Voi. 5 (19961:299-310. Modelling growth and nitrogen balance of barley under ambient and future conditions Jouko Kleemola 1 Department ofPlant Production, P.O. Box 27, FIN-00014 University ofHelsinki, Finland Tuomo Karvonen Laboratory ofHydrology and Water Resources Engineering, University of Technology, Espoo, Finland According to current scenarios, atmospheric C0 2 -concentration ([C0 2 ]) and average air temperature will rise in the future. The predicted longer growing season in Finland would imply that more pro- ductive cultivars and even new crop species could be grown. Moreover, higher [C0 2] is also likely to increase dry matter production of crops. This study analyzed the growth of spring barley (Hordeum vulgäre L.) under ambient and suggested future conditions, and its response to N fertilization. Model simulations of soil temperature and of snow accumulation and melting were also studied. The cali- bration and validation results showed that the model performed well in simulating snow dynamics, soil temperature, the growth of barley, and the response of crop growth to N fertilization under present conditions. According to the simulation runs, if a cultivar was adapted to the length of the growing period, the increase in dry matter production was 23% in a low estimate scenario of climate change, and 56% in a high estimate scenario under a high level of nitrogen fertilization. The simulation study showed that the shoot dry weight increased by 43%, on average, under high N fertilization (150-200 kg N/ha), but by less (20%) under a low level of N (25-50 kg N/ha) when the conditions under a central scenario for the year 2050 were compared with the present ones. Key words: C0 2 concentration, climate change, crop modelling, Hordeum vulgäre L. ‘Current address: Kemira Agro, Espoo Research Centre, P.O. Box 44, FIN-02271 Espoo, Finland Introduction According to current scenarios, atmospheric C02 -concentration ([C02 ]) and average air tem- perature will rise in the future. Carter (1992) stat- ed that an increase of I,O°C in air temperature may result in a 10-day extension in the length of the growing season in Finland. The longer grow- ing season will enable spring cereals to be sown earlier. This, in turn, would mean that more pro- ductive cultivars and even new crop species © Agricultural and Food Science in Finland Manuscript received February 1996 299 AGRICULTURAL AND FOOD SCIENCE IN FINLAND https://www.c-info.fi/en/info/?token=HvG9i9uRK3f0CXZX.TBeeeMi-XmzlfiudaNHu8Q.bQsBgkCcs1i_fNqO9c0qVqdKPB0ZhkDG_HW0pmSd6VzFvbN2fbzsOqNZ5enNVVRPduV-FjhhCBjeGRDclf5phaFJPIwQ6HnMMHtwPYTBeCYeHDgnd1xQjrQcPJKCJmFAVjYR64aKYHdtErKQS7XzAJkrWdbQsmcNLR8CoD-PqJim4vxEHvEXGv-kWrWPeDkwEeNUevAl4P9VxJ8STECab_2GE_NvZQlqTJ8OFoPo3r3k19mBN_O-q_lpBh23hiuAL2XA7iFEa2iejLVUkwc3hAE2rOby Kleemola, J. & Karvonen, T: Modelling growth of barley could be grown (Carter 1992). Moreover, in ad- dition to the increase in crop productivity caused by a longer growing period, higher [C0 2 ] is like- ly to increase net photosynthesis, and thus the dry matter production of crops, in the future (Kimball 1983, Cure and Acock 1986). A research group in the Department of Plant Production at the University of Helsinki has been developing a crop model which can be used to estimate the effect of changes in air temperature and [C0 2 ] on the development and growth of spring barley ( Hordeum vulgäre L.). In addition, the model simulates moisture, N and tempera- ture dynamics in soil, and snow accumulation and melting. After validating the model, using data collected under present conditions, it can be used to estimate the impacts of climate change on agricultural production. For example, the previous version of the model used in the present study has been applied over a 10 x 10 km grid across Finland in a study on climate change reported by Carter et al. (1996). A recent study by Kleemola et al. (1995) showed that a previous version of the model was capable of simulating the growth of spring bar- ley cultivated in heavy clay soil under ambient conditions. Their study also included test data collected in a greenhouse where the C02 was enriched above the ambient level. The model performed satisfactorily with these data too.The same field data were used in the present study, in which the model was constructed differently from that used by Kleemola et al. (1995). In this study, the model simulations of soil temperature, the dynamics of snow cover, and the response of