BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | Biogeosciences Discuss., 11, 9103–9134, 2014 www.biogeosciences-discuss.net/11/9103/2014/ doi:10.5194/bgd-11-9103-2014 © Author(s) 2014. CC Attribution 3.0 License. This discussion paper is/has been under review for the journal Biogeosciences (BG). Please refer to the corresponding final paper in BG if available. Biomass yield development of early, medium and late Maize varieties under a future climate in Lower Saxony, Germany J. F. Degener and M. Kappas University of Göttingen, Institute of Geography, Göttingen, Germany Received: 30 April 2014 – Accepted: 2 June 2014 – Published: 16 June 2014 Correspondence to: J. F. Degener (jdegene@gwdg.de) Published by Copernicus Publications on behalf of the European Geosciences Union. 9103 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | Abstract Lower Saxony, with a total land area of about 46 500 km2, constitutes one of the most important agricultural areas in Germany and thus within Europe. Roughly one third of its agricultural area is used for maize cultivation and as of today only few information exist on how a future changing climate will affect its local growing conditions. Thus the5 newly developed carbon-based crop model BioSTAR and a high-resolution regional cli- mate data-set (WETTREG) were used to evaluate the change in biomass yields of an early, medium and late maize variety. The climate input data is based on the SRES A1B scenario, with a potential fertilization effect or better still, an increased water use effi- ciency due to rising CO2 levels, taken into account. The biomass yield for all varieties10 was calculated for each year from 2001 until 2099 on a total of 91 014 sites. The results suggest clearly differentiated development paths of all varieties. All three show a sig- nificant positive trend until the end of the century. However the medium variety shows a statistical significant decline of 5% during the first 30 years and only a slight recovery towards +5% around the century’s end. The late variety has the clearest and strongest15 positive trend, with partially more than 30% increase of biomass yields around the end of the century or +25% mean increase in the last three decades. The early variety can be seen as in-between, with no negative but also not an as strong positive develop- ment path. All varieties have their strongest increase in yields after the mid of the 21st century. Statistical evaluation of these results suggests that the shift from a summer20 rain to a winter rain climate in Germany will be the main limiting factor for all varieties. In addition summer temperatures will become less optimal for all maize crops. Only if the plants can supply themselves sufficiently with water outside of the increasingly dry summer months, when also temperatures are much more favorable, an increase in biomass yields is feasible. As the data suggests the increasing atmospheric CO225 concentrations will play a critical role in reducing the crops water uptake, thus enabling yield increases in the first place. 9104 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 1 Introduction With a production of around 875Mt, maize was the second most grown crop on earth in 2012, only surpassed by sugarcane and surpassing rice (3rd 718Mt) and wheat (4th 675Mt). However in terms of nutrition, rice and wheat provided around 3.8 times more calories to the world’s average human (FAO, 2014). This spread in the data is a clear5 indicator for the variety of usage that maize allows for, from its first and foremost use as feed for livestock to a raw material for energy purposes. As of 2012, like most years before, Lower Saxony (LS) constituted Germany’s largest maize producer, accounting for more than a quarter of the 94.56Mt total German pro- duction, while extending over merely 13% of Germany’s overall territory. This is due to10 an over-average yield of 50.6 t ha−1 (avg. Germany 46.4 t ha−1) combined with a rela- tive large cropping area of 27% (avg. Germany 17%) of the total utilized agricultural area (DeStatis, 2013). Around the early 1980’s the cropping area of LS for silage Maize leveled out at around 220000 ha for several years. Around 2004 this began to change rapidly. Within15 five years the area nearly doubled, after less than than a decade the area already amounted to 514 000ha in 2012 (LWK, 2014a). An early look into the matter (Hoeher, 2007) did not show any increase in the local livestock nor a dramatic change in livestock diet or related im- or exports. Even more, the maize cropping area for feed receded by 30 000ha between 2004 and 2007. Energy Maize however, in LS used predominantly20 as a regenerative power source, showed an increase in cropping area by 38 000ha in only one year. Therefore it can be safely assumed that this increase in cropping area was due to reasons other than livestock farming. While there are some propositions for alternatives to this extensive maize cultivation (NMELVL, 2012; LWK, 2014b) its known production strategies and biomass yields will make it hard for any competing crop to25 replace maize. Thus it can be assumed that maize will be around for some time, raising the question how changing regional or local conditions will affect its yield potential. 9105 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | Wolf and van Diepen (1995) did an early estimation of the European grain maize yield potential, basically coming to the conclusion that no large changes are to be expected for the central part of the European Community and thus for LS. This outlook has not changed dramatically in present day studies (Supit et al., 2012), generally suggesting no trend or seldom a positive trend in rainfed maize yields for most parts of Germany.5 Spatial surveys covering only Germany in its entirety are relatively rare. However, it is often pointed out that maize already grows near optimal conditions in Germany and is as of today already limited through drought stress in its main growing period of July and August (Meyer et al., 2009; Taube and Herrmann, 2009). The expected further decline in summer precipitation of around 30% for some areas in Germany (Jacob10 et al., 2008), however uncertain this change might be, would thus strongly limit the growing conditions of maize in these regions. In part probably owed to the administrative structure in Germany, the assessment of climate change impacts on crops yields was mostly done on a federal state level. A wide variety of approaches (differences in climate model and dataset, crop model,15 reference period etc.) make a direct comparison often difficult at least. However, results from regions close to LS are still of special interest for comparison. The federal state of Hesse, directly to the south of LS, shows a regional differentiated pattern with a positive maize yield trend (up to +15%) in the southern part and a neutral to negative (mostly around −10%) northern part in middle of the 21st century under the20 SRES B2 scenario (USF, 2005). To the south-west of LS lies North Rhine-Westphalia. Fröhlich (2010) did show that most of the state will profit from a changing climate from an silage maize yield increase of around 2–4% (B1 scenario) or 3–7% (A1B scenario) until 2050. Saxony and Thuringia, both to the south-east of LS, have a generally neg- ative development until 2050 (A1B) with a decline in maize yields of roughly −10%25 (Mirschel et al., 2008, 2012). Especially the study for Thuringia does show how wide these results may spread, even if climate model, scenario and crop model are kept the same. Four alternative approaches, including more or less progress in cultivation and 9106 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | breeding, further differentiated by dry or moist conditions, resulted here in an average yield changes from −8.2% to +38.6%. Furthermore Buttlar et al. (2013) took a closer look at a part of LS, the region con- necting the cities of Hanover, Brunswick, Göttingen and Hildesheim. This study was however rather site specific, with biomass yield changes of maize between −3% and5 +7% (until mid-century) and −4% to +13% (end of century). While the current study does not expect to diverge largely from these findings, a re- gional or even local approach was necessary as a probable basis for action of regional decision makers. An important difference to the mentioned studies lies however in the selection of different maize varieties. For simplification many studies omit the use of dif-10 ferent varieties that are differentiated only by their required temperature sums to reach their respective development stages. As Southworth et al. (2000) could show in a study in the Midwestern United States this differentiation can indeed make a difference, as heat-resistant late (or long-term) varieties did show a considerably better yield devel- opment in a future climate than varieties with less temperature requirements. However15 rare, if studies do evaluate distinct varieties, the findings are similar as Liu et al. (2013) could show for Northeast China. Most studies however only hint in a more general way towards the influence of variety choice (Wolf and van Diepen, 1994; Kwabiah, 2004; Meza et al., 2008). 