the government of Denmark and Aarhus University at large for granting me the scholarship to pursue my Master education for two years under the Platform Cooperation of Environment and Climate/Growth and Employment. Finally, I would like express my deepest gratitude to my parents for their support, inspiration and guidance in my life. v

11 Acronyms/ Abbreviations AOM Added organic matter C C/N DM DS EU GHGs I IPCC K LAI m n N N2O N2O NH3 NH4 NO2, NO3ˉ OM SOM SOM1 SOM2 SOM3 α θ r θs Carbon Carbon-to-Nitrogen Dry matter Development stage European Union Greenhouse gases Empirical soil parameter for pore connectivity / fitting parameter for slope of the retention curve Intergovernmental Panel on Climate Change Soil hydraulic conductivity Leaf area index Empirical constant soil parameter used in retention models (shape factor) Empirical constant soil parameter used in retention models (shape factor) Nitrogen Nitrous oxide Nitrogen dioxide Ammonia Ammonium Nitrous Oxide Nitrate Organic matter Soil organic matter Slowly decomposing pool of soil organic matter Rapid decomposing pool of soil organic matter Inert pool of soil organic matter Empirical constant soil parameter used in retention models (shape factor) Residual volumetric water content Volumetric soil water content at saturation vi

12 Abstract The ongoing increase in concentrations of atmospheric greenhouse gas will most likely affect global climate for the rest of this century. In various parts of the world, agriculture is expected to be affected by climate change differently. The Nitrogen (N) dynamic might greatly be affected by climate change in the agricultural landscape and on the N transport in streams and lakes. This study aimed at investigating the impacts of projected climate change on crop yields and soil N dynamics from winter wheat and spring barley crops with the Daisy simulation model. Four distinct climate models were considered namely; ARPEGE_CNRM, ECHAM5_HIRHAM5, ECHAM5_RCA3 and HadCM3_HadRM3. Three study sites with contrasting typical Danish soils and climate conditions were considered; (i) Flakkebjerg (Loamy Sand), (ii) Foulum (Sandy Loam) and (iii) Jyndevad (Coarse Sandy).The model simulation were conducted for 20-year time slices in three different projection periods including, (baseline), 2040 ( ) and 2080 ( ). The results from model simulation indicated the general reduction in DM yields for both crops with the highest reduction of 0.7 t ha -1 (11.4%) on Loamy Sand based on average from all climate models and 1.6 t ha -1 (152.2%) on Coarse Sandy as projected by ARPEGE_CNRM model for winter wheat and spring barley respectively. The highest reduction in N yields were 27.6 kg N ha -1 (35.1%) on coarse sandy and 32.8 kgn ha -1 (41.7%) on loamy sand for winter wheat and spring barley respectively for 2099 projection. For 2099 projection, the highest increase in changes in mean organic N pool of 14 KgN ha -1 for winter wheat and 34.9 KgN ha -1 for spring barley on Loamy Sand soil were predicted by ARPEGE_CNRM and HadCM3_HadRM3 models respectively. The highest N leaching of 29.1 Kg N ha -1 (41%) on coarse sandy for winter wheat and 60.9 kg N ha -1 (60.2%) on loamy sand was projected by ARPEGE_CNRM and HadCM3_HadRM3 models respectively. ECHAM5_HIRHAM5 model projected the highest increase in denitrification rate of 34.9 kg N ha -1 (66.1%) and 42 kg N ha -1 (73.2%) on Loamy Sand soil for winter wheat and spring barley respectively. The model simulation results were varying based on based on type of climate model used, soil conditions, projection period considered. The use of distinct climate models in projecting the effect of climate change on crop yields and soil N dynamics found to be in accordance with the predicted temperatures and rainfall changes. Keywords: Climate change, climate variability, Daisy model, crop yields, soil nitrogen, nitrogen leaching, denitrification, changes in organic N pool, winter wheat, spring barley vii

13 1. Introduction The ongoing increase in concentrations of atmospheric greenhouse gas (Peters et al. 2013) will most likely affect global climate for the rest of this century (Meehl et al. 2007). In various parts of the world, agriculture is anticipated to be affected by climate change differently (Olesen. 2005). The effects of climate change depend on several crucial factors including; soil and climatic conditions as well as resources to cope with change (Olesen 2005). The variations in weather and climate will impact crop production as crops are very sensitive to these factors (Rötter et al. 2012; Coumou & Rahmsdorf 2012). Climate change affect crop production through changing of main processes in soil and plant namely; crop development, photosynthesis and growth as well as biological and chemical transformations of nutrients in soils (Olesen & Bindi 2002; Doltra et al. 2012). The impact of climate variability has also been observed on cultivated crops in terms of yield (Olesen et al. 2011). In the period of , all countries in Europe had a considerable increase in yield, because of technological improvement (Olesen 2014). But in the past 10 to 20 years yield have levelled off in most of these countries, with even more reduction in Greece. However, there was an exception for few countries like Finland in the north where yield increase was observed (Olesen et al. 2011). These observed changes in yield in North and South of Europe can be attributed to the overall observed temperature trends and precipitation patterns. The crop growth cycles are no longer the same and are expected to change over time as long as the frequency of heatwaves continues to rise (Olesen et al. 2011). Over the coming decades, the growing food demand driven by income growth and population must be offset by considerably increasing crops production and yields (Tilman et al. 2011). However, one of the threats to food security is the increasingly changing climate (Coumou & Rahmsdorf, 2012) especially in sub-saharan Africa and South Asia and also at global level (Nelson et al. 2009). Negative impacts of climate change on crop production are expected in many parts of Europe (Olesen et al. 2011; Soussana et al. 2012; Rötter, et al. 2012). The sensitivity of the intensive farming systems is mostly low in western and central Europe, this is due to the fact that, the given changes in temperature or rainfall have modest impact and because of capacity of farmers to adapt and compensate by changing management (Olesen et al. 2011). Still, the capacity of adaption might differ significantly between cropping systems and farms based on their specialization 1

14 (Olesen et al. 2011; Olesen 2014). The farming systems currently situated in hot and dry areas are predicted to be affected by climate change severely, while the intensive farming systems in cool climate may then respond favourably to a current climatic warming (Olesen and Bindi 2002; Olesen et al. 2011). Moreover, factors such as; climatic conditions, soils, land use, political and economic conditions are largely varying across the European continent and are likely to influence the responses greatly (Bouma et al. 1998; Olesen et al. 2011). The soil nitrogen (N) dynamics might greatly be affected by climate change in the agricultural landscape as well as on the N transport in streams and lakes (Jeppesen et al. 2011).The climate change impacts on environment is considered as most important aspect in agricultural production, especially on how the quality of aquifers, estuaries and rivers are affected by nitrate leaching (Jeppesen et al. 2009). The decomposition of organic matter is expected to be generally stimulated by the warming climate resulting into the release of mineral N from agricultural soil (Olesen et al. 2004). In the northern latitudes, and under warmer climate, the N cycling and N leaching is estimated to be increased (Jeppesen et al. 2011; Patil et al. 2012), and is likely to be attributed by the projected extreme warming with climate change. Also, it can be further enhanced by the more precipitation surplus during winter and autumn and which is further projected to increase (Christensen & Christensen 2007). Precipitation events are largely influencing the flux of soil mineral N, and therefore the risk of N leaching (N losses) may increase (Patil et al. 2012) 2

15 1.1. Problem statement and study justification In the world, one of the largest and greatest productive suppliers of food and fibre is Europe (Olesen 2014). Europe accounts for 20% and 19% of global cereal production and global meat production respectively in 2008 (Olesen et al. 2011). The agricultural production in Europe is mostly high, in particular Western Europe (Olesen 2014). The average cereal yields in the EU countries are 60% higher than the world average with nearly 63% of the cereals are produced in the EU27 countries (Olesen et al. 2011; Olesen 2014). However, in many European countries, the trend of cereal grain yield was found to be stagnant with increased in yield variability over recent years (Rötter et al. 2012). Some of these trends might have been due to the impacts of the recent climatic changes over EU (Olesen et al. 2011) including more frequent extreme weather events (Rötter et al. 2012). This implies that, there might be temporary reductions crop yields in most of European countries (Olesen 2014). For instance; a study by Southworth et al. 2002; Patil et al. 2012) demonstrated that the higher variability in crop yields and decline in future wheat yields are mainly attributed by increased variability of both temperature and precipitation. Although, Europe would be able to increase import to cover such deficits, this might put further pressure on food insecurity for low income countries (Porter et al. 2013) as well as required agricultural products imports might significantly contribute to GHG emissions out of Europe (Smith & Olesen 2010; Rötter et al. 2012). The agricultural production is expected to continually be affected by climate change in the future, whereby the effects will vary significantly in space and may change over time Patil et al In cool temperate regions such as Northern Europe, the agricultural productivity is anticipated to increase mainly due to a prolonged growing season, increasing CO2 concentration and the extension of frost free periods (Olesen & Bindi 2002). Due to the influence of extreme climatic events and other factors such as diseases and pests, the yields variation over year-to-year is expected to increase in the region (Kristensen et al. 2011; Olesen 2014). Though, farmers have been and are recently adapting to climate change impacts across Europe, especially by changing timing of cultivation and choosing other crop species and cultivars, climate change impacts on crop production are still mostly negative in most European regions (Olesen et al. 2011). In Denmark, the risk of N losses through leaching or denitrification might be elevated due to the increasing trend of precipitation during winter (Patil et al. 2010a; Patil et al. 2012). Consequently, this might results into more N to surface waters (Jeppesen et al. 2011). 3

16 Various previous modeling studies have studied mostly on the impact of changes in climatic means and variability on crop production (Olesen et al. 2000; Richter and Semenov 2005; Patil et al. 2012). Although, to my knowledge, few studies have concentrated on both crop responses and soil N dynamics (Olesen et al. 2007; Børgesen & Olesen 2011; Patil et al. 2012, Doltra et al. 2012). This calls for the need of further studies on the impacts of current and projected climate change on crop yields and soil N dynamics, particularly changes in organic N pool, N leaching and denitrification. The soil processes under projected climatic conditions and adaptation may interact in a complex manner and significantly influence crop production and N fluxes, thus to study experimentally, it time consuming and costly (Patil et al. 2012). In order to assess the impacts of future climatic factors before examining further in detail through field experimentation while incorporating only treatments such as; changes to temperature or precipitation which show significant and measurable effect, the multi-process dynamic models function relatively easy, cost effective and accessible methods (Patil et al. 2012). Thus, in this study, the Daisy model was used as a tool to examine how projected climate change will impact crop yields and soil N dynamics for winter wheat and spring barley crops in Northern Europe and as exemplified with typical Danish soils and climatic condition (Patil et al. 2012) Objective (s) The main objective of this study was to evaluate the effect of projected climate change on crop yields and soil nitrogen (N) dynamics for winter wheat and spring barley crops under typical contrasting Danish soil and climatic conditions. In order to examine this, the Daisy model was used. The specific objective(s) were to; i) Examine the effects of projected future climate change on the crop yields, both dry matter (DM) and nitrogen (N) based on short and long term climatic projection periods and different typical Danish soil types. ii) Assess the effect of projected climate change on soil nitrogen (N) dynamics in particular; changes in organic N pool (Net mineralization), N leaching and denitrification. iii) Evaluate the use of different climate models in simulating the effect of projected climate change on crop yields and soil N dynamics. 4

