ࡱ> q`؉bjbjqPqP ?::?0008F00l}G0B1B1"d1d1d1333lFnFnFnFnFnFnF$HhKF;33;;Fd1d1C7G???;d1d1lF?;lF??@:@d161 ta0<*@HA$MG0}G2@Kz>K:@"K\@356?8ty9q333FFb?X333}G;;;;$!)D) Zdenko Loncaric, Vladimir Vukadinovic, Blazenka Bertic, Vlado Kovacevic University of J. J. Strossmayer in Osijek, Croatia The simulation model of winter wheat organic matter production Introduction The biological systems of organic matter production by arable crops are determined by the agroecological, economical and technological conditions of production. The production level and rentability are defined by the agroecological conditions. That is decisive for planing and prognosis of winter wheat and generally crop production. The yield apprasial by computer simulation model can be a key of land use planing. The aim of this paper was to describe mathematical computer model of winter wheat productivity as practical model for general analysis of concrete production conditions and for yield prognosis at different climatic conditions in eastern Croatia. The elements of mentioned aim are the development of mathematical model of winter wheat productivity, analysis of production conditions by analytical use of model, prognostic use of model and comparison of influences of agrometeorological changes, soil types and fertilization on growth, development and yield of winter wheat. Material and methods Five monitoring localities were chosen: Osijek (OS, eutric cambisol), Krndija (KR, amphigley), Donji Miholjac (DM, hypogley), Naice (NA, pseudogley) and Vukojevci (VU, rigosol) and the agrochemical, pedological and microbiological analyses were done. Mentioned agrochemical data were soil pH reaction (pHH2O and pHKCl), available phosphorus and potassium extracted by acidic ammonium lactate extractant (Egner et al., 1960) and soil organic matter determined as organic carbon by sulfochromic oxidation as prescribed by ISO 14235. Soil respiration intensity was determined by measurement of released CO2 from soil at different temperature and moisture (Lon ari et al., 2003). Used agrometeorological data were as follows: air temperature mean (Table 1), rainfall (Table 2) and cloudiness in Osijek, Djakovo (for site Krndija), Naaice and Donji Miholjac and the soil temperature mean in Osijek and Djakovo during period 1986-1996. Period 1986-1996Mean air-temperature (C)Weather BureausJFMAMJJASOND1.Osijek0.21.36.111.716.719.722.021.316.611.55.21.12.Djakovo0.21.85.811.516.819.522.021.516.811.25.71.13.Naice1.02.26.211.416.019.221.620.916.511.25.61.64.D. Miholjac0.41.76.111.816.720.022.522.117.211.55.21.1Table 1. Mean air-temperatures for the period 1986 1996 Period 1986-1996Precipitation (mm)Weather BureausJFMAMJJASONDSum1.Osijek37,933,947,453,671,577,151,259,266,255,263,148,3664,52.Djakovo51,742,956,253,084,764,744,253,873,852,677,348,9704,03.Naice52,841,364,160,180,682,470,071,972,176,680,158,4810,44.D. Miholjac43,738,750,255,867,972,056,468,864,663,862,752,3696,8Table 2. Precipitation for the period 1986 1996 Results and discussion Soil properties The water retention capacity of analyzed soils was medium (0,35-0,45 cm3 cm-3), topsoil layer density was 1,55 (rigosol) 1,63 kg dm-3 (amphigley and pseudogley). The most frequent topsoil layers were clay loam (Osijek, Krndija, Vukojevci) than loam (Naice) and sandy loam (Rakitovica). The humus content of all analysed topsoil layers were low or medium: 10,2 (Vukojevci) 24,3 mg g-1 (Rakitovica). Total nitrogen content was 0,7 (Vukojevci) 2,4 mg kg-1 (Osijek). The highest phosphorus content was in Naice and Krndija, and the low content was only in Vukojevci. The potassium content was a little lower compared to phosphorus content. The soil respiration analyses show that the temperature increasing from 9 to 21C resulted in increasing of respiration of all analysed soil types and moistures. However, this increasing was higher in the top soil layer and at the higher soil moistures. The temperature increasing from 21 to 30C increased soil respiration only in eutric cambisol whereas in other analysed soil types the respiration was decreased. Soil moisture increasing from 60 to 100 % of field soil capacity increased the soil respiration. Model description The simulation model was created in QuickBasic as an application for PC with DOS, Windows, WMS and other OS. The model belongs to a class of empirical-mechanistic and dynamic models with temporarily increment. The program is composed of hierarchy ordered modules with 3 secondary subroutines and 5 modules: the main module for growth and development simulation, the modules for photosynthesis simulation, for emergence simulation, for the water dynamic simulation and for the nutrient availability simulation. The highest place in hierarchy order occupies the PAR and temperature usefulness, followed by water and nutrients dynamics modules as lower hierarchy levels. The model is temperature driven through development stages and represents analytical and prognostic solution for different possible and actual production situations (meteorological changes, different physical, chemical and biological soils quality, different cultivars and different fertilizations) as an alternative and annex of their complex researches in expensive and long-lasting field trials. Agrometeorological data, soil data, sowing