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Home » Projects » REB 2000 » Conferences REB 2000 The Effects of Climate Changes on Land Use in Romania Catalin SIMOTA and Mihai DUMITRU, Research Institute for Soil Science and Agrochemistry Bucharest Henk KIEFT, ETC Ecoculture, Leusden, Netherlands Mark Rounsevell, Department of Geography Universite Catholique de Louvain, Belgium Introduction The farming reality in Romania can be characterised as a large majority of low external input farming techniques. Investors, insurance companies and agricultural policy-makers are challenged to design development models based on current farmers reality. The effects of inefficient farming techniques on the environment must be in included in policy design. This type of questions asks for re-thinking of the agricultural production systems from two perspectives:
The study has assessed the effects of climate changes on the optimum input levels of fertiliser, fuel and labour from an economic perspective, and expressed this in yields and farm income for some strategic crops (winter wheat, maize, soybeans, sunflower) and basic production systems from Survival Agriculture to High Input Agriculture. The assessment is based on experiences with the IMPEL-project (Integrated Model to Predict European Land Use) and GIS-techniques. With the exception of maize, all the strategic crops showed significant increases in yield and, consequently annual profits, for the climate change scenario compared with the baseline climate. The effects of soil variability and economic risk, increase with the intensity of the agriculture system. Thus, the dependency of annual profit on the intensity of the farming system supports the introduction of Low Input Sustainable Agriculture as an appropriate way to maximise profit and minimise negative environmental effects. In line with recent changes in consumer behaviour, the climate change scenario suggests increasing areas of soybean and sunflower and decreasing areas of winter wheat and maize. METHOD Simulation Model An agro -climatic simulation model (ROIMPEL, see Box 1), developed under the umbrella of the IMPEL project (Rounsevell et al., 1998, see Box 2), was used to simulate the crop yields and the water and nitrogen balances in a specific area and for various systems of agriculture, based on long-time-series of recorded climate data. The output of the ROIMPEL model serves in a next step for assessing economic parameters, using the energy equivalent of the main activities (soil tillage, mineral fertilisation, and weed control by pesticides) in each of the specified agricultural systems. Therefore the real fuel costs and the fuel/energy conversion efficiency are the driving parameters for the economic analysis of the system. ROIMPEL
IMPEL project IMPEL ( I ntegrated M odel to P redict E uropean L and Use) is a project developing a model integrating the physical as well as socio-economic aspects of land use systems based on a modular approach. The modules comprise:
Location, soil and climate Model simulations were applied to an area (972 656 ha) of southern Romania (43° 40' 00" - 44° 40' 00" latitude, 22° 00' 00" - 24° 00' 00" longitude) overlapping the Dolj county. The soil in the area has been characterized by point soil profiles (620 soil profiles with standard soil physical properties for each horizon). The land use was derived from CORINE Land Cover project. The following soil classes were surveyed in the area: Argiluvisols (33.13%), Mollisols (29.39%), Cambisols (14.96%), Undeveloped and Arent Soils (14.49%), Vertisols (3.62%), Hydromorphic Soils (3.44%). The texture classes for these soils are: Loam/Clay (25%), Fine Sandy Loam/Clay (17.46%), Clay Loam (14.90%), Loam (11.93%), Fine Sandy Loam (7.38%), Fine Sandy Loam/Clay Loam (7.36%), Sandy/Loamy Sandy (6.53%), Fine Sandy Loam/Loam (6.52%). Easy-to-map soil parameters for the ROIMPEL simulation model (Maximum Available Water Content, Water Content for the Humidity Limit of Soil Workability, and Maximum Root Front Depth) were estimated with the data set on soil physical parameters and with indirect models based on pedotransfer functions. Scaling-up of the easy-to-map parameters was based on soil type and soil textural classes. The Maximum Available Soil Water Content to the root front depth was the key soil parameter used for spatial extrapolation of the site specific simulation output. This soil parameter was computed using a standard methodology from soil water parameters (soil water retention curve, field capacity, wilting point), soil mechanical parameters (soil resistance to penetration, defining root elongation rate) and a climate index characterising the cumulated atmospheric water deficit during the vegetation period. Climate data were collected from four meteorological stations (Craiova, Calafat, Caracal and Bechet) covering the case study area. Daily instrumental records for minimum and maximum temperature, rainfall and sunshine duration were available for the 30 year period, 1961-1990. Due to the relatively small differences between the instrumental meteorological data from the four weather stations, an inverse square root interpolation method was used to derive site specific climate values based on the values of the four meteorological stations. Farm management The agricultural production system in Romania is going through a profound and dramatic change mainly because of the reallocation of property and to the serious lack of agricultural credit at the farm level. About 80% of the agriculture area of Romania is under private ownership with an average size of about 2 ha. The farms are split into 4 to 12 parcels. The management system is characterised by very low inputs of fertilisers (less than 40 kg/ha) and pesticides, minimum mechanisation, and low quality seed. About 45 % of the population in Romania live in villages and 35% depend entirely on their agricultural outputs. Simulation models were run (using the site-specific soil and climate data) for different management strategies that correspond to the main direction of policy-making in the European Union. It was assumed that each management strategy was applied to the entire study area. This assists in the comparison of the model outputs, and their use by policy makers in assessing the relevance of the effect of climate change on the two production systems with their different levels of inputs and styles of management. The management strategies are related to the external input of nitrogen fertiliser and cover the range from Low Input (Sustainable) Agriculture (recommended for widespread use in the future by Agenda 2000) to High Input Agriculture (the actual intensive agricultural technology). Some characteristics of the two extreme systems of agriculture are:
The agriculture management parameters included in the models were the crop rotations, the nutrient supply, the vegetal residues management practices and the tillage operations. No effects of diseases or extreme weather factors (e.g. storms, early freezing, etc.) other than drought/wet periods were considered. As fertiliser management practices, only nitrogen was considered in detail. The phosphorus fertilisation was considered at levels of 10-20 kg ha -1 , which causes no major phosphorus stress problems during the vegetation period. Unfortunately, the phosphorus content of the soils in Romania is low and very low on about 40 % of the area (6.2 million ha from a total of 14.797 million ha). The agricultural statistics for the last 30 years show that maize was cultivated on an average of 42 % of the selected area, winter wheat on an average of 29%, sunflower on 15%, and soybean on 1%. Together they represent over 85% of the area under study. Economy For the model simulations the crop yield unit price was taken to be that for the international market. Thus, the price scenario for 2020 was based on the "Market" scenario. The "2020 - Market" scenario forecasts an almost complete freeing of the international market and changes to environmental policy. The freeing of the internal market would more or less completely resolve the problem with World Trade Organisation and would allow easy integration of the CEECs agricultural systems. In this case, the profitability of farming activities will be reduced and land use would be totally ruled by the world markets and environmental politics. The energy requirements used in the modelling exercise were: nitrogen fertiliser 75.3 MJ kg -1 , phosphorus fertiliser, 13.8 MJ kg -1 , Pesticides, 209 MJ kg -1 . The energy conversion rate of fuel was assumed to be 46 MJ l -1 . The fuel cost was taken as 0.3 US$ l -1 , whereas the electric energy cost was 0.25 US$ KWh -1 . The crop yield price was estimated to be 120 US$ t -1 for winter wheat, 160 US$ t -1 for maize, 320 US$ t -1 for soybean and 290 US$ t -1 for sunflower. The labour costs were considered to be 2 ECU h -1 . Climate change scenarios Data provided by 4 GCM models (HADCM2, ECHM3TR, CCCEQ, GISSEQ) for the selected area were used in the model simulations. GCM's were used with 6 emission scenarios (ISP2a, b, c, d, e, f) taken from MAGICC model, and for 3 climate sensitivities (high, mid, low). Climate sensitivity is defined as the magnitude of the equilibrium surface global warming following a doubling of CO 2 or CO 2 equivalent concentration in the atmosphere. GCM type, climate sensitivity and emission scenarios have a statistically significant effect on changes in air temperature and rainfall in the study area. The standard combination used for the case study was HADCM2 + mid climate sensitivity + ISP2b emission scenario. model results Sensitivity to climate change scenarios 30 years of daily data for air temperature and precipitation were derived for each combination of GCM and climate sensitivity, considering a standard emission scenario (ISP2b) using the weather data generator. The hypothesis when using the weather data generator was that the actual monthly averages of air temperature and cumulated precipitation were changed with values provided by GCM's, the coefficient of variation for these two weather parameters was the same for the actual and perturbed scenario. ANOVA-Single Factor techniques were used to compare the area scaled-up output of the ROIMPEL simulation model for various combinations of GCM's and climate sensitivity for the strategic crops (winter wheat, maize, soybeans, sunflower). For each crop and perturbed climate scenario the sample of 30 years output yield data were compared separately, with the analogous data sets derived for each of the other climate change scenarios. The analysis showed that:
The HADCM2 GCM with mid climate sensitivity and the IS92b emission scenario was taken as the standard climate change scenario for further in-depth investigations of the effects of climate change on crop yields in combination with the economic scenarios for the study area. Sensitivity to soil type Therefore, for winter wheat the sensitivity of yield changes to soil textural class, for the same soil type, is moderately high. Expected changes are higher for chernozems (light soils with high root front depth) and smaller for Brown Reddish Soils and Argillic Chernozems (hard soils with small root front depth). For maize the sensitivity of yield changes to soil textural classes, for the same soil type, is moderately high. The sensitivity of yield output is dependent on the agriculture system (LIA, HIA). Sensitivity to management intensity Yields and annual profit for the baseline climate and the HADCM2 standard scenario were estimated for the main strategic crops for various management practices. The intensity of management practice was defined by the amount of nitrogen fertiliser applied. All the other fertiliser requirements were balanced with the amount of nitrogen fertiliser. The site-specific output was scaled-up to the area level using the statistics derived from a soil resource-GIS considering two layers of information: soil type and soil texture. With the exception of maize, all the strategic crops showed significant increases in yield and, consequently annual profits, for the climate change scenario compared with the baseline climate. The effects of soil variability and economic risk, increase with the intensity of the agriculture system. Thus, the dependency of annual profit on the intensity of the farming system (expressed in nitrogen fertiliser amounts) supports the introduction of Low Input Sustainable Agriculture as an appropriate way to maximise profit and minimise negative environmental effects. In line with recent changes in consumer behaviour, the climate change scenario suggests increasing areas of soybean and