17 1.3. Research Hypothesis The following hypothesis were made and tested in the present thesis study: i) Projected climate change will reduce the yields of winter wheat and spring barley under typical Danish soil and climatic conditions. ii) The soil N cycling and N leaching will increase due to the effects of projected climate change under typical Danish soil and climatic conditions. iii) The uncertainty of future climate will be examined by using four different regional climatic model predictions Scope and Delimitations This thesis focused only on the Daisy simulated impacts of changes in mean and variability mainly three parameters including; global radiation (W/m 2 ), air temperature ( C) and precipitation (mm/d) on crop production and N leaching over years for 21 st Century. This mainly because, the listed weather parameters have more direct impacts on crop production in northern European and were included in four climatic models. The effects of projected increased CO2 Concentration on crop production and soil N dynamics has been considered as suggested by Intergovernmental Panel on Climate Change (IPCC) (Børgesen 2015). The direct or indirect effect of impacts of climate change on pest and pesticides and soil, were not taken into account in Daisy model as it was assumed that, farmers will be able to protect the crops. The management practices that were included in Daisy simulation model were such as; (i) fertilizer application in economically optimal amounts were based on current recommendations in Denmark (Børgesen 2015) and were kept constant in all climatic periods considered. (ii) Dates of ploughing, sowing, fertilization and harvest of crops were adopted from Doltra et al. (2012) and were kept constant under different climatic projection period. (iii) Also, the irrigation practice was not considered as a management option in this Daisy modeling study. 5

18 2. Theoretical Background 2.1. Observed climate change in Northern Europe The annual mean increase of 0.9 C in surface air temperature has been experienced in Europe between 1901 and 2005 is one of the evident impacts (Olesen et al. 2011). The leveling of crop yield in the past two decades, with exceptional increase in some countries like Finland is also in relation to observed climate change in Europe. This is mainly because of the technological improvement which happened in Europe between 1970 and 1990 which contributed to yield increase in all European countries, especially those in the west and central Europe (Olesen et al. 2011). Though, the difference in observed yield can be explained by the influence of other factors, yet because of its role on physiological activities of the plant, changes in climate are also associated (Olesen et al. 2011). The observed temperature trend have been higher in the north-eastern parts of Europe and mountainous regions, while the lowest trends are more observed in the Mediterranean region as a record of the past 25 years. However, the increase has been observed more in winter season as compared to summer, and has also been associated with the increase of warm temperature extremes as elaborated by Olesen et al. (2011). An increase in annual mean temperature by 0.68 C and with uniformly distribution during the year has been experienced in Denmark through the 20th century (Olesen 2005; Patil et al. 2012). In Europe, precipitation has generally been increasing in most parts during the 20th century despite of large spatial variations (Alcamo & Olesen 2012). The eastern and western Europe have experience an increase drought events occurrences, with more increase observed in the Mediterranean region as described in Olesen et al. (2011). Northern Europe on the other hand has received an increase of mean winter precipitation during most of the 20th century (Alcamo & Olesen 2012). Research has already shown the impacts of climate change in Europe are slight and mostly unthreatening, but they imply early signs of significant future impacts (Alcamo & Olesen, 2012). During the 20th century, an increase in annual mean precipitation by 78 mm and with uniformly distribution during the year has been observed in Denmark through the 20th century (Olesen 2005). 6

19 2.2. Climate change projections in Northern Europe In 21st century and in projecting climate change, the IPCC has currently established the pathways which consider a diverse of possible futures. The Representative Concentration Pathways (RCPs) are four greenhouse gas (GHG) concentration trajectories adopted by the IPCC for its fifth Assessment Report (AR5) (IPCC 2013). RCPs describe four possible climate futures, all of which are considered possible depending on how much greenhouse gases are emitted in the years to come (IPCC 2013). RCPs are named according to their impact on radiative forcing by 2100, whereby Radiative forcing is a measure of potential climate change (Olesen 2014). RCP 2.6 assumes that global annual GHG emissions peak between , with emissions declining substantially thereafter. RCP2.6 indicates how a warming of less than 2 C is possible. Emissions in RCP 4.5 peak around 2040, then decline. RCP 6, emissions peak around 2080, then decline and in RCP 8.5, emissions continue to rise throughout the 21st century with radiative forcing of 8.5 W/m 2 in 2100 (IPCC 2013) Temperature projection Over the 21st century, global mean temperatures will continue to rise in all of the RCPs. The rate of global warming begins to be more strongly from around the mid-21st century based on the scenario (IPCC 2013). An increase of global surface air temperatures by C are anticipated during the 21st century, while the northern latitudes are expecting to experience the highest increases (IPCC 2007; Patil et al. 2012). Several studies project that, in northern Europe, high percentile summer temperatures are expected to warm faster than mean temperatures (IPCC, 2013). Up to the end of 21st Century, in northern Europe, the annual average temperature is projected to change by 5 C as compared to the period when considering RCP6.0 (IPCC 2013). Also, northern Europe including Denmark is expected to experience the largest temperature increases with the largest effects during winter, with gradual substantial changes in snow cover as well as reduced variability in daily temperatures (Alcamo & Olesen 2012). Additionally, IPCC for its fourth Assessment Report (AR4) projected that, the variability in temperature considering both inter-annual and daily time scales, is expected to increase in summer and decrease in winter in most European areas (Christensen et al. 2007; Bindi et al. 2010). The annual temperature change in Northern Europe will possibly be greater than globally with 3.2 C compared to 2.8 C (global), while winter season is anticipated to have the largest temperature increase by 4.3 C as shown in 7

20 Table 1 (Olesen 2014). By the end of 21 st Century and in Denmark, an increase in annual mean temperature of 1 to 4 C with an approximately 10% increase in annual mean rainfall (Olesen 2005) Precipitation projection Global mean precipitation will increase at a rate per C lower than that of atmospheric water vapor (IPCC 2013). It will likely increase by 1 to 3% C 1 for scenarios other than RCP2.6 at the end of the 21st century (IPCC 2013). The general pattern of change shows that, high latitudes (50 N) are very likely to experience greater amounts of precipitation mainly due to the influence of thermodynamic factors and changes in circulation (IPCC 2013). The air can hold and transport more moisture during warmer winter and therefore giving more rain and snow. During winter, if the westerly winds increase then winter precipitation will increase as predicted by many models (Olesen 2014). Furthermore, up to the end of 21st century, about 20% of precipitation is expected to increase relative to period under RCP8.5 in northern Europe (IPCC, 2013). In the end of the 21st century under the RCP8.5 scenario, increases in annual runoff are expected in the high northern latitudes corresponding to large increases in winter and spring precipitation as depicted in Table 1 (Olesen 2014). The largest increase in precipitation for Northern Europe is during winter by +15%, while smallest in summer by +2% as indicated in Table 1. If uncertainties are counted in, precipitation may even decrease in summer, with a prediction in a decrease during summer for south of 55 N including most of Denmark (Olesen 2014). In Denmark, an increased winter precipitation trend has been experienced and is expected to continue in the future (Andersen et al. 2006) while small changes in the annual precipitation is projected (Olesen 2014). Table 1. The projection of change in temperature and precipitation for Northern Europe base on the AIB scenario (i.e. closest to RCP6.0) Temperature change ( C) Precipitation change (%) Season 25% quartile Median response 75% quartile 25% quartile Median response 75% quartile Winter Spring Summer Autumn Annual Source: Olesen (2014). 8

21 Extreme events. Heavy precipitation events are projected to increase in the higher latitudes and decrease in the subtropics particularly during winter (Alcamo & Olesen 2012). This could be affected by both changes in water vapour content as induced by large-scale warming and large-scale circulation changes (Alcamo & Olesen 2012). The study by Alcamo and Olesen (2012) indicated that the occurrence of flooding events would be attributed by the interaction of heavy rainfall, surface runoff, wind and sea level rise. Though, quantitative uncertainty on changes in extreme precipitation are still relatively large, yet more intense rainfall events are indicated (Christensen & Christensen 2003; Bindi et al. 2010). Cold events are projected to decrease considerably in a future warmer climate and it is reflected that heat waves would be stronger, more frequent and last longer towards the end of the 21st century (IPCC 2013). Up to the end of 21st century, projections show a warming of 0.3 C to 1.8 C in winter, associated with droughts with the largest changes in northern Europe (IPCC 2013). A study by Alcamo and Olesen (2012) suggested that crop production in northern Europe are expected to be destroyed severally by the extreme weather events namely; spells of high temperature, heavy storms or droughts. This is also demonstrated by considerable declines in primary productivity of terrestrial ecosystems attributed by European heat wave in 2003, which consequently led reduced farm income (Olesen & Bindi 2004; Ciais et al. 2005; Bindi et al. 2010) Projected Climate change effect on crop production northern Europe The agricultural systems are likely to be affected by climate change differently in space and may also change over time in various parts of Europe (Alcamo et al. 2007; Olesen 2014).). Positive effects of climate change might be experienced in northern areas, especially through increases in the range of species grown, lengthened growing season to increasing CO2 concentrations as well as an extension of the frost-free period (Olesen & Bindi 2002; Olesen 2014). Though, the extreme climatic events and other factors, including pests and diseases may likely result into the increased variability in yields over year-to-year in regions with intensive crop production (Kristensen et al. 2011; (Olesen 2014). Southern Europe are anticipated to experience more negative impacts of climate change including, the advanced variability in yield, decreased harvestable yields and a decline in the appropriate areas for traditional crops, which calls for the adaptation strategies to be considered (Bindi et al. 2010). In the Nordic countries, more favorable conditions for crop 9

22 production are expected as result of climate change effects as it can be seen from effects on agroclimatic indicators shown in Table 1 (Olesen 2014). By 2050, a considerable lengthening of the growing season by one month the northern regions is shown from these indicators, but far less in the southern parts of Scandinavia (Olesen 2014). The potential for crop yield will be little affected due to low solar radiation thus still will constrain the growth during the winter (Olesen 2014). Table 2. Effects of projected climate changes on changes in key agro-climatic indicators in Northern European agro-ecological zones by 2050 Effective Zone Effective solar radiation (%) growing days (days) Date of last frost (days) Dry days in spring (%) Dry days in summer (%) Atlantic North (western Denmark) Continental (eastern Denmark, southern Sweden) source: (Trnka et al. 2011; Olesen 2014). The areas of cereal cultivation are expected to expand northwards due to the influence of global warming, mainly into areas earlier dominated by fodder and grassland production (Olesen 2014). An increase in suitable area for grain maize production in Europe by the end of the 21st century has been projected by 30 to 50% in countries including Ireland, Scotland, southern Sweden and Finland (Olesen et al. 2007; Bindi et al. 2010). The grown cereals crops, for instance; winter cereals and spring cereals may be replaced with maize crop (Table 18) largely influenced by the longer growth duration (increased temperatures) (Elsgaard et al. 2012; Henriksen et al. 2013; (Olesen 2014). The energy crops show a northward expansion in potential cropping area, but a reduction in suitability in southern Europe by 2050 (Bindi et al. 2010). The grain yield is likely to be reduced by the projected temperature increases, which speed up crop development and therefore growing period will be reduced (Doltra et al. 2012). In Denmark, the yield reduction in winter wheat is estimated to vary from nearly 2 to 12% based on climate model and the projection period considered Kristensen et al. (2011) as well as location and soil type (Patil et al. 2012; Doltra et al. 2012). Doltra et al. (2012) projected grain yields to decline in the future because higher temperatures reduce the growing period by speeding up the crop development. 10