2 Materials and methods20 The basic approach used in this study was to use high-resolution climate data in com- bination with detailed soil information as the input for a crop model. All components involved are introduced in the following sections. 9107 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 2.1 Research area Lower Saxony (LS), with roughly 46 500 km2 of land area, is the second largest of25 the 16 federal states of Germany, providing around 15% of the nations agricultural land (DeStatis, 2013). Located to the North-West of Germany (Fig. 1) the state lies in a transition zone between a more maritime (NW) towards a more continental climate (SE) (Seedorf and Meyer, 1992) with an average annual temperature of around 9 ◦C and a mean precipitation of 749mm in the period of 1971–2000 (DWD, 2014).5 Principally LS consists of three distinguishable landscape structures: the coast, in- cluding the East Frisian Islands, the German North-Western Lowland (amounting for three quarters of LS’ total land area) as well as a low mountain range to its south, with the Harz as its most prominent representative (Drachenfels, 2010). The broad loess valleys to the south and especially the fertile “Börde” that fronts the low moun-10 tain range to the north are the main cultivation areas for high-demand crops like winter wheat. The Lowland mainly consists of “Geest”-land, Quaternary sediments that are particularly sandy to the North-East, with precipitation as low as 500mm, making ir- rigation already today necessary on several sites. The west of LS is dominated by livestock farming with the coastal area predominantly used for grassland farming as15 high ground water levels prevent intensive use (Heunisch et al., 2007). The regional differences manifest themselves in the average regional yields. In the period of 2003–2008 the average winter wheat yield south of Hanover was always above 8 t ha−1, above 7 t ha−1 south of Oldenburg and generally below 7 t ha−1 in the North-East. Maize yields behave rather similar, with dry maize silage (33% dry matter20 content) having the best yields to the south. The margin between the different parts of LS is however smaller for maize than for wheat and varies generally around 15 t ha−1. As can be seen in Fig. 1 the areas with the largest maize production coincide with areas where only little wheat is grown and where feed for livestock is in high demand. 9108 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 2.2 Crop model25 The crop model used in this study is a relatively new model called BioSTAR, developed at the Georg-August-University in Göttingen (Bauböck, 2013). The model uses a CO2 based crop development engine, thus taking a potential CO2 fertilization effect into account. The basic working principle uses temperature to determine the plants development stages and a combination of temperature, solar radiation and CO2 concentration the maximum photosynthesis rate. Both incrementally build the plants maximum possible biomass that is then limited by precipitation respectively the soil water content. The5 model is suitable for large-scale as well as parcel size yield assessments. The philos- ophy behind it is an easy to use model with a robust output and a manageable amount of required input parameters. The model was validated on sample sites in Lower Saxony with a general disagree- ment between actual and modeled yield of around 10%. The required climatic input10 variables are precipitation, temperature, atmospheric CO2 concentration, solar radia- tion, relative air humidity and wind-speed at 2m altitude. In addition, information on the soil type is required. As the model was initially conceived as a tool for the estimation of bio-energy potentials, the maize crops only contain silage maize (no food maize). Fur- thermore the three varieties do not consist of single breeds but represent an average15 of several early, medium or late breeds respectively. As a rather robust approach the model leaves out some aspects that might well be of great importance for a future yield development. Results in this study should thus be read as what would happen if nothing but the climatic input variables would change. These neglected aspects include any technological advances, including any changes20 in farm management. Irrigation was not included in the modeling, whether for current nor future yields, even if there do exist some areas today that are under irrigation. The sowing date was always the 115th day of the year and was not changed throughout the century. No extra fertilization was included and soil water content expected to be at 9109 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 100% at the beginning of the growing season. No effects of a prior crop on a specific25 site are taken into account. The model either stopped on day 300 of a given year or when full maturity was attained, depending on what happened first. At the time of the actual modeling the BioSTAR model could be understood as being in beta stage development. All crops had been validated, however some minor changes in the model engine and more heavy changes in the programs layout have happened since. No version number was in use when the model was applied. 2.3 Soil data5 The soil data used in this study is part of the official digital soil survey map of LS in a resolution of 1 : 50000, called BÜK50 (Boess et al., 2004). This map was intersected with data from the CORINE land-use classification of 2005 for Lower Saxony to extract sites that are used for agricultural purposes only. The result between the intersected soil and land-use map was a data-set of 91 014 sites with each used a as unique10 modeling area. The soil map contained codified information on the soil type and its thickness that were translated into the format required by BioSTAR. Fifteen 10 cm soil levels had to be identified, each containing the information on prevalent soil type with a 16th level representing everything below the initial 1.5m. The crop model uses these information solely for the calculation of soil water content and flows.15 2.4 Climate model and data The climate data was derived from the regional climate model WETTREG, a Ger- man portmanteau word translating into “weather condition based regional model”. The model uses a statistical downscaling method where large scale atmospheric patterns are brought into a statistical relationship with local climate station data (Enke and20 Spekat, 1997). The initial link is created by using known measured data at these sta- tions and globally gridded reanalysis data, with both ERA40 and NCEP/NCAR data using a k-means cluster approach. This link is then reestablished through GCM de- 9110 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | rived gridded data, here from the ECHAM5 global climate model. For each large scale weather pattern of the future a pool of local station data is available that is then resam-25 pled several times to create the climate signal (Enke et al., 2005). The actual climate models name is WETTREG 2010, as the initial approach (today called WETTREG 2006) neglected weather patterns that are relatively rare today but will increasingly emerge in a future climate. Thus two patterns were added to this latest version, significantly reducing the model bias in comparison to other climate models (Kreienkamp et al., 2010). WETTREG 2010 was applied at 248 stations dis- tributed throughout LS, whereas the mean of 10 iterations at each station was used5 as the climate signal for the 21st century (A1B SRES scenario). Using spatial inter- polation methodology these point based information were further upscaled to a grid of 100m×100m at the Jülich Research Centre through the CLINT interpolation model (Müller et al., 2012). This resulted in a grid of 11 520000 data points for each time step (with 10-day-values amounting to 36 single steps per year) for temperature, precipita-10 tion and potential evapotranspiration. The data was available for the years 1961–2100 with an additional data-set of interpolated measured station data from Germany’s Na- tional Meteorological Service (DWD) for the years 1961–2005 for validation purposes. Both data-sets agreed reasonably well in temperature and precipitation (with WET- TREG 2010 showing a mean annual average bias of +0.02 ◦C and −2.24% precipita-15 tion). Furthermore data on global radiation was taken from a run of ECHAM5 in a global T31 grid of 48×96 that was calculated within the scope of the ENSEMBLES project (Roeckner, 2009). The ECHAM5 data was chosen for the purpose of data consistency as the WETTREG2010 data did also employ ECHAM5 runs for the boundary condi-20 tions. The data-set was provided for the years 2001 until 2099 thus setting the limits for this study’s timeframe. Global radiation was calculated as the sum of surface net downward shortwave flux and surface net downward longwave flux. Wind speed was taken from official maps of LS of 2005 provided through the State Authority for Mining, Energy and Geology (LBEG) that uses the FAO approach for wind25 9111 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | speed in a height of 2m above grass. Typical wind-speed ranges