barley to N fertilization are compared with data collected under present conditions. More- over, the simulated effect ofincreases in air tem- perature and [C0 2 ] on the growth of barley are also reported. The future conditions are those suggested in scenarios developed for the Finn- ish Research Programme on Climatic Change (SILMU) (Carter et al. 1995, Carter 1996). Sim- ulations were conducted for three policy-orient- ed scenarios of SILMU representing low, cen- tral, and high levels of change in climate and [CO,] by the year 2050. The respective climate changes are: for annual precipitation +1.5%, +6.0% and +9.0%, and for mean annual temper- ature +O.6°C, +2.4°C and +3.6°C relative to the present-day. For details of seasonal changes, see Carter (1996). The corresponding [C0 2 ] scenar- ios are 456 ppm, 523 ppm and 555 ppm, com- pared to a present-day concentration assumed to be 350 ppm. The mode Crop growth The present crop model is based on the potato model described by Karvonen and Kleemola (1995) and the barley model described by Kl- eemola et al. (1995). The crop development stage (D vs ) is driven by the average daily temperature, the base temperature, and by the temperature sum required to complete each phase (Kleemola 1991). D vs is 0.0 at emergence, 0.5 at anthesis, and 1.0 at maturity. The model calculates crop production based on latitude, Julian day, daily global radiation, precipitation, and mean air tem- perature. The original crop model was slightly modified: the method presented by Teittinen et al. (1994) was used to determine the partition- ing of dry matter between shoot organs. At first, their model calculates the increase in shoot dry weight. Then, using the new shoot dry weight and the relative amount of each shoot organ, determined as a function of development stage, a new value for the dry weight of each plant or- gan is calculated. The partitioning of dry matter between roots and shoots, however, is calculat- ed according to van Keulen and Seligman (1987). The computation of the maximum net photosyn- thetic capacity of a leaf (P m, kg C0 2 /ha leaf /h) was also modified, Pm is computed as a function of [C0 2 ] inside the stomatal cavity (C): (1)P - x,*ln(C) -x,m I ' r 2 This relationship was fitted to data adapted from Kemppi (1992), and the following param- eter values were obtained; x,=30.513 and x 2=l 19.91. Cis determined as a function of at- 300 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 299-310. mospheric [COJ using a constant ratio of 0.6 (C,/[CO 2 ]). formulated for each layer (Karvonen and Varis 1992). Soil water Water movement in soil is modelled using a modification of the DRAINMOD -model (Sk- äggs 1978, Skäggs et al. 1991). The model cal- culates the depth of the ground water table and water movement in soil using a method based on soil water balance. The input variables of the system are precipitation and the capillary rise of water in the soil profile, and the output varia- bles are potential and actual evapotranspiration (ET o| and ETac| ), deep seepage and water flow- ing via drainage pipes. The water submodel re- quires a soil water retention curve and the satu- rated hydraulic conductivity of the soil as an in- put, and calculates water movement between soil layers, the depth of the ground water level, drain- age flow, and soil moisture content in each layer as output variables. The thickness of the layer used was 10cm in all the soil models. The esti- mates for ET ot and ET m| are used for computing the effect of water stress on photosynthesis. Soil nitrogen Mineralization and the application of fertilizers increase the amount of mineral N in soil and the uptake by plants, leaching, and denitrification decrease it in the present model. Soil processes involved in the N balance, i.e., mineralization, dissolution of fertilizers, nitrification and deni- trification, are modelled using first order reac- tion kinetics, i.e., the reactions are linearly re- lated to the amount of substrate. This approach is commonly used in modelling soil nitrogen dynamics (de Willigen 1991). The movement of water-soluble N through soil occurs both by molecular diffusion and by mass transport in the soil-water phase. In the current model, diffusion processes are neglected and only convective transport is considered. Convective transport is a function of the soil-water flux between two layers, calculated by the soil water submodel, and the mineral nitrogen concentration of these