23 2.3. Winter wheat and spring barley production in Denmark Currently, Europe is one of the leading and greatest suppliers of fiber and food. It accounted for 20% of global cereal production in 2008 (Olesen et al. 2011). In 2006, the European meat production and cereal production occurred in EU 27 countries in approximately 82% and 67% respectively (Bindi et al. 2010). Europe is commonly characterized by high levels of agricultural production mostly in Western Europe (Olesen 2014). Compared to the world s cereal average yields, the EU countries are more than by 60% higher (Alcamo & Olesen, 2012). In the Nordic countries, cereals have dominated the arable cropping systems, mainly spring barley and oats in northern part of the region and winter wheat and spring barley in southern parts (Olesen 2014). Generally, the southern parts of the Nordic region such are Denmark and southern Sweden are considered to have more intensively production systems, compared with regions further north (Olesen 2014). The cultivation of crops of high yields, for instance winter wheat has increased in the past years (Olesen 2014). Table 3. Land area and agricultural land use in Denmark for 2011 (1,000 ha). The data for individual crops is from 2012 (1,000 ha): Source; (Olesen 2014). Land Use Land area % to agricultural area Land area, total 4243 Agricultural area, total Arable land 2499 Organic farmed area Permanent crops Temporary pastures Permanent pastures Cereals Pulses and oilseed Root and tuber 81 3 Fodder crops Vegetables Fruits and berries Denmark is characterized by intensive cultivation, whereby, agricultural production occupied 63% of the total land area (Doltra et al About 50% of the agricultural area in Denmark is taken by cereals as indicated in Table 3 and in 2011, the most cultivated crops were winter wheat and spring barley with 0 49 and 0 32 of total cereal surface respectively (Doltra et al. 2012). The agriculture land use in Denmark is as depicted in Table 3 (Olesen 2014). The average DM yields 11

24 Mean DM yield (kg/ha) of winter wheat and spring barley in Denmark and Fyn province as the study area are as shown in Figure 1 and 2 respectively (DST, 2015) Winter wheat Spring barley Years Figure 2.1: Average DM yields (t/ha) of winter wheat and spring barley in Denmark. Source: Statistics Denmark (DST, 2015). In Nordic countries including Denmark, cereal grain production have risen over the past 50 years (Olesen 2014). In the period before 1990s, the increase in grain yields occurred mostly, and it is reported to be attributed by increased inputs of fertilizer and pesticides as well as more crop varieties which were productive (Olesen 2014). However, in the past 20 years, the yields of wheat and barley have remained constant in Denmark, Sweden and Norway (Olesen 2014). In this period, grain yields have been consistently different among the Nordic countries, with highest and lowest yields in Denmark and Finland respectively (Olesen 2014). 12

25 Mean grain yield (kg DM/ha) The climatic conditions are factors in which crop cultivation, quality and their productivity are directly depend upon in particular temperature and rainfall. Despite of continual progress in crop breeding, yet there is stagnation in wheat yields in parts of Europe which has been reported (Brisson et al. 2010) to be contributed by climate change impacts on agriculture in Europe which was already experienced (Peltonen-Sainio et al., 2010; Olesen 2014), with more effect in central and southern Europe. Increased grain yields of both cereals and oilseed crops have been contributed by warming in Northern parts of Europe, such as Finland, (Himanen et al., 2013; Olesen 2014) Winter wheat Years Spring barley Figure 2.2: Average DM yields (t/ha) of winter wheat and spring barley for province Fyn in Denmark. Source: Statistics Denmark (DST, 2015) 2.4. Climate change impacts on crop yields The productivity of crops is determined by crop development, photosynthesis and growth (Doltra et al. 2012). These determinants are affected by abiotic factors such as temperature, solar radiation, the length of the growing season (Alcamo & Olesen 2012) as well as supply of water and nutrients which are enhanced by climate change (Hay & Porter 2006). Climate change may affect crop productivity and yield through alteration of the plant processes (Doltra et al. 2012). The environmental conditions have been strongly affecting biophysical processes of agroecosystems (Alcamo & Olesen 2012). The study by Olesen et al (2011) discussed that agricultural crop production is primarily affected by climate change in six different ways namely; (i) the effects of elevated CO2 concentration on crop productivity and resource use efficiencies as direct effect, (ii) directly through effects of temperature, rainfall, radiation, humidity etc. on crop development and growth (Olesen & Bindi 2002), (iii) directly through extreme events such as extreme heat waves, hail and flooding causing destructions, (iv) indirectly through shifts in suitability of different crops, primarily a northward expansion of warm-season crops, (v) changes in crop nutrition and occurrence of weeds, pests and diseases as indirect effect, and (vi) degradation of the resource 13

26 base, for example, soil erosion and environmental pollution including; nitrate leaching as indirect effects of climate change. The relative changes in the controlling factors and the sensitivity of the specific ecosystem will determine the exactly responses (Alcamo & Olesen 2012). Most of crop models and traditional climate change study methods regularly cover the first three among six impact pathways which may result into the bias in impact of climate change on crop production as most of indirect effects are unconsidered (Alcamo & Olesen 2012). In the view of this study report, the effect of increased CO2 concentration, temperature and precipitation will be reviewed as explained below: Carbon dioxide (CO2) The yields of most European crops are stimulated by the increased atmospheric CO2 concentration. The effect is mostly pronounced to the crops with C3 photosynthesis pathway which most of these crops are grown in Europe (Alcamo & Olesen 2012). The yield of these crops can be increased to 20-40% in response of doubling of atmospheric CO2 concentration (Olesen 2014). Plants with C4 photosynthesis pathway which include warm season plants like maize, sorghum, sugar cane, miscanthus, amaranth and millet, the response is noticeably lower (Alcamo & Olesen 2012). The consumption of water by plants is also reduced by higher CO2 concentration, and therefore, in drier conditions, the tolerance of plants might be improved, consequently leading in higher yields under dry conditions (Alcamo & Olesen 2012; Olesen 2014). This is mainly due to the fact that, the amount and the openness of stomata will be reduced with higher CO2 concentration for both C3 and C4 plants (Alcamo & Olesen 2012). Thus, under dry or drought conditions, the transpiration will be reduced and more water use efficiencies, leading into higher yields (Alcamo & Olesen 2012). A study by Alcamo & Olesen (2012) highlighted that the quality of plant biomass is also affected by higher CO2 concentrations, this is mainly due to fact that, plants accumulate more sugar resulting to leaves, stems and reproductive organs to have higher carbon contents. Consequently, this might have some negative impacts on the quality of the food and feed. And therefore, there might be a need of some adjustments in farm management, for instances; changes in the timing of harvesting grape in to get the optimal quality as well as the way cattle are fed. Also, the increased CO2 concentration can results into a more trouble weeds. Though, there might be some changes in the attraction of plants for pests and diseases, whereby plants can be more resistant to attack (Alcamo & Olesen 2012). 14

27 Temperature and rainfall The timing of crop development, the efficiency of capturing energy, the crop water supply and crop growth are different ways in which crop is primarily affected by temperature changes Trnka et al. 2011). The growing season of plants is extended due to the effect of increased temperature, this is mainly due to fact that, with warming the active growth of plants starts in advance, and therefore plants develop faster (Olesen et al. 2011). The cooler regions might experience the greatest effects (Trnka et al. 2011), and perennial crops (or crop which remain in their vegetative phase, such as; grasslands and sugar beets) might be benefited from this (Olesen 2014). Though, for many annual crops, the crop duration is reduced due to increased temperature and is applied to all cereals and seed plants such as pulses and oilseed crops (Olesen et al. 2000). For example, the length of grain filling phase in wheat is reduced by 5% with 1 C temperature increase, as well as yield drops with the same level (Olesen et al. 2000; Børgesen et al. 2011; Olesen 2014). Though, through adopting to cultivars with longer growth duration, the reduction in crop duration can frequently be more counterbalanced in the Nordic countries (Olesen et al. 2012; Olesen 2014). Thus, with possibly longer growing seasons at high latitudes, this may even result into enhanced yields (Montesino-San Martin et al. 2014; Olesen 2014). The tropical crops such as maize, soybean and cotton may experience greater sensitivity to warming, compared to temperate crops such as wheat, barley and potato, especially when they are grown at the edge of their natural range (Alcamo & Olesen 2012). This can be seen from the development in yields of grain maize over the period in central and southern Europe. In Belgium and Germany, the wheat yield has been constant while maize yield and grain maize area have been increasing (Alcamo & Olesen 2012). The crop yield reduction due to the effect of increased temperature will be more than offset by the effect of increased CO2 on crop photosynthesis (Børgesen & Olesen 2011). During growing season, the effect of rainfall on crops is largely on ensuring a supply of water is enough to cover the water lost via evapotranspiration (Alcamo & Olesen 2012). Also, crop water supply is critically depends on water plant root development and holding capacity of soil (Alcamo & Olesen 2012). When crop water demand cannot be met by either rainfall, irrigation or through soil water supply, agricultural droughts occur and over recent years, these types of droughts have occurred mostly in southern Europe (Alcamo & Olesen 2012). Crop production will also indirectly be affected by climate change via its impact on nutrient retention on agricultural fields, weeds, pests and disease and soil fertility (Alcamo & 15

28 Olesen 2012). Table 4 summarizes the direct or indirect effects of the projected increase in greenhouse gases on agroecosystems. Table 4. Influence of CO 2, temperature, rainfall and wind on various components of the agroecosystem. Component Influence of factor CO2 Temperature Rain/wind Increase of temperature boosts yield up to a threshold beyond which Decreasing precipitation Plants Higher CO2 leads to increased yield declines. or increasing wind dry matter growth and decrease Speeds up the decreases dry matter in water use. development so that the cereals have a shorter growing season. growth. Water Higher CO2 conserves soil moisture by reducing transpiration. Higher temperatures increase evaporation, leading to higher irrigation demands and in dry environments to salinization. Higher rainfall will increase groundwater supply and in some areas increase groundwater levels. Soil Higher carbon concentrations of plant residues under higher CO2 will lead to higher soil carbon content. Higher temperatures boost soil organic matter turnover, leading to reduced soil carbon content but temporarily higher nutrient supply for plants. Drier and windier environments may lead to enhanced wind erosion, whereas more intense rainfall will enhance water erosion. Pests/ disease Higher CO2 reduces the quality of plant biomass for pests and disease, leading to fewer pests. Higher temperatures reduce the generation time of pests and disease and cause attacks to occur earlier in the year, making pests and disease more problematic. Some diseases are spread by wind or rainfall. Therefore more rainy and windy conditions will favour some diseases. Weeds Enhanced CO2 concentrations will differentially favour crop and weed species. This may make some weeds more problematic. Higher CO2 will also reduce the efficacy of some herbicides. Higher temperatures will lead to invasive weed species in some regions and will also affect the efficacy of herbicides. More rainy conditions may make some weed species more difficult to control with herbicides. Source: Alcamo & Olesen, 2012; Olesen 2014). 16

29 Extreme events and climatic variability Crop production can also rigorously be disrupted by extreme weather events such as spells of high temperature, heavy storms or droughts (Alcamo & Olesen 2012). Single extreme event will have long-term effects when the frequency of the event increases and consequently the agriculture needs to react either by adjusting or by ceasing activity (Olesen 2014). Changes in growing conditions are mostly responded non-linearly by crops and responses have threshold (Olesen 2014). This makes climatic variability and frequency of extreme events for entire yield, quality and yield stability to be of greatly importance (Olesen 2014). Hence, increased yield variability attributed by increased in temperature variability might result in a decreasing in mean yields (Olesen 2014). Though, an increased in climatic variability might considerably affect crop yields in the Nordic region, yet other parts of the world are anticipated to experience more severe impacts (Olesen 2014) Factors affecting crop productivity under climate change In Denmark, the simulated changes in crop productivity is mainly influenced by changes in crop development and photosynthesis, soil mineralization and N leaching that interact with management factors including, crop rotation, methods of cultivation, sowing date, the availability of catch crops as well as the supply of N to crops (Doltra et al.2012). Also, crops response and the level of N losses to environment are largely influenced by cropping history, soil texture, clay content and soil fertility; thus, in climate change studies, it is important to consider soil and climate variability (Petit et al. 2012) Climate change impacts on Nitrate leaching and soil N cycling In Europe and over recent decades, the environmental problems including eutrophication of ecosystems, air pollution, pesticides in groundwater, loss of biodiversity have been progressively added by agriculture. However, climate is both directly and indirectly influencing these challenges (Alcamo & Olesen 2012). The aquatic environment and the natural life in the aquatic ecosystem are essentially impacted by the nitrate that is leached from farmland under current conditions (Kronvang et al. 2005). The crop rotation, N fertilization rate, crop management and climate change effects on these factors are the important aspects in which nitrate leaching depend upon. (Jeppesen et al. 2011; Børgesen & Olesen 2011). The amount of excess water percolating through 17