from 5–6ms−1 at the coast to around 1–2ms−1 in the south of LS. To present knowledge no significant change in the wind speed pattern is anticipated for the future (NMUEK, 2012), hence the data was applied without further changes. Relative Air Humidity was calculated backwards from the WETTREG2010 data on evapotranspiration, as this was derived through the Penman/Monteith approach. All data was then intersected with the soil sites using the respective variables mean value.5 2.5 Statistics To account for extreme or unrealistic outliers, a two-way approach was devised for the original resulting data-set. At first all sites with a biomass yield of 0 gm−2 were excluded. This typically amounted to 706 sites that contain only bedrock in their soil levels. In a second step all data below the 0.1 and above the 99.9 percentile were ex-10 cluded, as values close to zero or unreasonably large yields were present. This proved to well eliminate outliers while preserving as much data as possible. Basic statistics in this study include standard deviation, coefficient of variability (cov), linear regression models and the coefficient of determination as described in Schön- wiese (2006). The time series could well be described using linear regression models,15 however tests with exponential, logarithmic, second and third order polynomial and potential models did show about equal results. The data was further explicitly tested for trends using a robust trend/noise ratio (t/n), where the difference in yield from the years 2099 and 2001 was divided through the time series’ standard deviation. A significant trend is assumed at a ratio of 1.96 or20 above, representing the α = 0.05 level. As this test is often considered to be relatively weak, the non-parametric Mann–Kendall-Test (MK) was applied as well. A further ad- vantage of MK is its ability to detect non-linear trends. Most statistics were applied for the time series of 2001–2030, 2001–2050 and 2001–2099. 9112 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | Data comparing first (2001–2050) and second (2051–2099) half of the century will25 sometimes give a ratio of 1st/2nd half. As the second half is here only 49 years long, only a ratio of 0.505 would mean that the value is equal for both half’s. To determine the climatic variables that significantly influence the yield development throughout the century a multivariate regression model was used. In a first step 11 vari- ables were included in the model that was then run for all sites. These variables include respectively 5 temperature and precipitation values (annual, winter, spring, summer, fall mean) as well as atmospheric CO2 concentration. This was done to get a general test of strength of all variables against each other at different sites. However, autocorrela-5 tion is very likely to occur, as at least temperature trends seem to be relatively equal across the five variables. Therefore a best-model approach was devised. 11 variables can be assembled into 2047 unique groups when their order is neglected. Each combi- nation was treated as a new model and calculated on 3740 randomly distributed sites. The multivariate model that explained the yield development best was then logged. If10 combinations gave equally good results, the first run, generally the one with less vari- ables, was logged. This was done for the years 2001–2099 as well as 2001–2050 to identify possible changes in variable impact. The statistics in this study have been calculated using MS Excel 2010 and Python (v 2.7) with the addition of SCIPY and NUMPY (Jones et al., 2001–), Pandas (Pandas,15 2012) and MATPLOTLIB (Hunter, 2007). The calculation of the multivariate regression models was done using R (v 2.15.2) and rpy2 (v 2.3.0). 3 Climate change in Lower Saxony Figure 2 gives a brief description of the average change of the climatic variables tem- perature and precipitation in LS. The climatic comparison is done by 30-year intervals20 where 1971–2000 is used as present-day climate that might be seen as more current than the climatic normal period of 1961–1990 (WMO, 2011). These intervals represent 9113 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | a near (2011–2040), middle (2041–2070) and long-term (2071–2100) climatic devel- opment. There are no areas at any time that do show a stagnant or even decreasing temper-25 ature development. However, warming in spring is always below the annual average while the winter months are always above. Fall temperatures are slightly below annual average and summer months above, though both deviate less from the mean than spring and winter seasons do. The mean temperature increase is 0.95 ◦C for near, 2.30 ◦C for middle and 3.40 ◦C for long-term scenarios. The development is relatively uniform throughout LS with a slightly stronger (but still less than 0.5 ◦C difference) de-5 velopment to the south-east. The precipitation development is different in terms of being positive or negative de- pending on time and space. If only annual means are considered, almost no change in precipitation can be detected, although a moderate decline is visible. It however becomes increasingly obvious that the winter and summer seasons are drifting into10 opposite directions. While in the near future all seasonal differences remain in a win- dow of more or less ±10%, these changes drastically amplify towards the end. The mean decline in precipitation is around −25% in the long-term perspective with some areas at a nearly −50% decrease. Winter increases are also substantial but at around 15% towards the end of the century they cannot fully counterbalance the summery15 losses. In summary, all deviations from today’s values will increase with passing time, foster- ing a local development towards a more winter rain climate that features increasingly hot and dry summers and mild wet winters. 4 Results20 The results in this study will describe the change in biomass yields during the 21st century. Changes are relative to the mean yields of the decade 2001–2010 as a repre- sentation of the present time. 9114 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 4.1 Mean yield development The results in this section give the average yield development of all modeled sites.25 As can be seen in Fig. 3 all three varieties visually show a positive yield development throughout the century. This is further underpinned by the actual biomass yields af- ter 2060, where the average yield per decade is always higher than for the reference period. Not as evident is the shared pattern of the decadic coefficient of variability. All three varieties have their lowest value in the present (3–4%) with an increase (except5 for the comparably low variability between 2031–2040) towards mid-century (above 8%) and a slight decline towards 6–7% at the end of the century. Actual yields will therefore vary more widely around the decadic mean at mid-century. Apart from these shared aspects there are also obvious differences in the overall development. The following description will thus cover each variety on its own.10 The early variety shows an R2 = 0.24 and an average increase in yields of 0.12% p.a. throughout the century. This trend could be slightly better explained through a poly- nomial model of second or third order with R2 = 0.27, however no big advantage would be expected from such an approach. The trend/noise (t/n) ratio 1.68 shows no sig- nificant trend for α = 0.95 but would for α = 0.9. Mann–Kendall delivers a more unam-15 biguous result with p < 0.001 over the century. It is therefore assumed that a significant trend exists throughout the entire time-period. This trend can basically be split into two parts: the period 2001–2050 has an R2 = 0.003 in a linear regression model with an average yield development of ±0% p.a. The period 2051–2099 has an R2 = 0.11 with an average yield increase of 0.2% p.a.20 The lack of a trend in the first half of the century is confirmed by its t/n ratio of 0.5 and p = 0.39 for MK. For the period 2001–2030 there even seems to appear a slight negative development, with a t/n ratio of −0.63 and a p = 0.08 for MK that is however not recognized as being significant. All in all it seems clear that a change in biomass yields is expected to happen,25 however only after the mid of the century and especially after 2070 when there is only 9115 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | one year with a critically lower yield than the present average. In these last thirty years the yields are about 9% higher than in the first decade, with the last decade being the one with the overall highest yields. If total production of early maize would be calculated over the century, 49.1% would be produced during the first half. The medium variety has an R2 = 0.15 and an average increase in yields of 0.09% p.a. over the century. As with the early variety a slightly better explanation is provided through a polynomial model with R2 = 0.19. A t/n ratio of 1.34 indicates no linear trend while a p < 0.001 for MK assumes a significant trend. These numbers represent5 the lowest indicator for a trend throughout the century of all varieties. This is due to a different break within the data that