layers. An ordinary differential equation was Soil temperature Soil temperature is calculated using the model described by Karvonen (1988). The present mod- el, however, unlike the approach used by Kar- vonen (1988), calculates heat transfer separate- ly from mass (water) transfer in soil. The soil temperature model determines the heat transfer in soil on the basis of thermal conductivity and the heat capacity of each soil layer. Moreover, a soil-specific relationship between unfrozen wa- ter content and negative soil temperature is need- ed to take into account the influence of latent heat during freezing/melting periods. The mod- el outputs are soil temperature and the distribu- tion oftotal water content into unfrozen and fro- zen fractions in each layer. Frost depth is de- rived from soil temperature, i.e. the depth where the soil temperature equals O.O°C. Snow dynamics The snow model was detailed by Karvonen (1988). He combined the models presented by Kuusisto (1984), and by Jansson and Halldin (1980). The model includes submodels for de- termining the form of precipitation (snow or water), the melting of snow driven by air tem- perature, depth and density of snow cover, and the amount of free water and ice in snow. Calculation of sowing day The sowing day was given as an input under present conditions, while under future warmer conditions, it can be expected that sowing would be earlier. Using data from Jokioinen (see be- low), the dependence of the sowing day on weather was analysed. It was observed that the sum of precipitation between the date when the five-day moving average of air temperature ris- es above 5°C (D b , Julian day) and the sowing date explained 90% of the variation in sowing dates during a six-year period. Based on this in- formation, we developed a simple model to de- 301 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Kleemola, J. & Karvonen, T: Modelling growth of barley termine the sowing day under future conditions. The model calculates an index, initialized at 0 when the moving average ofair temperature ris- es above 5°C, and reaching 1 at sowing. The in- dex is increased by a parameter related to Db (0.000523*D b ), and decreased by a precipitation parameter (P, mm /d), in case it is raining (0.1517*P). Calibration and validation of the model Experimental data Two sets of data were employed in the calibra- tion and validation of the model. The first set of data, comprising measurements of crop growth response to nitrogen fertilization, was collected at Viikki Experimental Farm, the University of Helsinki (60°10'N, 25°00'E) during the period 1986-87. These experiments were detailed by Kiltilä (1988), and are only briefly outlined here. The meteorological data needed for running the crop model (daily global radiation, precipitation, and mean air temperature) were recorded 10 km from the farm at Kaisaniemi, a meteorological station of the Finnish Meteorological Institute (FMI). The soil type in the experiment was sandy and muddy clay in 1986, and sandy and silty muddy clay in 1987. Both soil types were rich in organic matter (6-12% by weight). The spring barley cultivar, Agneta, was supplied with 10, 50, 90 or 120 kg N, 40 kg P and 62 kg K /ha at sowing. Above ground crop dry weight (kg/ha) was recorded at the yellow ripeness stage (mois- ture content of grains about 35%). For model- ling purposes, the saturated soil conductivity values were assumed to be the same as those used for data from Jokioinen (60°49'N, 23°30'E), adapted from Aura (1990). The water retention curve was adapted from Andersson and Wiklert (1972), according to the soil classification giv- en by Kiltilä (1988). The second set of data was used to analyse the performance of the model in simulating year to year variations in crop growth, snow dynam- ics and soil temperature. Furthermore, these data were also used as a baseline when conducting a sensitivity analysis for the model. The data were collected at the Agricultural Research Centre in Jokioinen, as a part of a joint Nordic project during the period 1982-1987 (Ilola et al. 1988). The meteorological data, as well as soil temper- ature and snow depth, were recorded at Jokioi- nen, a FMI site close to the experimental field. Soil temperature was measured once a week at the depths of 20, 50 and 100 cm. Snow depth was recorded daily, but five-day intervals were used in this study. The soil type in the experi- mental field was classified as heavy clay, with 