30 the soil profile and the concentration of mineral N in the soil water are the basic functions of the nitrate leaching from the root zone (Børgesen & Olesen 2011). The nitrate leaching is enhanced by increased percolation rates during autumn and winter influenced by large amounts of precipitation (Simmelsgaard 1998). The mineralization of soil organic N is influenced by the temperature and radiation which affect soil temperature (Thomsen et al. 2010; Børgesen & Olesen 2011). Also, during autumn and winter, the risk of nitrate leaching may be enhanced by high mineralization rate related with low N uptake by crop and more percolation (Børgesen & Olesen 2011; Patil et al. 2012). At higher temperatures, the decomposition of organic matter is mostly enhanced and then, the degradation of soil organic nitrogen is increased (Olesen 2014). As a result the risk of nitrate leaching might be increased especially when there is no or little crop cover with surplus of precipitation and percolation or runoff via soil profile (Olesen 2014). Hence, the environmental quality might decline partly due to the risk of nitrate leaching to surface and groundwater systems enhanced by the effect of higher temperatures (Patil et al. 2012). 18

31 3. Materials and Methods In this study, the Daisy version 5.19 was used. An overview of various steps conducted in the modeling is as demonstrated in Figure 3.3. This sections provide further detailed description of materials and methods used Crop model The soil-water-crop-atmosphere model Daisy (Abrahamsen & Hansen 2000) was used to analyze the effect of climate change on crop yields and soil N dynamics for winter wheat and spring barley. The Daisy is a mechanistic (physically based model) and deterministic one-dimensional agroecosystem model, which simulates N balance and losses, turnover of soil organic matter, crop growth and yield and water balance, based on the information on weather, management and soil (Hansen et al. 2012). Three main parts are comprised in the Daisy model including; (i) a soil component, simulating soil-related processes, exchange with the atmosphere, and the aerial environment of the plant (ii) a vegetation, simulating plant-related processes and (iii) a bio-climate that simulates surface processes as depicted in Figure 3.1 (Hansen et al. 2012). The process-models must parameterized in order to apply the Daisy model, and the model may be regarded as an ensemble of processes (Hansen et al. 2012). The Daisy model has been validated with good results (Smith et al. 1997; Børgesen et al. 2011). Daily climate data, information on crop management and detailed data on soil conditions are required input in Daisy model (Hansen et al. 2012) There is strong interconnection of the simulated organic matter balance and the nitrogen dynamics, so in the complete nitrogen balance model, the organic matter model has to be taken as an essential part (Hansen et al. 2012). The daily values of air temperature, precipitation and global radiation are the minimum data requirement and are considered as driving variables (weather data) (Hansen et al. 2012). The hourly values of precipitation, relative humidity, global radiation, air temperature and wind speed can be used by the Daisy model a more comprehensive information (Hansen et al. 2012).The Daisy model is simulating several processes within and between the main compartments as demonstrated in Figure

32 Driving variables: Weather Data Management Data Parameters: Soil Data Vegetation Data Bioclimate SVAT Light distribution Interception Snow accumulation Soil Uptake Turnover Sorption Transport Phase change Pesticide Nitrate Macropores Ammonium Macropores Organic Matter Macropores Heat Macropores Vegetation Growth Photosynthesis Respiration Uptake Water Soil Macropores matrix Macropores Numeric layer Figure 3.1: Schematic overview of the agro-ecosystem model Daisy A Notepad++ was used as a text editor to create different text file as it is contained in Daisy setup and then Daisy executable program was able to read the text files SimDen Model The SimDen Model was used in calculating the average N denitrification (kg N/ha) based on the recommended economical optimal N fertilizer application for winter wheat and spring barley in Denmark and then the results were used in calibration of denitrification. The SimDen Model is a simple empirical model for quantification of N2O emission and denitrification (Vinther 2005). Under Danish agricultural soils, SimDen model gives average estimates of the annual N2O emission and denitrification in soil with clay contents from 0 to 100 % using only actual clay content and amount of fertilizer as input parameters (Vinther 2005). 20

33 a) b) c) d) Figure 3.2: Then overview of the Daisy water balance (a), crop (b), nitrogen balance (c) and organic matter turnover (d). AOM = added organic matter, SMB = soil microbial biomass, and SOM = native soil organic matter; fx = portioning coefficient. The inert SOM3 pool does not interact with the rest of the system Solid lines represent flows of matter, and dashed lines represent flows of information. Source: (Hansen et al., 2012). 21

34 3.3. Study sites and climate conditions. Three study sites in Denmark were considered in this master thesis study including; (i) Flakkebjerg, (ii) Foulum and (iii) Jyndevad. The study sites provide a possibility to study and compare the impacts of different soil conditions on crop growth and soil N dynamics due to a considerable contrast in soil texture as depicted in Table 8. The climate in Denmark is temperate humid and soil types at study sites were; Loamy Sand (LS), Sandy Loam (SL) and Coarse Sandy (CS) respectively. The average annual temperatures and precipitation of, respectively; 7.5 C and 586mm for Flakkebjerg (Petit et al. 2012), 7.8 C and 740mm for Foulum and 8.9 C and 950mm for Jyndevad and the potential evapotranspiration is about 600mm year -1 (Manevski et al. 2015) Approach The overall approach for testing the hypothesis was through the use of Daisy model simulation combined with literature study to assess the impact projected climate change on crop yields and soil N dynamics. Specifically, the following aspects were considered in this study. i) To examine the impact of climate change on crop yields, four different climate models were considered in Daisy modeling process including; ECHAM5_HIRHAM5, ECHAM5_RCA3, HadCM3_HadRM3 and ARPEGE_CNRM. This has an advantage of having more comparative results as each model projected climatic conditions differently. ii) Three soil types from different sites were considered in evaluating the impacts of climate change on crop yields and Soil N dynamics in Denmark including; (i) Flakkebjerg (LS), (ii) Foulum (SL) and (iii) Jyndevad (CS). iii) To evaluate changes in winter wheat and spring barley yields and soil N dynamics in Denmark influenced by the projected climate change, three different climatic periods were considered; baseline ( ) and two projected climatic periods; 2040 ( ) and 2080 ( ). 22

35 3.5. Modeling process Prior to Daisy model simulation, various steps were considered in the modeling processes including; (i) Setting up (preparation) of the model based on the cropping systems, whereby the basic inputs, management file and run file were set up for each crop, (ii) model calibration and validation (testing) for some important parameters with regards to crop yields and Soil N while considering the average and standard values in the Danish arable farming systems and (iii) running the Daisy model to simulate the effect of projected climate change on crop yields and soil N dynamics on continuously winter wheat and spring barley crops. A summary of the various steps used in Daisy modeling is as illustrated in Figure 3.3. Figure 3.3: An overview of various steps used in this study (Daisy modelling). 23

36 3.6. Sensitivity analysis in calibrating grain yields The sensitivity analysis was conducted for the purpose of testing some important parameters related to crop growth, leaf photosynthesis and net mineralization. The model behavior based on new climate condition was evaluated through sensitivity analysis and consequently the most sensitive input parameters to be focused on during calibration were identified. In one parameter value while keeping other input parameters unchanged, a 10% systematic increase and decrease were considered and consequently the response of the crop yield and N leaching were analyzed during baseline scenario Daisy Model Calibration and validation In order to estimate parameters in a range in which variables in the model are tested on, model calibration is of importance. This can be done directly or indirectly, directly by deriving model parameters from measurements and/or indirectly by calibrating with experimental data and statistical evaluated (Haefner, 2005). According to the study by Chin, (2012), indirectly fitting approach is used in estimating parameters. In order to increase agreement between simulated and observed data, parameters were adjusted. In the view of this study, the calibration of the Daisy model was done by adjusting various parameters for baseline period ( ) while considering the observed climatic data. The purpose was to match model results on crop DM yields and soil N dynamics with the average predicted values from farm fields under the current N fertilizer application rates in Denmark (Børgesen 2015). The calibration was done for winter wheat and the same calibrated values from winter wheat were used in model simulation of spring barley. The calibration of important parameters was conducted while considering the three main areas including; (i) calibration of soil hydraulic parameters, (ii) Calibration of SOM pools and (iii) calibration of crop growth. The process of calibrating SOM pools and crop growth was simultaneous due to their interaction and influence (Manevski et al., 2015). A further calibration process is as explained and described below. Table 5 depicts the description of various parameters used in the calibration for crop modules. 24

37 Table 5. Description of various parameters used in model calibration for crop modules. Process Parameter Description DSRate1 Rate of development during vegetative DS (DS ) Phenology or crop development Leaf or canopy Photosynthesis DSRate2 Rate of development during reproductive DS (DS ) Fm Maximum CO 2 assimilation rate (g CO2/m2/h) DSEff Percent factor to control assimilate production at DS 0.0 to 2.0 SpLAI Specific leaf weight [(m 2 m -2 )/(gdmm -2 ) Assimilate partitioning Partit Assimilate partitioning at a given DS to roots, stems, leaves and storage organs Rooting depth / N uptake MaxPen Maximum depth of penetration, or rather of root solute and water uptake (cm) MxNH4Up Threshold of maximum NH4 uptake (g/cm/h) MxNO3Up Threshold of maximum NO3 uptake (g/cm/h) Organic matter C_per_N C/N ratio of specific crop organ turnover (crop residue) Fractions Division of the OM pool into the SMB, SOM and DOM pools Calibration of Soil hydraulic parameters The simulation of water and nutrient transport in the soil and crop production is largely depend on the soil hydraulic properties and SOM pool, thus set up of the soil column is important (Manevski et al., 2015). The soil hydraulic parameters were estimated using RETC (RETention Curve) as a computer application. The hydraulic conductivity curves and hydraulic parameters are estimated by RETC using brooks-corey or van Genuchten relationship respectively. Based on the calibration the soil hydraulic properties, the parameters were used in simulating water flow in all study sites are as depicted in Table 6. 25

38 Table 6.The calibrated soil hydraulic parameter for Mualem-van Genuchten model used to simulate the water flow in all study sites. Soil types Ksat (cm/ha) Sat (vol %) Res (vol %) Α (cm-1) n l Loamy sandy Sandy loam Coarse sandy Calibration of Soil Organic Matter (SOM) turnover The Daisy soil organic matter (SOM) model was parameterized considering long-term field experiments on the carbon content in Danish (Børgesen 2015). In order to mimic the medium-term soil carbon (C) turnover attained experimentally, the soil C degradation rates were adjusted for the purpose of matching the trend of soil organic matter content in arable farming system in Denmark (Børgesen 2015). It was important to consider the initial warming period of previous ten-years, for the aim of taking in consideration the influence of cropping history in modeling (Bruun & Jensen 2002). In this study, the SOM pools were adjusted for each soil type, thus influencing the rational Daisy model results for crop yields (both DM and N), N leaching and denitrification. The distribution of SOM between the slow and fast decomposition and inert pools is as shown in Table 7. 26