occurs around 2030 and is still present when the year is shifted ±10, though weaker. The period 2001–2030 shows a linear decline in yields of around −0.2% p.a. and an R2 = 0.16. From 2031 on this turns towards a positive trend of +0.13% p.a. and an R2 = 0.14. While the early variety10 did also show signs for a decline in yields, the data for the medium variety supports it more strongly. While a t/n ratio of −1.3 fails to be significant, the MK with p = 0.005 is, therefore a significant negative trend until 2030 is assumed. On average this decline will reduce the yields about 5%. This trend is then reversed towards the end of the century, in such a way that around15 2070 the yields are mostly above the present average. However, this happens in a lower magnitude than for the early variety. If only the period 2001–2050 is considered, t/n (0.01) and MK (p = 0.95) are both highly insignificant, meaning that the average yields are not changing. As this is generally comparable to the early variety, the yield variability is somewhat larger for the medium variety.20 The medium variety will thus have the least positive development in the 21st century. Yields after 2070 will on average be 5% above today’s. A comparison of both half’s of the century has 49.4% of a potential production happen in the first 50 years, again the highest value of all varieties. The late variety is somewhat of an exception. Where early and medium variety show25 at least minor comparability, the late variety has a uniquely positive development path. 9116 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | This is evident by just looking at the graph as well as in the numbers of the linear regres- sion model with an R2 = 0.65 and an average yield increase of 0.31% p.a. A change to another regression model does not show any different results. Also the t/n ratio (2.74) and Mann–Kendall (p < 0.001) are more explicit in determining significance than for the other varieties. However clear, this trend is not entirely constant. While the t/n ratio (2.31) and MK (p < 0.001) already show a highly significant trend towards 2050 the data seems to have a break around the year 2030. There appears to be no trend for the period 2001–5 2030 as a t/n ratio of 0.65 and p = 0.35 for MK suggest. A linear regression for 2001– 2030 shows an R2 = 0.09 and a mean yield increase of 0.11% p.a., a non-significant but positive trend, whereas 2031–2099 has a R2 = 0.37 and an increase of 0.26% p.a. The late variety has thus the most positive development throughout the century. The mean yields in the last three decades increase about at least 10% compared to today,10 with a mean of 25% and a maximum of 36%. The same holds true for a production comparison of both half-centuries, as the first 50 years would only contribute 47.1% to a potential overall production. 4.2 Regional yield development As Fig. 4 indicates there are also certain differences in the regional distribution of15 potential yields increases or decreases. The late variety does clearly have the most uniform development as it is positive for almost all times and sites. The share of sites with a positive development lies around 53% for the early variety in the period and increases to 87%, 96% and 95% towards the end of the century. In a similar fashion the medium variety starts out at very low share of 16% positive sites, increasing to20 49%, 88% and 83%. If the coefficient of variation is calculated regarding all sites and years from the re- spective periods, all three varieties show an increasing cov with progressing time. With 9.3% (2021–2040) to 10.7% (2081–2099) the late variety does have the least variabil- 9117 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | ity. The early (10.4 to 11.5%) and medium variety (10.0 to 11.5%) have a rather similar25 variation in their yields. The overall best sites are situated in the west of LS. Two main areas with a below- average yield development can be identified. One is to the north or north-east of LS, the other one to the south. This southern area is however not affected uniformly but rather quite positive and quite negative sites are alternating. The negative sites are consistently those with an overall shallow profile, situated on the slopes of the hilly landscape. By contrast, the sites with a positive development lie within the fertile river5 valleys with their good soil quality. Good soil quality is here defined only through the soils ability to retain water, basically defined by its field capacity. 4.3 Process analysis To determine the relative influence of certain variables on crop growth, a multivariate10 linear regression model was applied with the results shown in Fig. 5. On the left side single parameters are tested against each other for their quantitative and qualitative input strength. For example, a positive correlation of +4 for medium maize and CO2 indicates that a rise in atmospheric CO2 concentrations by 1 ppm leads to a yield in- crease of 4 gm−2. A negative correlation, as for example for summer temperature and15 medium variety, would however stand for a decline in yields by roughly 100 gm−2 if summer temperatures would rise by 1 ◦C. The shown results are the mean output of all sampled sites. Though problems due to some autocorrelation were expected, the linear multivariate models performed quite well. For 2001–2099 the mean model p value over all sites was < 0.001 for all varieties.20 For the late variety this holds true not just for the mean but even if all sites are regarded individually. The early and medium varieties did however contain around 1300 (1.5%) sites of less significance, which were however still within a margin of 0.01 > p > 0.001. The models concerning the first half of the century, 2001–2050, show slightly worse results. Though the mean p value of all models is still < 0.001, the number of models25 9118 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | with higher p values increased. Even the late variety now did have around 1500 sites exceeding this threshold, with early and medium variety on about 7000 sites. Roughly a third of these exceptional sites have p values > 0.05. In conclusion, the models are slightly better for the description of the long-term development than for the first half of the century. For 2001–2099 two main influence variables are detected. Summer precipitation shows a strong positive correlation for all three varieties. As the amount of rain is ex- pected to drastically decline throughout the century, this seems to be the main factor5 to limit future maize yields. On the other hand, atmospheric CO2 concentrations have a comparable positive correlation and are thus possibly the main agent for a positive yield development. The amount of spring precipitation seems to be of higher impor- tance for medium (p = 0.09) and especially early (p = 0.04) variety. Both do also show a negative connection with the rising summer temperatures (early p = 0.09, medium10 p = 0.08), at least to some degree. The late variety shows basically similar depen- dencies, however weaker. Instead spring temperatures (p < 0.01) seem to be of much higher importance than for the other two varieties. For 2001–2050 these indicators change only slightly. Still summer precipitation and CO2 concentrations remain the determining variables (p < 0.001). The late va-15 riety still shows some dependency towards spring temperatures (p = 0.09). For all three varieties fall temperatures seem to be of higher importance in the first 50 years (0.1 > p > 0.05), whereas summer temperatures and spring precipitation have no ap- parent influence. That these multivariate models are not entirely perfect becomes evident when for ex-20 ample winter precipitation and late maize are considered for 2001–2099. While not be- ing highly significant a certain connection between both variables is suggested. How- ever, as winter months include December, January and February, when no maize is grown, this also seems to be highly improbable. While the statistical model was be- lieved to be reasonably good in determining the relative influence of each variable,25 there was a need to exclude variables that are not necessarily important. 9119 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | Therefore the next step was to identify the one linear multivariate regression model for each site that best describes its yield development, as shown on the right side in Fig. 5. These results were largely in accordance with the results from the models with eleven input variables. Models in the following approach are numbered from R1 to R2047. R is here short for Run while the number indicates the variable combination. Higher numbers relate to more input variables. The relevant numbers are explained further in Fig. 5. For 2001–2099 the model best describing the yield development of all varieties was5 one containing only summer precipitation and CO2 concentrations (R37). 