data and biological (cultivars specific) data are needed for the model running. The model results in this simulation are as follows: simulation of emergence on different sowing dates, simulation of increasing and distribution of wheat organic matter, simulation of LAI dynamics, simulation of humus mineralisation and simulation of fertilization influence on yield of different wheat cultivars and in different agrometeorological conditions. The differences between simulated results and real field data are especially possible in water dynamic simulation because of model presumption that the soil is vertically homogeneous. Simulation results The winter wheat simulated yield on examined sites using averaged (1986-1996) agrometeorological data was from 6.98 t/ha in Krndija to 8,27 t/ha in D. Miholjac (Figure 1). Average precipitation data hided possible unfavourable influence of deficit during wheat vegetation in some individual years. The precipitations during wheat vegetations were from 446 mm in Osijek to 535 mm in D. Miholjac (Table 2). Wheat can produce good yield in conditions with 240-330 mm precipitations during vegetation (Jevti, 1977), and physiological experiments showed that assimilation is proportional to transpiration. Since the transpiration is directly depended on water availability, it is obviously that water wasn t limiting factor for yield in simulated vegetations. The highest yield on site D. Miholjac fit with the highest precipitations, but the lowest precipitations were on site Osijek and the lowest yield was on site Krndija. The fact that assimilation and yield doesnt depend just on available water is showed with different yields on sites Naice and Vukojevci although the same meteorological data were used. The simulation results show possible influence of precipitation on wheat yield in examples of 1989 and 1993 years (Figure 2). During wheat vegetation in 1989 on site Osijek was 303 mm, and on site D. Miholjac only 279 mm precipitations. The yield in Osijek was in dry 1989 year 0,5 t/ha lower than in average year (graphs 1 and 2) and on site D. Miholjac the difference is higher (1,5 t/ha). Higher influence of precipitation on site D. Miholjac than on site Osijek can be explained with differences in soil texture since the texture on site Osijek is clay loam and site D. Miholjac sandy loam. The example of humid 1993 year shows connection between soil texture and precipitation and their influence on wheat yield. On lighter soil in D. Miholjac the simulated yield was 7,6 t/ha with 549 mm precipitation, and on heavier soil on site Naice in spite of more precipitation (637 mm), the simulated yield was significantly lower (5,48 t/ha).  Figure 1. Simulated grain yield and straw mass with average agrometeorological data  Figure 2. Wheat grain yield and straw mass in dry 1989 and humid 1993 Input data for simulations whose results are shown on graphs 1 and 2 contains the same agrotechnique (sowing and fertilisation) for all years and sites and different physic-chemical soil properties (depending on site) and agrometeorological data (depending on site and year). The influence of ground water level on yield in simulated vegetations was very high in vegetation 1988 on site Naice because the ground water depth on 1 m at the beginning of vegetation resulted in low yield 2,94 t/ha. During vegetations was 591 mm precipitations (between May 15th and end of the vegetation more than 240 mm) and with shallow ground water, soil was to moist at the end of vegetation (41,77 - 46,44%). The consequences were lower transpiration and assimilation. However, if the ground water level at the beginning of vegetation is 2 m, simulated soil moisture is not to high and simulated yield is 6,72 t/ha. Described example shows very clearly that the model is sensitive to ground water level at the beginning of simulation process, especially on heavy soils, and therefore is very important to use proper level of ground water. The sowing date is very important factor of wheat vegetation with influence on yield (Gotlin, 1989, Jevti, 1977, Kova evi and Grgi, 1993) because of soil moisture dynamic and temperatures which are conditions for wheat growing in autumn. The influence of very law temperature on plants is not builded into the model, but the influence of sowing date reflects on simulated yields. For example, on site Djakovo using average meteorological data (1986-1996) the simulated yield was 6,64 t/ha if sowing date was October 1st, 6,98 t/ha with sowing on October 15th and 6,88 t/ha with sowing on November 1st. Hence, there wasnt important influence on yield. The other example with very significant differences was simulated yields with three sowing dates on site Osijek in 1996. The first sowing resulted in yield 7,20 t/ha, next 7,36 and simulated yield after sowing on November 1st was just 3,10 t/ha. Low yield was result of unfavourable soil temperature and soil moisture which resulted in lower total above ground mass of wheat and, hence, lower assimilation. To make model more sensitive to sowing data, it is necessary to build into the model influence of low temperatures on plant freezing. But, these examples shows complexity of wheat growing factors: at the beginning of spring vegetation when the above ground plant parts should