39 Table 7. The slow and fast decomposing and the inert SOM pools (Fractions). Soil horizon SOM1 SOM2 SOM3 Loamy sand Sandy loam Coarse sand Calibration of denitrification The denitrification was calibrated by fitting the predicted values from SimDen model with the simulated values. Water factor parameter was calibrated for each soil type with the calibrated values of 0.842, and for LS, SL and CS soils respectively Calibration of crop growth The measured grain DM yield of continuous winter wheat and spring barley in Denmark were used in calibration and validation of crop yields from the model simulation, thus improving the simulation of grain yields. Whereby, a good fit is of important before model scenario analysis. The sensitivity analysis of very important parameters was conducted to test how model respond from the simulated and observed value. In this calibration, the most important parameters relating to crop growth and development were considered. For instance, the leaf photosynthesis (Fm parameter) was adjusted to 3.80 and 2.40 for winter wheat and spring barley respectively. The Fm parameter was calibrated to improve the crop uptake CO2 and consequently attaining a reasonable grain yields based on at the study sites. Change leaf photosynthesis effectiveness (DSEff) in relation to development stage: in this analysis, the DSEff was adjusted to and for spring barley and winter wheat respectively. Root Max N uptake rates; the MxNH4up and MxNO3Up explain the upper daily N uptake rates in the rooting system which can be increased or decreased to calibrate total N uptake. With the view of this analysis, the Root Max N uptake rates were modified to MxNH4Up=1.3E-0007 and MxNO3Up=1.3E-0008 for spring barley and 27

40 MxNH4Up=1.3E-0007 and MxNO3Up=1.3E-0008 for winter wheat. Also, the concentration of N in leaves were considered in sensitivity analysis and calibration, this includes; PtLeafCnc: maximal level of leaf N; CrLeafCnc: minimum level of leaf N; NfLeafCnc: non-functional N content. Various parameters used in calibrating the crop yields are as described in Table 5. Table 11 shows some of values of parameters used in this study based on different projection periods Model set-up for scenario simulations Soil types and Crop management The Daisy model simulation were run on three representative Danish soil types including: (i) Loamy Sandy (LS), (ii) Sandy Loam (SL) and (iii) Course Sandy (CS). The detailed soil properties considered in this study is as shown in Table 8. In the simulation, the farming management practices (Table 9) including; amount of fertilizer N applied, dates of ploughing date, sowing, fertilization and harvest of crops were based on suggestions from the study by Doltra et al The amounts of fertilizer applied to each crops were centered on recent Danish recommendations (Børgesen, 2015) and were not altered in the future climate scenarios. Table 8. Details on Soil properties for three study sites (soil types). Study site (Soil type) Soil depth Clay Silt Fine sand Coarse Sand Humus BD SOM_ (K_sat) (cm) % (Mg m 3 ) C/N Flakkebjerg (Loamy Sand) Foulum (Sandy loam) Jyndevad (Coarse Sandy) SOM: Soil Organic Matter, BD: Dry Bulk Density, K: Hydraulic conductivity 28

41 Table 9. Applied mineral N fertilizer, dates of ploughing, sowing, fertilization and harvest used in the modelling for winter wheat and spring barley for baseline and projected climatic periods. Dates were obtained from Doltra et al Crop N Fertilizer applied (Kg N/ha) Plou Sow Fert 1 Fert 2 Julian Harvest days W_Wheat Sep 15-Sep 15-Sep 10-May 233(±3) S_Barley Mar 1-Apr 1-Apr 246(±3) Climate models and Meteorological data In order to simulate the effect of current and projected climate change on crop yields and soil N dynamics, Daisy model needs the daily meteorological data. Thus, four different generated climatic models were used Denmark including the ECHAM5_HIRHAM5, ECHAM5_RCA3, HadCM3_HadRM3 and ARPEGE_CNRM. The mean seasonal and annual temperatures and precipitation are as shown in (Appendix A). The changes in mean temperature and precipitation compared to the baseline period over time for different climatic models were as indicated in Figure 3.4 and 3.5 respectively. The highest changes in season and annual mean temperature and precipitation were projected by HadCM3_HadRM3 and ECHAM5_HIRHAM5 respectively. For each climatic model, the scenario analysis was done for 20-years series whereby three different projection periods were considered including; the baseline climate ( ), 2040 ( ) and 2080 ( ). 29

42 Seasonal and annula precipitation changes DJF MAM JJA SON Annual Seasona and Annual Temperature changes DJF MAM JJA SON Annual HadCM3_HadRM3 ECHAM5_RCA3 ECHAM5_HIRHAM5 ARPEGE_CNRM (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) Temperature (deg C) Figure 3.4: Mean seasonal and annual changes in temperature (degc) for two projected climatic periods; (CP2) and (CP3) from baseline period (CP1) based on four climate models in Denmark. HadCM3_HadRM3 ECHAM5_RCA3 ECHAM5_HIRHAM5 ARPEGE_CNRM (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) (CP3-CP1) (CP2-CP1) Precipitation (mm) Figure 3.5: Mean seasonal and annual changes in precipitation (mm) for two projected climatic periods; (CP2) and (CP3) from baseline period (CP1) based on four climate models in Denmark. 30

43 Simulations set-up Four distinct climate models were used in model simulation including; (i) ARPEGE_CNRM (ii) ECHAM5_HIRHAM5, (iii) ECHAM5_RCA3 and (iv) HadCM3 HadRM3. The simulation set up was done for 100-years series, whereby three climatic projection periods were considered namely; , and Three soil types; Loamy Sand, Sandy Loam and Course Sand were each used in each of the climatic model and projection period. This gave a total of 3 soil x 4 models x 3 climatic periods = 36 model run (Table 10). The Daisy model simulation was done on the continuous winter wheat and spring barley crops. For the projected periods, the changes in various parameters including the increased CO2 concentration were taken into account (Table 11), and the ModelPar_change_cresjune was used as a calculator. It is crucial to include the changes in parameters in order to get a proper simulation results with respect to projected climatic period and the required changes in parameters. Also, Daisy model cannot take into account the changes on important parameters with regards to the projected future changes in climate change. Table 10. Simulation set-up of Daisy modelling for both crops in this study W_Wheat S_Barley Soil types and Climate Models Loamy Sand ARPEGE_CNRM ECHAM5_HIRHAM5 ECHAM5_RCA3 HadCM3_HadRM3 Sandy Loam ARPEGE_CNRM ECHAM5_HIRHAM5 ECHAM5_RCA3 HadCM3_HadRM3 Coarse Sand ARPEGE_CNRM ECHAM5_HIRHAM5 ECHAM5_RCA3 HadCM3_HadRM3 : No changes in parameter were considered, : Changes in parameter were considered. 31

44 Table 11. Changes in parameters used in Daisy modeling based on baseline and future climatic periods Parameter Winter Wheat Spring Barley CO 2 (ppm) Fm Splai Nkonc relative PtLeafCnc PtStemCnc PtSOrgCnc PtLeafCnc: maximal level of leaf N; CrLeafCnc: minimum level of leaf N; NfLeafCnc: non-functional N content Daisy Model output variables and analysis The variables that were simulated and considered in this study were; (i) Crop harvest, where grain yield for both DM (t ha -1 ) and N (Kg N ha -1 ) were considered, (ii) N-Leaching (Kg N ha -1 /dt -1 ), (iii) Changes in organic N pool (Kg N ha -1 ) and (iv) denitrification (Kg N ha -1 /dt -1 ). The outputs from the Daisy model were used in the analysis. Various equations and/or constants were considered in the establishment and analysis of crop (DM) yields and Soil N dynamics as explained below; i) Nitrogen Leaching = NH4-Leak-Matrix+ NH4-Leak-Macro+ NO3-Leak-Matrix+ NO3 Leak-Macro + NO3-Drain...Equation (Eq) 1 Whereby, SI Unit are in Kg N ha -1 /dt -1 for both variables in Eq 3. ii) Organic N Pool = Residuals_N_top (Kg N ha -1 /dt -1 ) + Residuals_N_root (Kg N ha -1 /dt -1 ) + Organic_N_Surface (Kg N ha -1 ) + Organic_N_Soil (Kg N ha -1 ) Eq 2 iii) DM yields = 0.85 of crop yields (hkg ha -1 ) for both winter wheat and spring barley Eq 3 32

45 iv) Changes in organic N-Pool = Organic N PoolYear1- Organic N PoolYear0....Eq 4 In order to find the reasonable changes in organic N-pools over time in Denmark, the following equation was used in each soil type as adopted from Børgesen (2015). Y = *(x) Eq 5 Where: Y is the response of changes in organic N pool X is the percentage (%) clay content of soil types as indicated in Table Statistical analysis The statistical analyses were performed with Microsoft Excel version 2013 to examine the effects of projected climate change on crop yields (both dry matter and nitrogen) and soil N dynamics including; changes in organic N pool, N leaching and denitrification based on four different climate models and three climatic projection periods under typical Danish soil types. The climate model variation in crop yields and soil N dynamics for 20 years series were analyzed by using Standard deviation (SD). All calculations were performed in Microsoft Excel and graphs and figures were made in Microsoft Excel. 33

46 Mean DM_yields (t/ha) 4. Results This section describes the most important observations including the Daisy model calibration results and simulation results. The mean simulated results for crop yields and soil N dynamics were mainly based on four different climatic models; (i) ARPEGE_CNRM (ii) ECHAM5_HIRHAM5, (iii) ECHAM5_RCA3 and (iv) HadCM3_HadRM3. Three climatic projection periods and three soils including; loamy sandy, sandy loam and coarse sandy were considered Calibration results Calibration of DM yields The observed mean DM yields was found to be closely related to the simulated grain DM yield for both crops under study (Figure 4.1). In comparing the observed DM yield based on average values from Danish statistics and simulated DM yields for the period of , the agreement between observed and simulated DM yield were indicated. The observed and simulated mean DM yields for both winter wheat and spring barley for the period of is shown in Figure 4.2. The observed and simulated DM yields related with most of soil types, Figure 4.1: Observed and simulated mean sorg DM yields for winter wheat and spring barley but with variation in SL and CS for winter wheat and CS soil for spring barley Observed (Fyn province) Simulated (sandy Loam) Winter wheat Observed (Fyn province) Spring barley Simulated (sandy Loam) 34

47 N leaching (kg N/ha) Mean DM yields (kg/ha) Observed climate ARPEGE_CNRM ECHAM5_HIRHAM5 ECHAM5_RCA3 HadCM3_HadRM3 Loamy Sand Sandy Loam Coarse Sandy Loamy sand Sandy loam Coarse sand W_Wheat Crops and soil types S_Barley Figure 4.2: Mean DM yields (kg/ha) from observed climate and model simulations for winter wheat and spring barley based on baseline climatic period ( ) Calibration for N leaching Figure 4.3 shows the observed and model simulated mean N leaching for both winter wheat and spring barley for baseline period ( ). In both crops and on CS soil, the observed N leaching was higher than in most of climate models, except for HadCM3_HadRM3 model. The observed and simulated N leaching was found to be in agreement on LS soil in both crops and on SL for spring barley Observed climate ARPEGE_CNRM ECHAM5_HIRHAM5 ECHAM5_RCA3 HadCM3_HadRM Loamy Sand Sandy Loam Coarse Sandy Loamy sand Sandy loam Coarse sand W_Wheat Crop and soil types S_Barley Figure 4.3: Mean N leaching (kg N/ha) from Observed climate and model simulations for winter wheat and spring barley based on baseline climatic period ( ). 35