92% of the early variety, 80% of the medium variety and 85% of the late variety sites had this as the optimal model. The remaining sites of the early variety were best described by a model only containing CO2 concentrations (R10). The same is partially true for the late variety, as 3% of the models show their best results when only including CO210 (R10), however models that only used spring temperatures (R6) accounted for the remaining 12%. This connection to spring temperatures was also be identified in the models featuring all variables. The two runs R6 and R10 have a combined share of about 10% of the medium variety’s remaining sites, while the other remaining 10% are a combination of summer precipitation, summer temperatures and CO2 concentration15 (R171). For the period 2001–2050 the varieties did show a more differentiated picture. The late variety did still have R37 as the dominant model on 90% of its sites. 5% were made up of R6 and another 5% of other not further distinguished models. The early variety had R37 on just 58% of its sites, 5% showing R171 and almost the entire rest20 of 34% from R2 with summer precipitation only. The medium variety had only have 24% comprising of R37, 6% of R171 and a dominating 66% of R2. 9120 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 5 Discussion The results from this study basically agree with the findings of e.g. Southworth et al. (2000) in that the choice of variety will have a critical effect on how maize yields will25 develop under a future climate. It even agrees to the point that late varieties will show the most positive development which can be quite substantial with > 25% in Lower Saxony towards the end of the century. One reason for this beneficial development of late maize is clearly the fact that to- day’s temperature sums in LS are not suitable for a full completion of its growing cycle. Temperature sums from 20 April to 15 October (minus 6 ◦C temperature basis) vary today around 1500 ◦C in LS and are therefore perfect for medium varieties but below5 optimum for late varieties. It seems that around 2030, when temperature sums have increased by about 100 ◦C, the late variety can fully benefit from these temperatures. That the late variety disproportionately benefits from the generally rising temperatures is further supported by statistical analysis, as the late variety is the only one to show a substantial positive correlation to rising spring temperatures.10 The future climatic condition are however not entirely beneficial for the growth of late maize varieties. The main limiting factor, for all varieties, is the decline in summer precipitation. However, the time spent within these dry months in relation to the total growing time is shorter for the late variety as e.g. the medium one. Thus the late variety can use the moister spring or fall conditions for a successful growth. Similarly the early15 variety profits from the spring conditions while the medium variety would need more water during the summer months of which August will be the driest. An adaption of sowing date could mitigate these negative effects to some degree. Some testing on single sites however suggests that the general yield development series of late>early >medium variety is not changed, though the absolute difference20 might change. The influence of the sowing date on the results of this work are currently under evaluation. 9121 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | In a related matter, critical development stages of maize, e.g. during flowering, where water shortage disproportionately restrains plant growth (Ehlers, 1996), were not suf- ficiently implemented in the used version of the crop model. This has changed in the25 current version and is under evaluation as well. While a shift in sowing dates is ex- pected to be generally beneficial for maize yields, this increase in sensitivity is believed to have a rather detrimental effect. While increasing temperatures are generally good for the late variety, temperatures during the growing season should not exceed an optimum growing temperatures of 25– 30 ◦C over longer time periods, as this would inhibit photosynthesis rates (Endlicher, 2007). However, heat days will increase in LS throughout the century. The effects of this are visible in the multivariate regression models with an increasing negative impact of5 summer temperatures on yields. In reality this effect is expected to be even worse. The main reason is the use of a relatively smooth time-series. This is firstly caused by the statistical nature of the climate model. The usage of mean values from 10 climate model runs tends to elim- inate extreme values. Secondly, the results of this downscaled climate data-set were10 10-day-values that were further combined into monthly averages. Temperature peaks were thus eliminated within the monthly means. The same is true for the monthly val- ues of precipitation, as BioSTAR simply assumes that the monthly value is distributed evenly over each day of the month. This is clearly not the case in nature where a steady flow of water would be optimal for the plants water supply. It will make some difference15 if 20mm precipitates in one day followed by nine dry days or if 2mm for each of the ten days is assumed. While a shortage in precipitation might be worse than elevated tem- peratures, it can be mitigated relatively easy by irrigation while the latter can hardly be opposed. As some areas in LS are already today under irrigation it would be interest- ing to estimate probable changes in irrigation practices, meaning an estimation of the20 amount of water needed for optimal growth and taking the actually available amount of water into account. 9122 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | That even relatively smooth precipitation series lead to varying yields becomes evi- dent when the observed decadic variability of the yields is compared with the variability of precipitation. More precisely the yields have their greatest variability in the decade25 2051–2060, when summer precipitation has the greatest variability too. The same link can be found in the decade 2021–2030 but not for 2031–2050, as both decades have either high yield or high precipitation variability but not both. The underlying cause for the change in yield variability therefore seems to be more complex than a single de- pendency on summer precipitation. There further exist some climatic circumstances that might negatively affect yield development that were not accounted for in this study. One is tropospheric ozone, as 30 ppb are sufficient to induce ozone intoxication in plants (Long et al., 2006). Since 1950 the concentration of tropospheric ozone has nearly doubled. Studies suggest5 that maize yields might be 2–5.5% higher today if this rise would not have happened (Avnery et al., 2011). It is however debatable if tropospheric ozone concentrations will further increase, at least in Europe, due to anthropogenic emission as CMIP5 runs suggest (Fiore et al., 2012). A greater potential risk arises through common or invasive pests. Complicated in-10 teractions and feedbacks between climate, crop and pests make concise predictions difficult (Schaller and Weigel, 2007). However, as Fröhlich (2010) points out, there is no expectation at all that the climatic change will lead to a reduction in infestation of any pest. In how far new cropping techniques or breeds will be able to counteract such problems is beyond the scope of this study.15 The general outlook considering the climatic changes could thus be interpreted as quite severe. However, the results from this study suggest the contrary with rising yields towards the century’s end. The only variable contributing significantly towards rising yields is atmospheric CO2. Maize as a C4 crop is not expected to profit from rising CO2 through an elevated20 photosynthesis rate (Lambers et al., 2008). However an increased water use efficiency is expected in C3 as well as C4 plants. This effect is accounted for by the crop model, 9123 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | resulting in a relatively linear decrease in the amount of water that is needed to produce the same amount of plant matter. The reduction is comparable for all three varieties and ranges between 25–30%.25 In an environment where water is getting increasingly scarce this is a desirable devel- opment. It appears that the negative impacts of summer temperature and precipitation are stronger until mid-century, especially for medium or early variety. The positive influ- ence of CO2 steadily increases to a point where the positive effects prevail and yields are rising. That water saving through increased CO2 concentrations can have such a strong effect is also pointed out by Taube and Herrmann (2009), where Grasslands profit from a rise even under increasing drought stress during summer months. This would be in5 line with Morgan et al. (2004) who are emphasizing the importance of water saving through increased CO2 in contrast to a direct fertilization effect. CO2 might still not be solely responsible for the rising yields. It undoubtedly plays a major role in doing so, however other factors that have not been included in the pro- cess evaluation might contribute as well. Mera et al. (2006) included the effect of solar10 radiation in their research and found a non-linear contribution to yield development, however not as prominent as changes in precipitation or water availability. 