63% of the topsoil particles < 0.002 mm. In the model, the soil was treated as two layers: 1) 0- 30 cm as the top soil and 2) below 30 cm depth as the bottom soil. A spring barley cultivar. Porno, was grown throughout the experiment. The crop was fertilized with 80-100 kg N, 35- 40 kg P, and 40-65 kg K /ha each year. Above- ground crop dry matter was monitored weekly, and these measurements were used in validating the crop model. The water retention curve was adapted from Andersson and Wiklert (1972), according to the physical characteristics of the soil reported by Ilola et al. (1988). Model simulations were carried out on a year- ly basis for the data ofViikki, as the experiments were conducted in different fields. For Jokioin- en, the model was run continuously from 1 Jan- uary 1982 until 31 December 1987, and the ini- tial values for the soil state variables were given only once, at the beginning of the simulation period. The crop variables were initialized at sowing each year. Crop response to nitrogen fertilization The data collected at Viikki Experimental Farm in 1987 were used for calibrating the response of the crop to nitrogen fertilization, and the data from 1986 were used for validating the nitrogen response. The base temperatures used in deter- mining the development rate of the crops were adapted from Saarikko et al. (1993). The tern- 302 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 299-310. perature sum requirements of the crops for the periods between emergence and heading, and between heading and yellow ripeness were ad- justed using the data from the year 1987. The parameter determining the lower limit of the range in which the leaf N content (g N/ leaf m 2) does not limit the photosynthesis was also cali- brated using 1987 data. The values for the other parameters of the model were taken from Kl- eemola et al. (1995), and Kleemola et al. (1996), who also gave a description for those parame- ters. Figure la indicates that the calibration was successful, despite a small overestimation of the crop dry weight at the lowest N treatment. The weather conditions in the year 1986 were quite different from those of the year 1987: the aver- age temperature was 14.8°C in May-July (12.5°C in 1987) and the totalprecipitation was 147 mm (170 mm in 1987). However, the model per- formed satisfactorily in 1986, too (Fig. lb), though in contrast to 1987, the crop dry weight was slightly underestimated at the lowest nitro- gen level. Figure 1 also indicates that both the observed and simulated optimal nitrogen fertili- zation level, i.e. the nitrogen fertilization level at which additional nitrogen does not increase growth, was lower in 1986 than in 1987, pre- sumably because of water deficiency. Crop growth The same set of parameters as in the nitrogen data above were used when running the model for the second set of data collected at Jokioinen, 1982-87. However, as the cultivar was differ- ent, the parameters determining the temperature sum requirements of the crop were changed. The values were taken from Saarikko et al. (1993). The model performed well compared to obser- vations in simulating the shoot dry weight (DW) at Jokioinen. The coefficient of determination (r2 ) for all the samples taken during the growing periods in 1982-87 was 0.90. The coefficient of variation was 8.5 and 8.6% for the measuredand the simulated shoot DW at maturity, respective- ly. As an example.Fig. 2a shows the time-course of the measured and the simulated shoot dry weight for the year 1983, when the weather con- ditions were close to the optimum for crop growth. Figure 2b presents data for 1984, when the soil moisture was in excess and was report- ed as restricting crop growth (Ilola et al. 1988). However, the simulated response of crop growth to this excess soil moisture was greater than the one observed. Figure 2b shows that the simula- tion was erroneous during a two-week period after the day number 175; during the rest of the growing period the simulated growth rate was close to that observed. Soil temperature We compared the simulation results of the soil temperature model with the data of the years 1982-84,collected at Jokioinen. As the temper- ature was measured weekly the data comprised 156 observations at each depth. The model ac- Fig. 1.The response of simulated and measured crop growth to N fertilization in (a) 1987 and (b) 1986.The model was calibrated using data from 1987. 