grow very intensive, lower photosynthetic area and lower water use by transpiration had important influence on simulated system variables and yields are lower. Kova evi and Grgi (1993) in analyses of wheat production in Croatia found yield decreasing caused by later sowing date and they indicated law temperatures and soil water surplus as possible reasons. The wheat vegetations with same sowing date, but in different years and on different sites influences very significant on wheat leaf mass during vegetation and therefore on photosyntheses and yields. Wheat leaf area index (LAI) has analogical dynamics compared to photosyntheses intensity: slow increasing at the beginning, no changes during winter, rapid increasing in spring and decreasing after wheat flowering (Figure 3). Simulated vegetation 1997 on site Osijek resulted with maximum LAI value 6,32 on May 10th, and in year 1994 on site Naice on May 1st LAI was only 3,62. At the same time, whit average meteorological data 1986-1996 on site Osijek maximum LAI value was 5,47. These LAI differences reflects on yields and the lowest yield was on site Naice (5,48 t/ha), the highest was in average years in Osijek (7,86). In 1997 on site Osijek yield was slightly lower (7,36) although at the beginning of May LAI was highest. The reason of lower yield comparing to average data for Osijek was lower temperature on third decade in May (12,0C comparing to 17,6C).  Figure 3. LAI dynamics during different wheat vegetations Described simulation model contain part for nutrient (N, P and K) availability simulation with nitrogen as most often limiting factor. Simulation of different fertilisations on site Osijek shows that fertilisation increasing from 100 to 220 kg /ha N increased yield (Figure 4). The yield without fertilisation is very low although simulation results shows that during vegetations 73,1 kg/ha N was mineralised. However, mineralisation was intensive in May and afterwards but until May there wasnt available nitrogen in soil.  Figure 4. Simulated wheat grain yield on two sites and different nitrogen fertilisation Simulated yield on site D. Miholjac without fertilisation was also very low (Figure 4) but slightly higher than on site Osijek. The reason was high nitrogen mineralisation rate (117,2 kg/ha N). That was also the reason of yield decreasing with highest fertilisation comparing to lower fertilisation levels. Simulated mineralisation rate on site Krndija was 83,7 kg/ha N, on site Naice 99,6 and on site Vukojevci only 27,2 kg/ha N. Site Vukojevci had the lowest humus content and therefore the lowest mineralisation potential.  Figure 5. Simulated humus mineralisation dynamics on analysed sites Simulated daily mineralisation rates on investigated sites and mineralisation dynamics are shown on graph 5. Spring mineralisation on site Osijek was 0,30 1,00 kg/ha N, Krndija 0,37- 1,08, D. Miholjac 0,49 1,55, Naice 0,45 1,28 and Vukojevci 0,06 0,38 kg/ha N daily. Greenwood et al. (1987) simulated daily mineralisation rates 0,22 0,88 kg N/ha. Jeuffroy and Recous (1999) estimated in experiments netto humus mineralisation 24 - 25 kg N/ha and 9 11 kg N/ha plant residues mineralisation during period February to wheat flowering. Conclusions Analytical use of model is possible in different production situations with complex analysis of wheat growth factors included. Displaying of simulation results includes tables with 7 simulated soil moisture parameters, 8 parameters of nutrients availability and 11 parameters of growth and development dynamic. That enables vertical and horizontal analyses of winter wheat vegetations. Prognostic use of model enables yield prognoses in expected or prognosed production conditions. Important possibility is the beginning of simulation at every date of wheat vegetation with defining momentary parameters of production situation. These mathematical-computer models play a special role in interpretation data bases that can be used for evaluation of soil suitability and quality, agricultural production zoning, soil evaluation, spatial planning etc. It can be stated that mathematical modelling of production conditions is one of basic factors in food production progress aiming at utilization and preservation of natural soil resources with special attention to environment protection by more efficient fertilizer application. 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Fertilizers in context with resourbcpv.HJPRdfh&Ȉ֮񟓄񟓄uusuuuUhI0h;/wCJOJQJaJhI0h(}CJOJQJaJhR]CJOJQJaJhI0hR]CJOJQJaJ(hI0h(}B*CJOJQJ\aJph$hI0h(}CJOJQJaJmH sH h 2CJOJQJaJhI0h 2CJOJQJaJhI0hivICJOJQJaJ(ce management in agriculture. Volume I. Proceedings. Schnug, E., Nagy, J., Nemeth,T., Kovacs,Z., Dovenyi-Nagy, T.(ed.). Debrecen. International Scientific Centre of Fertilizers (CIEC), 2003, 112-118.     International Conference on Climate Change: Impacts and Responses in Central and Eastern European Countries. 5-8 November 2005, Pecs, Hungary PAGE  PAGE 115 Loncaric et al.: The simulation model of winter wheat organic matter production, 115-121 ȈɈʈ͈̈ψЈ҈ӈՈcdeklmnopvwz{|Չ׉؉ľľᩜ~hYh;{CJOJQJaJhY0JOJQJmHnHuh;{h;{0JOJQJ!jh;{h;{0JOJQJUh;{ h;{0Jjh;{0JU$hYh;{CJOJQJaJmHsHhN#jhN#U$hI0hivICJOJQJaJmH sH mno|Չ։׉؉gd(} &`#$gdN#h]hgd;{=0&P 1hPs:p;{. 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