48 Change in Org N Pool (kg N/ha) Mean N-Denitr (kg N/ha/dt) Calibration for denitrification The denitrification was calibrated with reference to the denitrification levels based on results from SimDen model for each crop as shown in (Figure 4.4). The calibration for average denitrification levels was based on the economically optimal N fertilizer application for both winter wheat and spring barley in Denmark as indicated in Table 9. The denitrification from observed climate found to be in accordance with the denitrification predicted by SimDen model on each soil SimDen Model Loamy Sand Sandy Loam Coarse Sandy Observed Climate Winter Wheat SimDen Model Spring Barley Observed Climate Figure 4.4: Mean denitrification (kg N/ha under three soil types based on SimDen Model and simulated for baseline period ( ) Calibration for Change in organic N pool. Calibration of the mean changes in organic N pool in Denmark was based on the long-term field experiments on the carbon content in Denmark as adopted from (Børgesen (2015). The purpose of calibrating changes in soil organic N pool was to match with the typical soil C degradation rates in arable farming system in Denmark. The soil C degradation rates from measured and model simulation for baseline climatic period ( ) is as depicted in Figure 4.5. The predicted and simulated mean changes in organic N pool was in accordance for winter wheat crop than spring barley Predicted Simulated (Winter wheat) Loamy Sand Sandy Loam Coarse Sandy Simulated (Spring barley) Figure 4.5: Mean predicted and simulated change in Organic N Pool (kg N/ha) under three soil types and for climatic period

49 4.2. Simulation results The simulated results on the projected impact of climate change on crop yields and soil N dynamics are presented in this section Grain yields response Table 12 depicts the mean grain yield (t DM ha -1 ) of winter wheat and spring barley based on four climatic model simulation and three soil types and under three different climatic periods. Under projected future climatic conditions, the trend of grain DM yields of winter wheat and spring barley is projected differently by climatic models for different climatic periods and soil types. The higher DM yields were simulated in loamy sand compared to other soils for both crops. In case of winter wheat and by 2099, the yield reduction of 1.4 t ha -1 (40.6%) on CS soil and 0.9 t ha -1 (18.7%) on LS soil is projected by ARPEGE_CNRM and HadCM3_HadRM3 climate models respectively. Under the average of all four models and by 2099, the reduction of DM yield for winter wheat was projected by 0.7 t ha -1 (11.4%), 0.4 t ha -1 (12.1%) and 0.2 t ha -1 (13.5%) on LS, SL, CS soils respectively, in comparison with the baseline period (Figure 4.6a). For 2060 projection, the DM yield increase of 1.2 t ha -1 (17.1%) on SL and 0.9 t ha -1 (17.5%) on CS soils is projected by HadCM3_HadRM3 and ECHAM5_HIRHAM5 climate models respectively. The slight increase in DM yield can be explained by the reduced water stress days for the projected climatic periods compared to the baseline climatic periods. Taking the average of all four models and by 2060, the DM yields of winter wheat is slightly projected to increase by 0.1 t ha -1 (1.5%), 0.3 t ha -1 (4.7%) and 0.3 t ha -1 (5.9%) on LS, SL, CS soils respectively (Figure 4.6a). In case of spring barley, the HadCM3_HadRM3 model predicted the highest reduction in DM yields in all soil types, with the highest reduction of 1.6 t ha -1 (152.2%) on CS soil by By 2099, DM yield reduction of 0.4 t ha -1 in SL soil as well as 0.3 t ha -1 on LS were predicted by ARPEGE_CNRM and ECHAM5_HIRHAM5 climate models respectively. The highest predicted reduction in DM yield by HadCM3_HadRM3 found to be in agreement with the highertemperature projected by HadCM3_HadRM3 model in The slight increase in DM yields of 1.1 t ha -1 (30.4%) by 2099 and 0.8 t ha -1 (22.2%) by 2060 both on CS soil in comparison with the baseline period based on ECHAM5_HIRHAM5 and ARPEGE_CNRM climate model projections (Figure 4.6b) respectively. The increase in DM yield might be due to the influence of reduced water stress days in 2060 from most of the models (Appendix B) as well as relative lower temperature increase predicted by ECHAM5_HIRHAM5 model. 37

50 The changes in yield for both winter wheat and spring barley crops from baseline period was observed from four models and under three contrasting soil conditions. Based on the calculated Standard Deviation (SD) for 20-years series (Table 12) on three soils and projection periods, the highest variation in the simulated DM yields of winter wheat and spring barley were depicted by HadCM3_HadRM3 and ARPEGE_CNRM models. Table 12. Simulated average DM yields (t/ha) presented in value and Standard Deviation (SD) of winter wheat and spring barley as predicted for the baseline and projected climatic periods by four climatic models based on Loamy Sand, Sandy Loam and Coarse Sand soils. Soil types and Climate Models W_Wheat S_Barley Yield SD Yield SD Yield SD Yield SD Yield SD Yield SD Loamy Sandy CLM CLM CLM CLM Sandy Loam CLM CLM CLM CLM Coarse Sandy CLM CLM CLM CLM SD: Standard Deviation, CLM: Climate Model (1: ARPEGE_CNRM, 2: ECHAM5_HIRHAM5, 3: ECHAM5_RCA3, 4: HadCM3_HadRM3. 38

51 Soil types and changes in projection periods Loamy Sandy Sandy Loam Coarse Sandy Soil types and changes in projection periods Loamy Sandy Sandy Loam Coarse Sandy a) HadCM3_HadRM3 ECHAM5_RCA3 ECHAM5_HIRHAM5 ARPEGE_CNRM CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP Changes in mean DM Yields (t/ha) from baseline period b) HadCM3_HadRM3 ECHAM5_RCA3 ECHAM5_HIRHAM5 ARPEGE_CNRM CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP Changes in mean DM Yields (t/ha) from baseline period Figure 4.6: Changes in mean grain yield (t DM ha -1 ) for projected Climatic Period 2 (CP2_ ) and Climatic Period 3 (CP3_ ) from baseline period (CP1_ ) based on three soil types and four climatic models for winter wheat (a) and spring barley (b). 39

52 Table 13 shows the mean Nitrogen yields (kg N ha -1 ) with associated standard deviation of winter wheat and spring barley crops. Under projected future climatic conditions, the trend of N yields (kg N ha -1 ) of winter wheat and spring barley was generally projected to decrease in most of climate models (Table 13). In case of winter wheat and by 2060, the N yield is projected to slightly decrease from most of models (Figure 4.7a) by 8.9 kg N ha -1 (9.1%) on CS and 4.4 kg N ha -1 (3.2%) on CS soils for ARPEGE_CNRM and ECHAM5_HIRHAM5 climate models respectively. By 2099, N yields were observed to decrease greatly by 27.6 kg N ha -1 (35.1%) on CS soil, 17.2 kg N ha -1 (14.3%) and 16.4 kgn ha -1 (13.1%) on LS based on ARPEGE_CNRM, HadCM3_HadRM3 and ECHAM5_RCA3 models (Figure 4.7a). If the projected changes in N yield is averaged from all four models and by 2099, a more reduction in N yields were found by 14.1 kg N ha -1 (11.3%), 7.7 kg N ha -1 (6.5%) and 7.6 kg N ha -1 (9.8%) on LS, SL, CS soils respectively. Table 13. Simulated mean N yields (kg/ha) and Standard Deviation (SD) of winter wheat and spring barley as predicted for the baseline climate and projected climate by different climatic models under Loamy Sand, Sandy Loam and Coarse Sandy soils. Soil types and Models Loamy Sandy W_Wheat S_Barley Yield SD Yield SD Yield SD Yield SD Yield SD Yield SD CLM CLM CLM CLM Sandy Loam CLM CLM CLM CLM Coarse Sandy CLM CLM CLM CLM SD: Standard Deviation, CLM: Climate Model (1: ARPEGE_CNRM, 2: ECHAM5_HIRHAM5, 3: ECHAM5_RCA3, 4: HadCM3_HadRM3. 40

53 Generally, the reduction trend of N yield for spring barley was found in most of climatic models (Figure 4.7b), with the relative lower reduction by 2060 compared to By 2099, the highest reduction in N yield were 35.5 kgn ha -1 (46.6 %) on LS soil for both ECHAM5_HIRHAM5 and ECHAM5_RCA3 climate models and 33.2 kgn ha -1 (41.2%) on LS soil based on HadCM3_HadRM3, while a relative lower decline in N yields was found in CS soil (Figure 4.7b). The highest reduction in N yield based on HadCM3_HadRM3 was related to increased temperature (Figure3.4). Taking the average of the four models and by 2099, the highest reduction were; 32.8 kgn ha -1 (41.7%), 27 kgn ha -1 (36.6%) and 6.5 kgn ha -1 (15.2%) on LS, SL, CS soils respectively. This reduction was greater than for 2060 period. Based on the calculated standard deviation, the HadCM3_HadRM3 and ARPEGE_CNRM were found to have the highest variation in the simulated mean N yields from the mean over 20-years in all soil types and projection periods. The HadCM3_HadRM3 indicated the largest variation in mean N yields for winter wheat than spring barley crops (Table 13). 41

54 Soil types and changes in projection periods Loamy Sandy Sandy Loam Coarse Sandy Soil types and climatic periods Loamy Sandy Sandy Loam Coarse Sandy a) HadCM3_HadRM3, ECHAM5_RCA3, ECHAM5_HIRHAM5, ARPEGE_CNRM, CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP Changes mean in N yields (Kg N/ha) from baseline period b) HadCM3_HadRM3 ECHAM5_RCA3 ECHAM5_HIRHAM5 ARPEGE_CNRM CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP Changes in mean N yields (Kg N/ha) from baseline period Figure 4.7: Changes in simulated mean N yields (kg ha -1 ) for projected Climatic Period 2 (CP2_ ) and Climatic Period 3 (CP3_ ) from baseline period (CP1_ ) based on three soil types and four climatic models for winter wheat (a) and spring barley (b). 42

55 Changes in Julian harvest days Table 14 shows the trend in Julian harvest days for both winter wheat and spring barley crop based on different model predictions and three projection periods. The trends of the Julian harvest days were found to decrease in all climate models and under all soil types when compared with the baseline climatic periods (Figure 4.8). For 2099 projection, the highest decrease of Julian harvest days of 15 days for both crops was in agreement with the models that projected a more increase in future temperature including HadCM3_HadRM3 and ARPEGE_CNRM models (figure 3.4). A slight decrease in Julian harvest days were predicted by the less warm models such as, ECHAM5_HIRHAM5 with the reduction of 5 days and 3 days for winter wheat and spring barley respectively. The highest decline in Julian harvest days were shown by 2099 as compared to 2060 climatic period. The reduction in Julian harvest days for the crops can be explained by the influence of projected increase in temperature that enhance crop development and thus expected to reduce grain yields (Doltra et al. 2012) Table 14. Effects of projected climate changes on crop growing days based on four different climatic models and in three climatic projection periods. W_Wheat Sowing date ARPEGE_CNRM ECHAM5_HIRHAM5 ECHAM5_RCA3 HadCM3_HadRM3 Harv Harvest day Harv Harvest day Harv Harvest day Harv Harvest day Sep 24-Jul Jul Jul Jul Sep 17-Jul Jul Jul Jul Sep 9-Jul Jul Jul Jul 220 S_Barley Apr 8-Aug Aug Aug Aug Apr 6-Aug Aug Aug Jul Apr 30-Jul Jul Jul Jul