303 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Kleemola, J. & Karvonen, T: Modelling growth ofbarley counted for 95, 96 and 97% of the variation be- tween the measured and the simulated soil tem- peratures, pooled for all three years, at the depths of20, 50 and 100cm, respectively. At each depth, the variation in the simulated values was small- er than in the measured ones. The time-course of the soil temperature at the depth of 20 cm is shown in Fig. 3 for the years 1982 (a) and 1983 (b). The model performed satisfactorily through- out each year, except in the spring, when the sim- ulated soil temperature rose more rapidly than the measured one in both years. Moreover, the model overestimated the soil temperature in win- ter 1983. This may have been caused by the fact that the soil water model tends to overestimate the soil moisture content of the top soil layers. The simulated soil probably had more water to freeze than the actual soil, and for the same avail- able energy proportionally less water froze than actually happened so that the simulated soil tem- perature remained higher than the measured one. Another reason for the overestimation may have been that the depth of the snow cover was small in 1983 (see later) and the model may have ex- aggerated the heat flux through the thin snow cover into the soil. Snow depth The appearance and disappearance of snow cover was modelled satisfactorily. The simulation of snow depth was not as good (Fig. 4). The model was able to explain 81% of the variation in the measured snow depth during the winters 1982- 84 (pooled for all three years). The simulated values were generally slightly higher than those measured. An accurate estimation of the depth (and density) of snow is essential for simulating the soil temperature successfully in the winter and the spring. There exists a clear relation be- tween the snow depth and the soil temperature during the winter (Figs 3,4): the thicker the snow cover the higher the soil temperature. Results Sensitivity analysis The sensitivity of the model outputs to changes in variables suggested to be affected by climate change, were analysed. The sensitivity analysis was conducted in order to aid in making conclu- sions about the results given by the model for the weather conditions in 2050. The baseline weather used was the 1982-87 data from Jokio- inen, and the nitrogen fertilization rate was as- sumed to be 200 kg/ha. Model runs were con- ducted for the low, central and high scenarios of SILMU. One variable (air temperature, precipi- tation and [C0 2 ]) at a time was adjusted accord- ing to each scenario. The results of the sensitiv- ity analysis are presented in Table 1. Table 1 shows that the effect of C02 on crop growth was bigger than that of the other variables according to the model. The year to year variation was slightly decreased when compared to ambient Fig. 2. The time-course of simulated and measured shoot dry weight at Jokioinen in (a) 1983 and (b) 1984. 304 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 299-310. Fig. 3. The time-course of simulated and measured soil temperature at the depth of20cm in (a) 1982 and (b) 1983. 305 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Kleemola, J. & Karvonen, T: Modelling growth ofbarley conditions. The increase in precipitation had lit- tle affect on crop growth. Neither was the varia- tion between years affected. However, the in- crease in precipitation clearly increased the amount of drainage water in the autumn and the spring (results not shown). Higher air tempera- ture decreased shoot dry weight and increased the variation between years. The decrease in shoot dry weight resulted, in part, from acceler- Table I. Sensitivity analysis of the model: the response of simulated shoot dry weight (relative to the present) of present-day barley cultivar to the changes in air tempera- ture, precipitation and [C02 ]. One weather variable was increased in timeaccording to SILMU low, central and high scenario. Weather data collected at Jokioinen during the period 1982-87 was used a baseline. Coefficient of varia- tion (CV, %) between years shown in parenthes; CV was 9.20% for the baseline period. Scenario Weather variable Low Central High Precipitation 100(9.17) 101(9.04) 101 (9.11) Temperature 98(9.90) 92.0(12.6) 87.5 (14.1) (COJ 120(7.93) 129(7.36) 133 (7.16) ated development under higher temperatures and a consequent shortening in the length of the growing period. Higher temperatures also