sunflower and decreasing areas of winter wheat and maize. Normally, dry and wet years give different relationship between yeld and annual profit and the nitrogen fertiliser amount. The effects of soil variability are more important in dry years and least important in wet years. For almost all cases the range of the annual profit variation due to soil variability increases with the intensity of agricultural system. Conclusions Considering the socio-economic conditions of the selected region (cheap labour prices, small area farms, high percentage of population involved in agriculture, fuel prices at international market levels, no subsidies for agriculture), Low Input Sustainable Agriculture is the optimum agricultural system. This system produces acceptable yields and annual incomes. High Input Agriculture gives higher yields, but with low economic benefits. The amount of nitrates leached below the root front depth to the groundwater -both under HIA and LISA- may still be below the internationally accepted threshold values for the pollution with nitrates, but care is needed to substantially reduce the nutrient loads. Therefore also from this environmental perspective LISA farming systems do rank higher. In short: LISA is economically and environmentally performing well, contributing to satisfying the macro-economic need of sufficient production and social need for increased rural employment. The response of agricultural crop production in the study region to climate change was found to be strongly dependent on:
This suggests that general statements about crop productivity change have little relevance, and that land use change studies need to consider, explicitly, the variability of climate and other environmental factors in space and time. Within this context, the consideration of land management is central to any attempt to estimate potential future changes in land use. Levels of management vary considerably not only between regions, but also between farms within the same region. It is useful, therefore, to be able to present the outputs of land use change analysis (yields, annual benefits, etc.) in terms of quantitative parameters that relate to the intensity of management (e.g. nitrogen fertiliser amount). Differentiation of the simulation output according to a weather characteristic that varies between years (normal, dry and wet years) gives valuable further insight into the economic risk associated with different levels of management. Economic risk will become increasingly important in the context of climate change if increased variability in the weather is perceived by on-farm decision-makers. Future Land Use Change - a conclusion of the IMPEL project on the Europe scale The performance of IMPEL in comparison with observed land use data provided useful insights into the processes of land use change. In more than one case study, a discrepancy was observed between the modelled and observed land use data, where a new crop had recently been introduced into an area. These differences were often shown to decrease with time. It seems probable that the model has anticipated the move toward a new crop before it has actually happened. This occurs because the model assessment is based on maximum profit, but many farmers are reluctant to move to a new, untested crop, immediately. With time the crop is adopted as it becomes apparent that it is profitable. This point highlights the role of rates of change in decision-making, which may be very important with climate change. Will farmers be able to keep pace with the changes in their environment that are caused by climate change and thus, select the optimum land use for the new conditions? This question goes to the heart of the debate on adaptation to climate change and thus, our ability to model these time-lags is very important for future climate change studies. Further discrepancies between modelled and observed land use data could be attributed to the influence of risk aversion in land use decision making. In modelling experiments that excluded this factor, for example in eastern England, risk perception was shown to be extremely important in determining crop selection. Risk aversion is likely to be increasingly important in the context of future climate change if the climatic variability between years increases. The results of the case studies as a whole suggested that land use change will be more pronounced in northern Europe than in the south. This is because the farming systems in northern Europe have greater flexibility (more suitable soils and climate and more investment) and so, the decision-making of the farmers in these regions is based on more choices, i.e. more land uses. Thus, the apparent stability of southern European land use suggested by the models is misleading because a lack of choice is commensurate with a poor ability to adapt to environmental change. Put another way, the farmers of southern Europe are more vulnerable to change. This point highlights the importance of considering climate impacts on agriculture within the context of farm typologies and management regimes. Some typologies are inherently restricted in their ability to change because, for example, of a dependency on contractors, and so they will be poorly adaptable to climate change. The inability to adapt to environmental change will probably also be a strong feature of Mediterranean agroecosystems, where the lack of choice is strongly pronounced. Thus, the key issue for the Mediterranean regions is whether the limited land use options can be maintained in the future in the face of increasing environmental change pressures. If not, what are the consequences? References Rounsevell,M., Armstrong, A., Audsley, E., Brown, O., Evans, S., Gylling, M., Lagacherie, P., Margaris, N., Mayr, T., de la Rosa, D., Rosato, P., Simota, C., 1998. The IMPEL project: integrating biophysical and socio-economic models to study land use change in Europe. Proceedings of the World Congress for Soil Science, Montpellier (France) 20-26 August 1998, Symposyum 35, Scientific registration 1279, Cirad, (on CD-ROM) |
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 This book is the result of the proceedings of the Romanian Environmental Forum, 6th edition held in Bucharest between 16 and 19 November 1999. 
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