56 Crops and changes in projection periods W_wheat S_Barley The decrease in Julian harvest days for both crops in this study sites in Denmark was in agreement with the findings from other studies. For instance; a study by Doltra et al. (2012) under FASSET simulations demonstrated that, at the end of century and in comparison with baseline situation, the duration of flowering to maturity of winter would be decreased by 9 days. The decrease in 6 Julian harvest days was generally noted for continental eastern Denmark (Olesen 2005). HadCM3_HadRM3 ECHAM5_RCA3 ECHAM5_HIRHAM5 ARPEGE_CNRM CP3-CP1 CP2-CP1 CP3-CP1 CP2-CP Changes in Julian harvest days (days) from baseline reriod Figure 4.8: Simulated changes in Julian harvest days of winter wheat and spring barley for the projected climatic periods (CP2_2040, CP3_2080) from baseline period (CP1_ ) based on four climatic models and under Responses on changes in organic N pool The trend of changes in mean organic N pool (Kg N ha -1 ) were generally predicted to increase under future climatic conditions in most of climate models. Under all soil types, the highest increase in changes on organic N pool were shown to be by 2099 compared to 2060 climatic period in comparison with baseline climatic period. This is in agreement with the model prediction for a more increase in temperature by 2099 compared to 2060 climatic period (Figure 4.9). The largest and lowest simulated changes in mean organic N pool were found to be in loamy sand and coarse sandy soils respectively (Figures 4.9 a, b). This can be explained by the high quantity 44

57 of organic matter (Table 12) over LS as well as soil water content compared to other soil types. On LS and by 2099 (Figure 15a), the highest changes in mean organic N pool from baseline climatic periods were 14 KgN ha -1 for winter wheat and 34.9 KgN ha -1 for spring barley based on ARPEGE_CNRM and HadCM3_HadRM3 climate model predictions respectively. By 2099 and in SL soil, the highest changes in mean organic N pool were predicted to be 14.4Kg N ha -1 for winter wheat and 24.1Kg N ha -1 for spring barley based on ECHAM5_HIRHAM5 and HadCM3_HadRM3 climate model respectively (Figure 4.9b). Taking CS soil and by 2099, the highest increased changes in mean organic N pool were predicted to be 7.5 Kg N ha -1 for winter wheat and 14.1 Kg N ha -1 for spring barley under ARPEGE_CNRM and HadCM3_HadRM3 climate model predictions respectively (Figure 4.9c). Most of the models that predict the highest increased changes in the mean organic N pool were projecting an increased in temperature under future climatic conditions (Figure 3.4). Taking the average for all four mean and by 2099, the changes in organic N pool for winter wheat were predicted to increase by 12.2 Kg N ha -1 (26.4%), 7.8 Kg N ha -1 (26.6%) and decrease by 0.7 Kg N ha -1 (50.2%) on LS, SL, CS soils respectively. While, in case of spring barley, the simulated mean changes in organic N pool were predicted to increase by 25.3 Kg N ha -1 (32%), 19.3 Kg N ha -1 (34.1%) and 9.3 Kg N ha -1 (31%) on LS, SL, CS soils respectively. Generally, the trend of mean changes in organic N pool was found to be higher in spring barley than winter wheat (Figures 4.9 a, b, and c). Table 15 shows the standard deviation (SD) of all four climate model prediction on mean changes in organic N pool for winter wheat and spring barley crops under three soil types and climatic periods. The highest variation in the simulated mean changes in organic N pool for 20-years was depicted by HadCM3_HadRM3 and ARPEGE_CNRM models, mostly in winter wheat than spring barley crop. 45

58 Changes in Organic N pool (Kg N/ha) Changes in Organic N pool (Kg N/ha Changes in Organic N pool (Kg/ha) a) W_Wheat S_Barley a) Loamy Sandy b) ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. b) W_Wheat S_Barley b) Sandy loam ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. c) W_Wheat S_Barley c) Coarse sandy ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. Figure 4.9: Simulated average changes in organic N pool (kg N ha -1 ) for winter wheat and spring barley as predicted for the baseline and two projected climatic periods based on four climatic models under Loamy Sand (a), Sandy Loam (b) and Coarse Sand (c) soil. 46

59 Table 15. Standard deviation (SD) and standard error (SE) of changes in organic N pool based on different climate models and on three soil types and three projection periods for winter wheat and spring barley. Soil types and Models W_Wheat S_Barley Loamy Sandy SD SE SD SE SD SE SD SE SD SE SD SE CLM CLM CLM CLM Sandy Loam CLM CLM CLM CLM Coarse Sandy CLM CLM CLM CLM CLM: Climate Model (1: ARPEGE_CNRM, 2: ECHAM5_HIRHAM5, 3: ECHAM5_RCA3, 4: HadCM3_HadRM Responses on nitrate leaching The simulation results show an increase in the mean N leaching (Kg N ha -1 ) for winter wheat and spring barley based on most of the model and on soil types and projection periods in comparison with the baseline period as depicted in (Figure 4.10 a, b c). An increase in N leaching is largely predicted over 2099 compared to 2060 climatic period, this found to be in agreement with the highest projected increase in future temperature and rainfall by 2099 which can influence high N loss. The mean N leaching was found to be higher in spring barley compared to winter wheat (Figure 4.10 a, b, c), which might be due to the higher yield levels in spring barley compared to winter wheat (Table 13). Generally, the largest N leaching was depicted in CS compared with other soils, this could be partly influenced by low capacity of root zone. In case of winter wheat and by 2099, the highest changes in mean N leaching from baseline period were found to be 29.1 KgN ha -1 (41%) and 17.6 Kg N ha -1 (55%) as predicted by ARPEGE_CNRM 47

60 climate model in CS and SL soils respectively (Figure 4.10 b, c). This trend might be partly influenced by the projected increase in temperature and rainfall that can influence a high organic matter turnover as projected by ARPEGE_CNRM climate model. There was a slight decrease in changes of N leaching simulated by HadCM3_HadRM3 model (Figure 4.10 b c), which could be due to the slight decrease in predicted changes in organic N pool (i.e. less mineralization) compared to the baseline period under SL and CS soils. For the spring barley crop and for 2099 projection, the largest changes in mean N leaching for the projected climatic periods compared to the baseline periods were 60.9 kg N ha -1 (60.2%) and 53.7 kg N ha -1 (55.7%) in LS and SL soils respectively as projected by HadCM3_HadRM3 as a climate model that predicted the highest increase in temperatures. The lowest changes in mean N leaching for 2099 was generally projected by a less warm ECHAM5_HIRHAM5 model by 2.5 kg N ha -1 (2.6%) in CS (Figure 4.10), this agreed with the influence of increased temperature in enhancing the rate of organic matter turnover and thus net-mineralization. For 20 years, the highest variation in mean N leaching was revealed by HadCM3_HadRM3 then ARPEGE_CNRM model for both winter wheat and spring barley crops, particularly on CS soil followed by SL soil (Table 16). 48

61 N leaching (kg/ha) N leaching (kg/ha) N leaching (kg/ha) ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. a) Loamy sand W_Wheat S_Barley ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. b) Sandy loam W_Wheat S_Barley ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. c) Coarse sandy W_Wheat Crops and projection periods S_Barley Figure 4.10: Simulated mean Nitrate Leaching (kg N/ha) for winter wheat and spring barley crops as predicted in three climatic periods by four different climatic models and three soils; (a) Loamy sand, (b) sandy loam and (c) course sand. Bars indicate the standard error (SE). 49

62 Table 16. Standard deviation (SD) of mean N leaching (kg ha - 1) based on different climate models and on three soil types and three projection periods for winter wheat and spring barley. Soil types and Models W_Wheat S_Barley Loamy Sandy ARPEGE_CNRM ECHAM5_HIRHAM ECHAM5_RCA HadCM3_HadRM Sandy Loam ARPEGE_CNRM ECHAM5_HIRHAM ECHAM5_RCA HadCM3_HadRM Coarse Sandy ARPEGE_CNRM ECHAM5_HIRHAM ECHAM5_RCA HadCM3_HadRM Responses on denitrification Figures 18a, b, c show the mean denitrification (kg N ha -1 ) with associated standard error mean (SEM) for winter wheat and spring barley as projected by four different climate models and under three soils; LS, SL and CS soils. In either of the crop, the LS showed the highest denitrification levels and the lowest on CS soil. The different patterns of denitrification between models and climatic periods were observed, with the highest denitrification levels predicted more by 2099 compared to 2060 projection. In case of winter wheat and by 2099, the highest denitrification rates were; 52.8 kg N ha -1, 30.3 kg N ha -1 and 5.9 kg N ha -1 on LS, SL and CS soils respectively. The largest denitrification levels for spring barley crop were; 57.4 kg N ha -1, 21 kg N ha -1 and 2.8 kg N ha -1 on LS, SL and CS soils respectively. The highest denitrification levels were projected by ECHAM5_HIRHAM5 model (Figures 18a, b and c), as the model that projected the highest rainfall increase by 2099 (Figure 3.5), which might influence a high denitrification levels. 50

63 Taking the change in mean denitrification for the projected climatic period from baseline periods and for winter wheat, the highest positive changes was observed to be 34.9 kg N ha -1 (66.1%) increased by 2099 in LS soil as projected by ECHAM5_HIRHAM5 model. The ARPEGE_CNRM model predicted the negative change in mean denitrification by 1.7 kg N ha -1 (-8.8%) on LS, this was in agreement with the lower seasonal and annual rainfall (Figure 3.5) projected by ARPEGE_CNRM model that could influence a lower denitrification. In case of spring barley and by 2099, the highest positive change in mean denitrification from baseline period was 42 kg N ha - 1 (73.2%) on LS predicted by ECHAM5_HIRHAM5 model. The ARPEGE_CNRM estimated the lowest positive changes in mean denitrification of 6.1 kg N ha -1 (22.3%), 3.8 kg N ha -1 (28.4) and 0.03 kg N ha -1 (2.6%) on LS, SL and CS soils respectively. The standard error (SE) in different models found to overlap within the same climatic period, except for ECHAM5_HIRHAM5 model (Figure 4.11 c). (Figures 4.11 a, b, c). Based on calculated SD, HadCM3_HadRM3 model depicted the highest variation in mean denitrification from the mean on SL soil and for winter wheat. 51

64 Denitrification (kg N/ha) Denitrification (kg N/ha) Denitrification (kg N/ha) ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM a) Loamy sand W_Wheat S_Barley ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM3. b) Sandy loam W_Wheat S_Barley c) Coarse sandy ARPEGE_CNRM. ECHAM5_HIRHAM5. ECHAM5_RCA3. HadCM3_HadRM W_Wheat S_Barley Crop and projection periods Figure 4.11: Simulated mean denitrification (kg/ha) for winter wheat and spring barley crops as predicted in three climatic periods by four different climatic models and three soils; (a) Loamy Sand, (b) Sandy Loam and (c) Course Sand. Bars indicate the standard error (SE). 52

65 Table 17. Standard deviation (SD) of mean denitrification (kg ha - 1) based on different climate models and on three soil types and three projection periods for winter wheat and spring barley. Soil types and Models W_Wheat S_Barley Loamy Sandy ARPEGE_CNRM ECHAM5_HIRHAM ECHAM5_RCA HadCM3_HadRM Sandy Loam ARPEGE_CNRM ECHAM5_HIRHAM ECHAM5_RCA HadCM3_HadRM Coarse Sandy ARPEGE_CNRM ECHAM5_HIRHAM ECHAM5_RCA HadCM3_HadRM