in- creased the potential evapotranspiration, lead- ing to a decrease in shoot dry weight in dry years. Crop growth responses under scenarios of cli- mate change A few test runs were made to determine the ef- fect of the suggested changes in [COJ, air tem- perature and precipitation on crop growth. The baseline weather used was the 1982-87 data from Jokioinen. Under the central scenario ofSILMU for 2050, the simulation runs suggested that the average shoot DW of barley would increase by 23% if a present-day barley cultivar, such as Porno was used and the N fertilization level was 100kg /ha (Table 2). The interannual variability of the shoot DW increased slightly, in accord- ance with an increase in the variability of the length of the growing period. However, if the cultivar was changed to one requiring a higher temperature sum for maturing, the increase in shoot DW was 35 % and the coefficient of vari- ation decreased. Fig. 4, The time-course of simulated and measured snow depth during the winters of 1982 and 1982-83. 306 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Vol. 5 (1996): 299-310. The model was also used to estimate the crop response under the low and high climate change scenarios of SILMU (Table 2). The “new” crop cultivar was assumed to be adapted to the changed conditions, so that the sowing day was assumed to change according to the climate, and the development rate was adjusted so that the harvest dates were close to those observed un- der ambient conditions. Again, the model sug- gested clear increases in crop growth: by 23% under the low scenario and by 56% under the high scenario ofSILMU assuming a high N fer- tilization rate, 200 kg /ha (Table 1). Some simulation runs were also conducted Fig. 5. The simulated response of crop growth to N fertilization under observed conditions, and under conditions adjusted according to the central scenario of SILMU with increased CO, -concentration and air temperature by 2050, for (a) a favourable year (1983) and (b) a dry year (1986). Arrows indicate the N fertilization level required for a maximum yield. 307 AGRICULTURAL AND FOOD SCIENCE IN FINLAND Table 2,Average simulated increase (%) in shoot dry weight of a present-day and a new barley cultivar when shoot dry weight obtained under conditions in 2050 was compared to one obtained under present conditions. Weather data col- lected at Jokioinen during the period 1982-87 were used as the baseline. central low high scenario scenario scenario N fertilization present cv. new cv. new cv. new cv. 100kgN/ha 23 35 200kgN/ha 25 43 23 56 in order to analyse the effect of climate change on the optimal rate of N fertilization. Figure 5 shows the simulated crop response when weath- er data from two different years were used as baseline conditions. The growing conditions were very favourable in 1983, whereas in 1986 they were much drier. According to the simula- tions, enhanced crop growth requires additional nitrogen. Under present-day conditions, when the growing conditions are good shoot DW contin- ues to increase with increasing nitrogen fertili- zation up to a level of 125 kg N/ ha (Fig. sa). However, if soil moisture deficiency restricts growth, the optimum N fertilization rate is found at much lower levels (Fig. sb). The effect of the scenario changes in climate is to increase the level at which this “saturation effect” of nitro- gen occurs, under both favourable and drought conditions. The effect is reinforced in simula- tions with an adapted cultivar (Figure 5). Discussion The model performed well at simulating vari- ous components of the soil-plant atmosphere system for which measurements were available for comparison. This indicates that the simula- tion results given in the present paper are credi- ble for the kind of weather and soil types used in this research. However, more validation stud- ies are required since this is the first time the model has been applied in its present configura- tion. For example, experiments on only two soil types were considered, and measurements of crop responses under enriched [C0 2 ] were not used. Moreover, data on the soil N content and soil moisture were not available. According to the simulation results climate change will clearly enhance crop growth under northern conditions. The increase in the dry matter production, as a result of a longer grow- ing period and higher [C0 2 ], is not likely to be counteracted either by the increased develop- ment rate of