6 Conclusions As could be shown, the changing climate will have a predominantly positive effect on the yield development of maize and its varieties in Lower Saxony. A real positive devel-15 opment is however not expected to set in before the second half of the 21st century. The first half will be stagnant in yields for the early variety. In the last decades of the century the yields will on average increase about 9%. The medium variety even shows a negative development in the first half that is later reversed. Towards the century’s end the yields then increase about 5% in comparison to today’s yields. The late variety20 has the all-out best yield development, with an average increase of 25% for 2071– 9124 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 2099 and a strong positive trend beginning already around 2030. In addition to this above-average rise the yields itself are higher so that a transition of local agricultural practices towards the late variety is conceivable. The yield development of all yields is accompanied by an increase in yield variability during mid-century that seems to25 partially follow precipitation patterns. Thus the development will generally be positive in the long run, though the path for each variety diverges. As the varieties react in different ways to the changing annual pattern of temperature and precipitation, the results do indicate that the consideration of different varieties might also change the outcome of studies at different study sites. At any rate the few other existing studies are hinting towards the same result. Varieties5 with longer or shorter growing periods will have an advantage in areas where medium varieties are predominantly grown today. Besides, for Lower Saxony or Germany in general, a decline in summer precipitation is not seen as an insurmountable obstacle for local agriculture, as there is no necessity for irrigation on most sites today and present water reserves would allow an expan-10 sion of irrigated areas at least to some degree. Intensive groundwater management will a basis for this, as increasing winter precipitation could cover the water extrac- tion during the summer months. New breeds and cropping techniques will also aid to counteract the negative effects of climate change, including the expansion of pests or hitherto unknown effects that might arise.15 In conclusion the maize yields in Lower Saxony will not suffer from long lasting de- clines but will have a generally positive outlook over the course of the 21st century. Acknowledgements. We acknowledge support by the Open Access Publication Funds of the Göttingen University. We would like to thank the LBEG for the provision of the climate data-set. This Open Access Publication is funded by the University of Göttingen.20 9125 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | References Avnery, S., Mauzerall, D. L., Liu, J., and Horowitz, L. W.: Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage, Atmos. Environ., 45, 2284–2296, 2011. Bauböck, R.: GIS-aided modeling and analysis of biomass potentials in Lower Saxony – in-25 troduction to the crop model BioSTAR, Ph.D. thesis, Georg-August-University, Göttingen, 2013. 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Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | Müller, U., Engel, N., Heidt, L., Schäfer, W., Kunkel, R., Wendland, F., Röhm, H., and El- bracht, J.: Klimawandel und Bodenwasserhaushalt, landesamt für Bergbau, Energie und Geologie (LBEG), Hannover, Geoberichte 20, 2012. NMELVL: Maisanbau: Mehr Vielfalt durch Alternativen und Blühstreifen, available at: http:25 //www.ml.niedersachsen.de/download/78012, 2012. NMUEK: Empfehlung für eine niedersächsische Klimaanpassungsstrategie, 2012. Pandas: pandas: Python Data Analysis Library, (17.07.2013), 2012. Roeckner, E.: ensembles stream2 echam5c-mpi-om sra1b run1: World Data Center for Climate, 2009.30 Schaller, M. and Weigel, H. 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Forest Meteorol., 164, 96–111, 2012.10 Taube, F. and Herrmann, A.: Relative benefit of maize and grass under conditions of climatic change, in: Optimierung des Futterwertes von Mais und Maisprodukten, vol. 331 of Land- bauforschung/Sonderheft, edited by: Schwarz, F. J., VTI, Braunschweig, 115–126, 2009. USF: Klimawandel und Landwirtschaft in Hessen: Mögliche Auswirkungen des Klimawandels auf landwirtschaftliche Erträge, iNKLIM Baustein 2, Universität Kassel, 2005.715 WMO: Guide to Climatological Practices: 2011 edition, 2011. Wolf, J. and van Diepen, C.: Effects of climate change on silage maize production potential in the European community, Agr. Forest Meteorol., 71, 33–60, 1994. Wolf, J. and van Diepen, C.: Effects of climate change on grain maize yield potential in the european community, Climatic Change, 29, 299–331, 1995.720 9129 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | J. F. Degener and M. Kappas: Maize development in Lower Saxony 3 Europe Germany Lower Saxony Average silage maize fresh matter production in 2010 [in 1000t] < 300 300 – 600 600 – 900 900 – 1200 1200 – 1500 > 1500 Fig. 1. Average Maize production in 2010 as fresh-matter in 1000 metric tonnes by district (LSN, 2014) ing as high ground water levels prevent intensive use (He- unisch et al., 2007).175 The regional differences manifest themselves in the aver- age regional yields. In the period of 2003–2008 the average winter wheat yield south of Hanover was always above 8 t/ha, above 7 t/ha south of Oldenburg and generally below 7 t/ha in the North-East. Maize yields behave rather similar, with dry180 maize silage (33 % dry matter content) having the best yields to the south. The margin between the different parts of LS is however smaller for maize than for wheat and varies gener- ally around 15 t/ha. As can be seen in Fig. 1 the areas with the largest maize production coincide with areas where only185 little wheat is grown and where feed for livestock is in high demand. 2.2 Crop Model The crop model used in this study is a relatively new model called BioSTAR, developed at the Georg-August-University190 in Go¨ttingen (Baubo¨ck, 2013). The model uses a CO2 based crop development engine, thus taking a potential CO2 fertil- ization effect into account. The basic working principle uses temperature to determine the plants development stages and a combination of temper-195 ature, solar radiation and CO2 concentration the maximum photosynthesis rate. Both incrementally build the plants max- imum possible biomass that is then limited by precipitation respectively the soil water content. The model is suitable for large-scale as well as parcel size yield assessments. The phi-200 losophy behind it is an easy to use model with a robust output and a manageable amount of required input parameters. The model was validated on sample sites in Lower Sax- ony with a general disagreement between actual and modeled yield of around 10 %. The required climatic input variables205 are precipitation, temperature, atmospheric CO2 concentra- tion, solar radiation, relative air humidity and wind-speed at 2 m altitude. In addition, information on the soil type is re- quired. As the model was initially conceived as a tool for the estimation of bio-energy potentials, the maize crops only210 contain silage maize (no food maize). Furthermore the three varieties do not consist of single breeds but represent an av- erage of several early, medium or late breeds respectively. A a rather robust approach the model leaves out some as- pects that might well be of great importance for a future yield215 development. Results in this study should thus be read as what would happen if nothing but the climatic input variables would change. These neglected aspects include any techno- logical advances, including any changes in farm manage- ment. Irrigation was not included in the modeling, whether220 for current nor future yields, even if there do exist some areas today that are under irr gation. The sowing date was always the 115th day of the year and was not changed throughout the century. No extra fertilization was included and soil wa- ter content expected to be at 100 % at the beginning of the225 growing season. No effects of a prior crop on a specific site are taken into account. The model either stopped on day 300 of a given year or when full maturity was attained, depending on what happened first. At the time of the actual modeling the BioSTAR model230 could be understood as being in beta stage development. All crops had been validated, however some minor changes in the model engine and more heavy changes in the programs layout have happened since. No version number was in use when the model was applied.235 2.3 Soil Data The soil data used in this study is part of the official digi- tal soil survey map of LS in a resolution of 1:50000, called BU¨K 50 (Boess et al., 2004). This map was intersected with data from the CORINE land-use classification of 2005 for240 Lower Saxony to extract sites that are used for agricultural purposes only. The result between the intersected soil and land-use map was a data-set of 91,014 sites with each used a as unique modeling area. The soil map contained codified information on the soil type and its thickness that were trans-245 lated into the format required by BioSTAR. Fifteen 10 cm soil levels had to be identified, each containing the informa- tion on prevalent soil type with a 16th level representing ev- erything below the initial 1.5 m. The crop model uses these information solely for the calculation of soil water content250 and flows. Figure 1. Average Maize production in 2010 as fresh-matter in 1000 metric tonnes by district (LSN, 2014). 