66 5. Discussion 5.1. Climate change effects on grain yield Climate change and increasing atmospheric CO2 is expected to impact the crop production globally if adaptation measures and technological progress are not taken into account (Rotter 2012). Climate change may affect crop productivity and yield through alteration of the plant processes (Doltra et al. 2012). Alcamo and Olesen (2012) highlighted that projected temperature increase is expected to have the largest effects in cold regions like Denmark. Crop development is expected to be enhanced by the projected temperature increases thus shortening the growing period as a results reduction in grain yield is likely (Doltra et al. 2012). The variations in climatic conditions, soils and land-use type have been reported to determine the impact of climate change across Europe (Petit et al. 2012). In this study, four climate models projected responses in in both DM and N yields differently on three contrasting soil conditions and three projection periods for both winter wheat and spring barley crops. Though, the general reduction of grain yields (DM and N) for both winter wheat and spring barley were largely pronounced by 2099 climatic period in comparison with the baseline period (Figures 4.6 a, b and 4.7 a, b). In case of winter wheat and by 2060, the climate model HadCM3_HadRM3 and ECHAM5_HIRHAM5 projected a slight increase in DM yields, this might be due to the influence of higher water stress days on crop growth during the baseline period compared to the 2060 as depicted by mentioned models (Appendix B). The general reduction in grain DM yield was indicated by warm models ARPEGE_CNRM and HadCM3_HadRM3 models by This could be due to the effect of elevated temperature projected by these models for 2099 projection (Figure 3.4). In case of spring barley, the HadCM3_HadRM3 model predicted the reduction in mean DM yields in all soil types by 2060, with the largest decrease in DM yield on CS soil. The reduction in DM yield by HadCM3_HadRM3 was in agreement with the increased temperature projected by HadCM3_HadRM3 for 2099 projection. Olesen (2005) elaborated that the CS soil has larger yield decrease for spring barley mainly influenced by increased temperature compared to SL. The slightly increase in grain DM yield in all crops could be due to the effect of increased CO2 concentration considered in this study for the future climatic periods. This was in accordance with the findings from 54

67 Olesen (2005) who discuss that the yield reduction due to increased temperature in all crops can be offset by the influence of increased CO2 concentration. However in some cases, when high temperature increase combined with small CO2 concentration, then the net yield reduction occur (Olesen 2005). In this study and for 2099 projection, the highest decrease in N yields was projected by most of the models such as; ARPEGE_CNRM, HadCM3_HadRM3 and ECHAM5_RCA3 and based on contrasting soil conditions for winter wheat (Figure 4.7a) and spring barley (Figure 4.7b). Reduction in N yields might be due to the influence of projected temperature increase on crop growth by 2099 as predicted by most of models, thus reducing growing period. The findings from this study were mostly in agreement with other studies; for instance, a study by (Kristensen et al. 2010; Bindi 2010) reported a considerable reduction in cereal grain yield as climate change may play greater role in slowing the growth in yields. The simulated grain yield has reported to be influenced mainly by both temperature and precipitation (Børgesen & Olesen 2011). The duration of crop growth during the active growth period is mostly shortened by increased temperature and thus reducing yield (Kristensen et al. 2011). During crop grain-filling period, the reduction in yield is greatly enhanced by higher temperatures (Børgesen & Olesen 2011). The reduction in Julian harvest days has been reported in this study, the Julian harvest days were found to decrease differently depending on climate models and projection period (Figure 4.8). Figure 5.1. Mean simulated change in yield of winter wheat, spring barley and ryegrass at increasing temperatures for a site in Denmark. Source: (Olesen 2014). According to the study by Olesen et al. (2000), the winter wheat and spring barley responded differently to increased temperature, where yield reductions with increasing mean temperatures was predicted by using the CLIMCROP model in Denmark. Petit et al. (2011) discussed that winter wheat had the largest reduction in grain yield in which the period of crop growth is reduced in Denmark (Figure 5.1). This was mainly due to the vegetative and reproductive phases being affected by the changes in sowing date in spring and summer (Petit et al. 2011). Spring barley 55

68 demonstrated a minor response to higher temperate and was mainly because of the earlier sowing of the crop during spring and therefore be able to maintain a productive growing season (Olesen 2014). During the flowering period, the temperature above 35 C can severely affect the seed and fruit and consequently reducing yields (Olesen 2014). The yield increases of 8 2 and 13 8% for winter wheat and reductions of 22 and 34 8% for spring barley, for the 2040 and 2080 projections on arable farm system, respectively on a SL soil were revealed (Doltra et al. 2012). This was in agreement with the slight increase in winter wheat crop for the 2060 projections (figure 4.7a) and greatly reduction in spring barley for 2099 projections (Figure 4.7b). Based on the projection periods, climate models and soil types considered; the yield reduction for winter wheat in Denmark is estimated to differ from approximately 2 to 12% (Patil et al. 2012; Doltra et al. 2012). This has also been depicted in this study (Figure 4.6a). Studies by Olesen et al. (2011) and Elsgaard et al. (2012) elaborated that the grain maize can be introduced in the rotation in southern Scandinavia for the purpose of avoiding the reduction in cereal grain yields as grain maize will be positively favored with the projected future temperature increases (Table 18). Petit et al. (2012) explained that the rise in mean temperature above baseline period (1-5 C) decreased grain yield at both study sites (Flakkebjerg and Jyndevad) in Denmark, however the average reduction in grain yield was greater at Flakkebjerg (0.25 Mg DM ha -1 ) compared with at Jyndevad (0.1 Mg DM ha -1 ), which could be due to the higher yields at Flakkebjerg (LS) compared to Jyndevad (CS). According to Southworth et al. (2002) and Petit et al. (2011) the higher variability in crop yields enhanced by increased variability of both temperature and precipitation leading into reductions in future wheat yields was reported. The grain yield reduction in this study was found to be generally higher for 2099 projections compared to 2060 projection. This might be due to the influence of an increased CO2 concentration as indicated in the study by Olesen and Bindi (2002) whereby results showed that photosynthesis was mostly enhanced by elevated CO2 concentration and thus lowering of transpiration. Thus, this might help to offset decreasing grain yield caused by higher temperatures and this yield benefit is reported to be more depicted in the short term projection compared to the long term projection (Børgesen & Olesen 2011) 56

69 5.2. Climate change effect on soil Nitrogen dynamics Changes in organic N pool The simulation results indicate an increase in changes in mean organic N pool with projected climate change for both crops and soil types studied. The greatest changes in organic N pool were revealed on LS than other soils and projected by the warm climate model, HadCM3_HadRM3. This was in agreement with the higher temperatures projected by HadCM3_HadRM3 for 2099 projections (Figure 3.4) as well as high soil water and organic matter contents on LS than others. This is in accordance with other studies of climate change impacts on changes in organic N pool from cereal cropping systems in Denmark. For instance; Børgesen & Olesen (2011) highlighted that, the organic turnover is expected to be enhanced by increasing warming, while taking into account the enough amount of water is available. This study found that the rate of net N mineralization might be substantially higher compared to the increase in soil respiration influenced by warmer climate and thus might result into high risk of N loss (Turner and Henry 2010; Børgesen & Olesen 2011). A study by Olesen et al. (2004) elaborated that the breakdown of organic matter is anticipated to be stimulated by warmer climate resulting into the release of mineral N from agricultural soils. The findings from Jeppesen et al. (2011) indicated that the Northern latitudes are expected to experience the increased in N cycling and leaching. This was reported to be due to the higher precipitation surplus during winter and autumn and expected extreme warming (Petit et al. 2011). In the regions, for instance; north and east Europe where temperature increases and soil moisture is sufficient, the decomposition of organic matter becomes faster (Bindi, 2010). 57

70 Nitrogen Leaching In humid region, the loss of nitrogen into aquifers and surface waters is driven mainly by intensive agriculture, and thus posing threats to the quality of surface and ground waters (Abrahamsen 2000). The effect of climate change on the environmental impacts emanating from agriculture activities are taken as important aspect (Bindi 2010), particularly, a global and local problem of nitrate leaching on the quality of river, estuaries and aquifers (Jeppesen et al. 2009; Bindi 2010). In the view of this study, the increasing trend in N leaching was indicated in both crops and from most of climate models, soil types and across all projection periods. The largest N leaching was predicted for the 2099 compared to 2060 projection, this might be related to the higher increasing turnover of organic matter for 2099 projection influenced by higher projected temperatures and precipitation as showed in various climate models (Figures 3.4 and 3.5). For both crops, the largest N leaching was projected by a warm climate model, HadCM3_HadRM3 on all soil types and across projection periods (Figure 4.11). The highest N leaching was on CS compared to other soil. This was found to be in agreement with the projected increased temperatures on changes in organic N pool as projected by HadCM3_HadRM3 model (Figure 4.9). The findings from this study was in accordance with the findings from other studies. For instance, the studies by Børgesen & Olesen (2011) and Patil et al. (2012) on the climate change on N leaching from cereal cropping systems in Denmark observed an increasing in N leaching due to the impacts of climate change. Apart from analysing crop rotation changes, Doltra et al. (2012) looked at climate change impacts on N leaching. An indirect effect of climate change is closely related to several factors such as increased amounts of precipitation, heavy rainfall, faster mineralization processes and longer duration of bare soils due to later sowing and earlier harvest (Doltra et al. 2012). Doltra et al. (2012) projected that all future cropping systems will have larger N leaching rates (Figure 5.2), whereby the N leaching was higher on CS than SL soil in arable farm. This was found to in agreement with the results from this study (Figures 4.10 a, b, c). The cereal crops have reported to be responsible for the increase of N leaching in the future in a range of kg N/ha by 2080 (Doltra et al. 2012). All systems showed significant overall increasing in N leaching, however, one model estimated a higher leaching than the other (Doltra et al. 2012). Trnka et al. (2011) also concluded that increased input of N fertilizer will be required due to the extended growing season. As productivity depends on the limiting factors, the nutrient requirements increases and increase 58

71 of fertilization rates is most likely needed. Henriksen et al. (2013) assumed an unchanged level of N fertilization due to aquatic environmental protection, and if this proves to be correct, increase in N leaching is potentially little. However, maintaining current N levels can minimize the positive productivity potentials of increased temperature and CO2 levels. Different factors that contribute to an increased N leaching under climate change have been reported including; (i) if the soil is not covered, higher temperatures and during autumn and winter, enhances the N mineralization resulting to larger N leaching (Thomsen et al. 2010), (ii) reduced growing season with later sowing especially for winter cereals, as well as the risk of N leaching due to a long period in autumn influenced by earlier harvest and without soil cover (Doltra et al. 2012) and (iii) if no or little vegetation cover is considered and with high rainfall during winter, this might result into higher risk of leaching to available soil mineral N (Doltra et al. 2012). Several related factors of climate change effect on N dynamic in agroecosystems including; the plant C/N ration is increased by the higher CO2 sequestration influenced by larger CO2 concentrations (Børgesen & Olesen 2011), which in turn increase the dead organic substrates to the soils and consequently might influence soil C and N dynamics (Børgesen & Olesen 2011). Also, a study by Olesen et al. (2004) indicated that, the risk of N loss mostly through N leaching can be enhanced by the fact that under warmer climate and prolong period of bare soil in autumn together with the influence of higher temperatures on soil organic matter turnover (Turner & Henry 2010). A study by Doltra et al. (2012) explained that N leaching would mostly be enhanced by the mineralized N from soil organic matter, if the current applied N fertilizer rate is kept moderate in Denmark. Figure 5.2: Annual nitrogen leaching (NO 3-N+NH4- N) in individual crops of each crop rotation for the baseline period and two projected climate scenarios. Source; Doltra et al