crops, and the shortened grain fill- ing period, or by the increased potential eva- potranspiration inducing water deficiency con- ditions for crops. It was observed that the in- crease in yields is also dependent on the crop- ping methods employed. As shown in Table 1, the increase in temperature decreases shoot dry weight by decreasing the length of the growing period. This effect is more than counter balanced by the positive effect of the increase in [C0 2 ] (Table 2). A further increase in yields will be obtained, for example, if new cultivars, with a longer growing period are used. Moreover, the nutrient requirements of crops will also increase if the growth is enhanced. The results of this study refer to spring-sown crops. Some of the aspects and trends may hold for winter crops, too. The higher [C0 2 ] will in- crease the photosynthesis ofwinter crops in Fin- land. However, the crop system for autumn-sown crops is more complex than for the spring-sown crops. It is likely that the frequency of warm periods in the winter time, during which the snow disappears, will increase under a changed cli- mate, as indicated by our model (results not shown). This, in turn, may affect the suscepti- bility of crops to winter damage. In addition, the refreezing of the melted water can also cause damage. On the other hand, the total duration of snow cover will probably decrease, which will be beneficial for overwintering crops. The short- er the winter period, the smaller the storage of assimilates crops have to accumulate in the au- tumn to survive. Biochemical reactions require 308 Kleemola, J. & Karvonen, T: Modelling growth ofbarley AGRICULTURAL AND FOOD SCIENCE IN FINLAND some energy during the winter period too, and as assimilation is not possible in the winter due to low temperatures and probable snow cover, the plants have to accumulate some assimilates prior to overwintering. It is difficult to design field experiments for studying these problems, as the heating of field plots would be very diffi- cult in the winter without disturbing other prevailing conditions. A partial solution would be to analyse the exisiting data on overwinter- ing during warm winters under ambient condi- tions. Acknowledgements. This study was financed by the Acad- emyofFinland as a part of the Finnish Research Programme on Climatic Change (SILMU). References Andersson, S. & Wiklert, P. 1972. Markfysikaliska un- dersökningar i odlad jord. XXIII. Om de vattenhållande egenskaperna hos svenska jordarter. (Water-holding properties of Swedish soils). Grundförbättring 25: 53-143. Aura, E. 1990. Salaojien toimivuus savimaassa (The function of drainagepipes in clay soils). Maataloudentut- kimukeskus, Tiedote 10/90. 93 p. Carter, T. R. 1992. The greenhouse effect and Finnish agriculture. Maatilahallinnon aikakauskirja 1/1992: 31-57. - 1996. Developing scenarios of atmosphere, weather and climate for northern regions. Agricultural and Food Science in Finland 5: 235-249 (this issue). -, Posch, M. & Tuomenvirta, T. 1995. SILMUSCEN and CLIGEN, User's Guide. Publications of the Academy of Finland 5/95. Painatuskeskus Oy, Helsinki, Finland. 62 p. -, Saarikko, R. A. & Niemi, K. J. 1996. Assessing the risks and uncertainties of regional crop potential under a changing climate in Finland. Agricultural and Food Science in Finland 5: 329-350 (this issue). Cure, J.D. & Acock, B. 1986. Crop responses to carbon dioxide doubling: a literature survey. Agricultural and Forest Meteorology 38: 127-145. Ilola, A., Elomaa, E. & Pulli, S. 1988. Testing of a Dan- ish growth model for barley, turnip rape, and timothy in Finnish conditions. Journal of Agricultural Science in Fin- land 60: 631-660. Jansson, P.-E. & Halldin, S. 1980. Soil water and heat model. Technical description. Technical Report 26, Swed- ish Coniferous Forest Project, Uppsala, Sweden. 81 p. Karvonen, T. 1988. A model for predicting the effect of drainage on soil moisture, soil temperature and crop yield. Laboratory of Hydrology and Water Resources Engineer- ing, Helsinki University of Technology. 