9130 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | J. F. Degener and M. Kappas: Maize development in Lower Saxony 5 Min. – Max. P5 P25 P75 P95 Median [°C] 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 [°C] 0,5 - 1,0 1,0 - 1,5 1,5 - 2,0 2,0 - 2,5 2,5 - 3,0 3,0 - 3,5 3,5 - 4,0 4,0 - 4,5 4,5 - 5,0 2071 ̶ 2100 2041 ̶ 2070 2011 ̶ 2040 Year Fall Summer Spring Winter Year Fall Summer Spring Winter Year Fall Summer Spring Winter -50 -40 -30 -20 -10 0 10 20 30 40 50 [%] Year Fall Summer Spring Winter Year Fall Summer Spring Winter Year Fall Summer Spring Winter 2071 ̶ 2100 2041 ̶ 2070 2011 ̶ 2040 Fig. 2. Indicators of a regional climatic change. Box-Whisker-Plots of temperature (left) and precipitation (right) changes for three different periods in relation to 1971–2000 by season. The data is taken from the WETTREG2010 data-set and represents the mean over Lower Saxony plained the yield development best was then logged. If com- binations gave equally good results, the first run, generally360 the one with less variables, was logged. This was done for the years 2001-2099 as well as 2001-2050 to identify possi- ble changes in variable impact. The statistics in this study have been calculated using MS Excel 2010 and Python (v 2.7) with the addition of SCIPY365 and NUMPY (Jones et al., 2001–), Pandas (Pandas, 2012) and MATPLOTLIB (Hunter, 2007). The calculation of the multi- variate regression models was done using R (v 2.15.2) and rpy2 (v 2.3.0). 3 Climate Change in Lower Saxony370 Fig. 2 gives a brief description of the average change of the climatic variables temperature and precipitation in LS. The climatic comparison is done by 30-year intervals where 1971-2000 is used as present-day climate that might be seen as more current than the climatic normal period of 1961-1990375 (WMO, 2011). These intervals represent a near (2011-2040), middle (2041-2070) and long-term (2071-2100) climatic de- velopment. There are no areas at any time that do show a stagnant or even decreasing temperature development. However, warm-380 ing in spring is always below the annual average while the winter months are always above. Fall temperatures are slightly below annual average and summer months above, though both deviate less from the mean than spring and win- ter seasons do. The mean temperature increase is 0.95 °C for385 near, 2.30 °C for middle and 3.40 °C for long-term scenarios. The development is relatively uniform throughout LS with a slightly stronger (but still less than 0.5 °C difference) devel- opment to the south-east. The precipitation development is different in terms of be-390 ing positive or negative depending on time and space. If only annual means are considered, almost no change in precipita- tion can be detected, although a moderate decline is visible. It however becomes increasingly obvious that the winter and summer seasons are drifting into opposite directions. While395 in the near future all seasonal differences remain in a window of more or less ±10%, these changes drastically amplify to- wards the end. The mean decline in precipitation is around - 25 % in the long-term perspective with some areas at a nearly -50 % decrease. Winter increases are also substantial but at400 around 15 % towards the end of the century they cannot fully counterbalance the summery losses. In summary, all deviations from today’s values will in- crease with passing time, fostering a local development to- wards a more winter rain climate that features increasingly405 hot and dry summers and mild wet winters. 4 Results The results in this study will describe the change in biomass yields during the 21st century. Changes are relative to the mean yields of the decade 2001-2010 as a representation of410 the present time. Figure 2. Indicators of a regional climatic change. Box-Whisker-Plots of temperature (left) and precipit tion (right) changes for three different periods in relation o 1971–2000 by season. The data is taken from the WETTREG2010 data-set and represents the mean over Lower Saxony. 9131 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 6 J. F. Degener and M. Kappas: Maize development in Lower Saxony 4.1 Mean Yield Development The results in this section give the average yield development of all modeled sites. As can be seen in Fig. 3 all three vari- eties visually show a positive yield development throughout415 the century. This is further underpinned by the actual biomass yields after 2060, where the average yield per decade is al- ways higher than for the reference period. Not as evident is the shared pattern of the decadic coefficient of variability. All three varieties have their lowest value in the present (3-4 %)420 with an increase (except for the comparably low variability between 2031-40) towards mid-century (above 8 %) and a slight decline towards 6-7 % at the end of the century. Actual yields will therefore vary more widely around the decadic mean at mid-century.425 Apart from these shared aspects there are also obvious dif- ferences in the overall development. The following descrip- tion will thus cover each variety on its own. The early variety shows an R2 = 0.24 and an average in- crease in yields of 0.12 % p.a. throughout the century. This430 trend could be slightly better explained through a polyno- mial model of second or third order withR2 = 0.27, however no big advantage would be expected from such an approach. The trend/noise (t/n) ratio 1.68 shows no significant trend for α= 0.95 but would for α= 0.9 . Mann-Kendall delivers a435 more unambiguous result with p < 0.001 over the century. It is therefore assumed that a significant trend exists throughout the entire time-period. This trend can basically be split into two parts: the period 2001-2050 has an R2 = 0.003 in a linear regression model440 with an average yield development of ±0% p.a. . The period 2051-2099 has an R2 = 0.11 with an average yield increase of 0.2% p.a. . The lack of a trend in the first half of the cen- tury is confirmed by its t/n ratio of 0.5 and p= 0.39 for MK. For the period 2001-2030 there even seems to appear a slight445 negative development, with a t/n ratio of -0.63 and a p= 0.08 for MK that is however not recognized as being significant. All in all it seems clear that a change in biomass yields is expected to happen, however only after the mid of the century and especially after 2070 when there is only one year with a450 critically lower yield than the present average. In these last thirty years the yields are about 9 % higher than in the first decade, with the last decade being the one with the overall highest yields. If total production of early maize would be calculated over the century, 49.1 % would be produced dur-455 ing the first half. The medium variety has an R2 = 0.15 and an average in- crease in yields of 0.09 % p.a. over the century. As with the early variety a slightly better explanation is provided through a polynomial model with R2 = 0.19. A t/n ratio of 1.34 in-460 dicates no linear trend while a p < 0.001 for MK assumes a significant trend. These numbers represent the lowest indica- tor for a trend throughout the century of all varieties. This is due to a different break within the data that occurs around 2030 and is still present when the year is shifted±10,465 1.0 1.1 1.2 0.9 1.3 y = 0.0012x – 1.42 R² = 0.24 2020 2040 2060 2080 1,0 1,1 1,2 1,3 y = 0.0031x – 5.23 R² = 0.65 y = 0.0009x – 0.8 R² = 0.15 1.0 1.1 1.2 0.9 1.3 1.0 1.1 1.2 0.9 1.3 [2001-10 ≙ 1] Early Medium Late year Fig. 3. Maize yield development in relation to the mean of 2001- 2010 for the three varieties. The black lines and data indicate the lin- ear trend over the century. Dotted lines represent two linear trends during the century with a breaking point at 2050 (early) or 2030 (medium & late variety) though weaker. The period 2001-2030 shows a linear decline in yields of around -0.2 % p.a. and an R2 = 0.16. From 2031 on this turns towards a positive trend of +0.13 % p.a. and an R2 = 0.14. While the early variety did also show signs for a decline in yields, the data for the medium variety supports it470 more strongly. While a t/n ratio of -1.3 fails to be significant, the MK with p= 0.005 is, therefore a significant negative trend until 2030 is assumed. On average this decline will re- duce the yields about 5 %. This trend is then reversed towards the end of the century,475 in such a way that around 2070 the yields are mostly above the present average. However, this happens in a lower magni- tude than for the early variety. If only the period 2001-2050 is considered, t/n (0.01) and MK (p= 0.95) are both highly insignificant, meaning that the average yields are not chang-480 ing. As this is generally comparable to the early variety, the yield variability is somewhat larger for the medium variety. Figure 3. Maize yield development in relation to the mean of 2001–2010 for the three varieties. The black lines and data indicate the linear trend over the century. Dotted lines represent two linear trends during the century wit a breaking point at 2050 (early) or 2030 (medium and late vari ty). 9132 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | 8 J. F. Degener and M. Kappas: Maize development in Lower Saxony Ea rl y M ed iu m La te 2021 to 2040 2021 to 2040 2021 to 2040 2041 to 2060 2061 to 2080 2081 to 2099 2041 to 2060 2061 to 2080 2081 to 2099 2041 to 2060 2061 to 2080 2081 to 2099 Local percentage variation of biomass yields of early, medium and late maize varieties compared to 2001-2010 in Lower Saxony, Germany Fig. 4. Local percentage variation of biomass yields of early, medium and late maize varieties compared to 2001-2010 termining variables (p < 0.001). The late variety still shows some dependency towards spring temperatures (p= 0.09).590 For all three varieties fall temperatures seem to be of higher importance in the first 50 years (0.1> p > 0.05), whereas summer temperatures and spring precipitation have no ap- parent influence. That these multivariate models are not entirely perfect be-595 comes evident when for example winter precipitation and late maize are considered for 2001-99. While not being highly significant a certain connection between both vari- ables is suggested. However, as winter months include De- cember, January and February, when no maize is grown, this600 also seems to be highly improbable. While the statistical model was believed to be reasonably good in determining the relative influence of each variable, there was a need to exclude variables that are not necessarily important. Therefore the next step was to identify the one linear mul-605 tivariate regression model for each site that best describes its yield development, as shown on the right side in Fig. 5. These results were largely in accordance with the results from the models with eleven input variables. Models in the following approach are numbered from R1 to R2047. R is here short610 for Run while the number indicates the variable combination. Higher numbers relate to more input variables. The relevant numbers are explained further in Fig. 5. For 2001-99 the model best describing the yield develop- ment of all varieties was one containing only summer precip-615 itation and CO2 concentrations (R37). 92 % of the early vari- ety, 80 % of the medium variety and 85 % of the late variety sites had this as the optimal model. The remaining sites of the early variety were best described by a model only containing CO2 concentrations (R10). The same is partially true for the620 late variety, as 3 % of the models show their best results when only including CO2 (R10), however models that only used spring temperatures (R6) accounted for the remaining 12 %. This connection to spring temperatures was also be identified in the models featuring all variables. The two runs R6 and625 R10 have a combined share of about 10 % of the medium va- riety’s remaining sites, while the other remaining 10 % are a combination of summer precipitation, summer temperatures and CO2 concentration (R171). For the period 2001-50 the varieties did show a more dif-630 ferentiated picture. The late variety did still have R37 as the dominant model on 90 % of its sites. 5 % were made up of R6 and another 5 % of other not further distinguished mod- els. The early variety had R37 on just 58 % of its sites, 5 % showing R171 and almost the entire rest of 34 % from R2635 with summer precipitation only. The medium variety had only have 24 % comprising of R37, 6 % of R171 and a dom- inating 66 % of R2. Figure 4. Local percentage variation of biomass yields of early, medium and late maize varieties compared to 2001–2010. 9133 BGD 11, 9103–9134, 2014 Maize yield development in Lower Saxony J. F. Degener and M. Kappas Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion D iscussion P aper | D iscussion P aper | D iscussion P aper | D iscussion P aper | J. F. Degener and M. Kappas: Maize development in Lower Saxony 9 CO2 PWin PSpr PSum PFall TWin TSpr TSum TFall 2001-2099 2001-2050 0 2 4 0 2 4 0 2 4 0 2 4 0 100 200 0 100 200 -100 CO2 PWin PSpr PSum PFall TWin TSpr TSum TFall ≙ α=0.05 e=early m=medium l=late variety e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l e m l Results from the linear multivariate regression model ≙ α=0.1 0 20 40 60 80 100 Maize l 2099 Maize m 2099 Maize e 2099 Maize l 2050 Maize m 2050 Maize e 2050 0 20 40 60 80 100 Maize l 2099 Maize m 2099 Maize e 2099 Maize l 2050 Maize m 2050 Maize e 2050 2001-2099 2001-2050 early medium late early medium late 20 40 60 80 0 [%] Percentage share of respective best model R37 R2 R6 R171 R10 R5 Other (PSum + CO2) (PSum) (TSpr) (PSum + TSum + CO2) (CO2) (TFall) Fig. 5. Left side: output of 9 variables from the linear multivariate regression analysis for 2001-2099 (top) and 2001-2050 (bottom) — Right side: Relative share of different linear (multivariate) models to the total number of models Rx represents the number of the 2047 possible runs through variable combination — Px is precipitation by respective season, Tx for temperature 5 Discussion The results from this study basically agree with the findings640 of e.g. Southworth et al. (2000) in that the choice of variety will have a critical effect on how maize yields will develop under a future climate. It even agrees to the point that late varieties will show the most positive development which can be quite substantial with > 25% in Lower Saxony towards645 the end of the century. One reason for this beneficial development of late maize is clearly the fact that today’s temperature sums in LS are not suitable for a full completion of its growing cycle. Tem- perature sums from 20th April to 15th October (minus 6 °C650 temperature basis) vary today around 1500 °C in LS and are therefore perfect for medium varieties but below optimum for late varieties. It seems that around 2030, when temperature sums have increased by about 100 °C, the late variety can fully benefit from these temperatures. That the late variety655 disproportionately benefits from the generally rising temper- atures is further supported by statistical analysis, as the late variety is the only one to show a substantial positive correla- tion to rising spring temperatures. The future climatic condition are however not entirely ben-660 eficial for the growth of late maize varieties. The main limit- ing factor, for all varieties, is the decline in summer precip- itation. However, the time spent within these dry months in relation to the total growing time is shorter for the late va- riety as e.g. the medium one. Thus the late variety can use665 the moister spring or fall conditions for a successful growth. Similarly the early variety profits from the spring conditions while the medium variety would need more water during the summer months of which August will be the driest. An adaption of sowing date could mitigate these negative670 effects to some degree. Some testing on single sites however suggests that the general yield development series of late > early > medium variety is not changed, though the absolute difference might change. The influence of the sowing date on the results of this work are currently under evaluation.675 In a related matter, critical development stages of maize, e.g. during flowering, where water shortage disproportion- ately restrains plant growth (Ehlers, 1996), were not suffi- ciently implemented in the used version of the crop model. This has changed in the current version and is under evalua-680 tion as well. While a shift in sowing dates is expected to be generally beneficial for maize yields, this increase in sensi- tivity is believed to have a rather detrimental effect. While increasing temperatures are generally good for the late variety, temperatures during the growing season should685 not exceed an optimum growing temperatures of 25-30 °C over longer time periods, as this would inhibit photosynthe- sis rates (Endlicher, 2007). However, heat days will increase in LS throughout the century. The effects of this are visible in the multivariate regression models with an increasing neg-690 ative impact of summer temperatures on yields. In reality this effect is expected to be even worse. The main reason is the use of a relatively smooth time-series. This is firstly caused by the statistical nature of the climate model. The usage of mean values from 10 climate model runs tends695 to eliminate extreme values. Secondly, the results of this downscaled climate data-set were 10-day-values that were further combined into monthly averages. Temperature peaks Figure 5. Left side: output of 9 variables from the linear multivariate regression analysis for 2001–2099 (top) and 2001–2050 (bot m) – Right side: relativ share of different linear (multi- variate) models to the total number of models Rx represents the number of the 2047 possible runs through variable combination – Px is precipitation by respective season, Tx for temperature. 9134