215 p. Ph.D. the- sis. -& Kleemola, J. 1995. Prediction of water and nitrogen limited crop production. In: Kabat, B. et al. (eds.). Mod- elling and parametrization of the soil-plant-atmosphere system: A comparison of potato growth models. Wage- ningen Pers, Wageningen,The Netherlands, p. 335-369. - & Varis, E. 1992. Components of soil nitrogen balance. In: Karvonen, T. & Varis, E. (eds.). Mathematical models in crop production. Department of Plant Production, Uni- versity of Helsinki, Publication nr: 32. p. 161-176. Kemppi, V. 1992. Ilmakehän pitkäaikaisen korkean CO2 -pitoisuuden vaikutus vehnän ja ohran yhteyttämiseen, kasvunopeuteensekä kloroplastien hienorakenteeseen. (The effect of long-term C02 enrichment on the rate of photosynthesis and growth, and fine-structure of chloro- phyll of wheat (Triticum aestivum L.) and barley (Hordeum vulgare)). Department of Plant Production, University of Helsinki. 77 p. M. Sc. thesis. Keulen, H. van & Seligman, N.G. 1987. Simulation of water use, nitrogen nutrition and growth of a spring wheat crop. Pudoc, Wageningen,The Netherlands. 31 Op. Kiltilä, K. 1988. Ohran typpilannoituksenvaikutus alus- kasvien kasvuun. (The effect of nitrogen fertilization of barley on growth of under sown crops). Department of Plant Production, University of Helsinki. 105 p. M.Sc. the- sis. Kimball, B.A. 1983.Carbon dioxide and agricultural yield: An assemblage and analysis of 430 prior observations. Agronomy Journal 75: 779-788. Kleemola, J. 1991. Effect of temperature on phasic de- velopement of spring wheat in northern conditions. Acta Agriculturae Scandinavica 41: 275-283. -, Rehu, E., Peltonen-Sainio, P. & Karvonen,!. 1995. Modelling the impact of climatic change on growth of spring barley in Finland. Journal of Biogeography 22: 581-590. -.Teittinen, M. & Karvonen,!. 1996. Modelling crop growth and biomass partitioning to shoots and roots in relation to nitrogen and water availability, using a max- imisation principle. I. Model description and validation. Plant and Soil (in press). Kuusisto, E. 1984. Snow accumulation and snowmelt in Finland. Publications of the Water Research Institute 55, Helsinki, Finland. 149 p. Saarikko, R.A., Carter, T.R. & Kleemola, J. 1993. De- termining temperature and photoperiod effects on the phenological development of spring cereals under north- ern conditions. In: Wilson, D. et al. (eds). Crop Adapta- tion to Cool, Wet Climates, COST Workshop, Aberyst- wyth, UK. 23-24 March 1993. p. 165-174. 309 Vol. 5 (1996): 299-310. AGRICULTURAL AND FOOD SCIENCE IN FINLAND Skaggs, R.W. 1978. A water management model for shal- low water table soils. North Carolina Water Resources Institute, Report No. 134. North Carolina State Universi- ty, Raleigh, NC, USA. 178 p. Karvonen, T & Kandil, H. 1991. Predicting Soil water fluxes in drained lands. An ASAE (American Society of Agricultural Engineers) meeting presentation, Paper No. 912090. 23 p. Teittinen, M., Karvonen,!. & Peltonen J. 1994. A dy- namic model for water and nitrogen limited growth in spring wheat to predict yield and quality. Journalof Agron- omy and Crop Science 172: 90-103. Willigen, P. de 1991. Nitrogen turnover in the soil-crop system; comparison of fourteen simulation models. Fer- tilizer Research 27: 141-149. SELOSTUS Ohran kasvun ja typpidynamiikan mallintaminen nykyisissä ja tulevaisuuden olosuhteissa Jouko Kleemola ja Tuomo Karvonen Helsingin yliopisto jaTeknillinen korkeakoulu Tutkimuksessa analysoitiin ohran kasvua ja typpilan- noituksen vaikutusta siihen kokeellisten aineistojen ja matemaattisen mallin avulla. Mallin toimivuutta testattiin nykyisissä oloissa kerätyillä aineistoilla, ja mallia käytettiin ennustamaan ilmastonmuutoksen vaikutusta ohran kasvuun. Samalla analysoitiin mal- lin kykyä simuloida maan lämpötilaa sekä lumen ker- tymistä ja sulamista. Kalibrointi- ja testaustulokset osoittivat, että käytetty malli simuloi hyvin ohran kasvua ja typpilannoituksen vaikutusta siihen sekä maan lämpötilaa ja lumen kertymistä ja sulamista ny- kyisissä oloissa. Ohran fytomassa nykytasoon verrat- tuna oli hitaan ilmastonmuutoksen oloissa 23 % suu- rempi ja nopean ilmastonmuutoksen oloissa 56 % suurempi, jos viljelijän oletettiin vaihtavan lajiketta kasvukauden pituuden mukaan ja käyttävän korkeaa typpilannoitustasoa. Keskinopean muutosennusteen mukaan ohran fytomassa lisääntyi korkeaa typpilan- noitustasoa (200 kg N /ha) käytettäessä suhteellises- ti enemmän kuin alhaista typpilannoitustasoa (100 kg N/ha) käytettäessä. Mallin antamien tulosten perus- teella ohran kasvun lisääntymiseen vaikuttaa eniten kohoava hiilidioksidipitoisuus. Lämpötilan kohoami- nen sen sijaan alentaa jonkin verran tuotantopotenti- aalia, jos viljelyyn ei oteta uusia lajikkeita, jotka vaa- tivat suuremman lämpösumman tuleentumiseensa. 310 Kleemola, J. & Karvonen, T: Modelling growth ofbarley AGRICULTURAL AND FOOD SCIENCE IN FINLAND