Project title: Simulation of climate scenarios and the bio-economic assessment of different agricultural management systems in the Marchfeld region
F. Strauss, E. Schmid, E. Moltchanova Collaborating GEO-BENE partners: BOKU (lead), UTL, SSCRI, IIASAIntroduction
The aim of this analysis is to assess the impacts of climate change on crop yields, soil organic carbon stocks, and nitrogen leaching, as well as on the profitability of different crop production systems in the Marchfeld region. Rischbeck (2007) used downscaling methods from global climate models to predict climate change for the Marchfeld region. In this study we have developed regional climate scenarios for the next 30 years using a linear regression model based on daily weather observations from 1975 to 2006 at the weather station in Grosz Enzersdorf. The climate scenarios are integrated together with other site specific data such as soil types, two crop rotations (corn-winter wheat-sunflower-winter wheat-spring barley, and sugar beet-winter wheat-field peas-winter wheat-spring barley) and different crop managements systems (conventional, reduced or minimum tillage with or without irrigation, as well as with or without straw removal) in the biophysical process model EPIC (Environmental Policy Integrated Climate; Williams, 1995; Izaurralde et al., 2006). The most important modules in EPIC are weather simulation, hydrology, erosion and sedimentation, Nitrogen-, Phosphor-, Potassium- and Carbon-cycles, plant growth, soil qualities and tillage operations. Hence, panel data of crop yields, soil organic carbon stocks and Nitrogen leaching were simulated with EPIC depending on the site data including 30 climate scenarios, and the twelve crop production systems. Moreover, the simulated crop yields are used to assess the economic profitability of the different production systems in the region.Data and Method
The data source for the climate scenarios are daily weather observations from 1975 to 2006 in Grosz Enzersdorf. Trends for temperature, solar radiation, relative humidity, wind and precipitation were calculated by means of linear regression, in which the residuals were reallocated randomly by retention of the monthly sequences. This process of reallocation was repeated 30 times to capture the variability and uncertainty in the climate scenarios. EPIC simulations over 64 years (1975 to 2038) were performed to analyse the impacts of climate change and different crop production systems on crop yields, soil fertility and nitrogen leaching. Furthermore, the variable production costs were calculated for each crop and production system (BMLFUW, 2008). In the profitability analysis we compare stochastic producer prices, based on historical price time series, and average variable production costs. The historical price time series provide a basis for the generation of stochastic prices for the two periods from 1975 to 2006 and from 2007 to 2038. We presume that prices are subjects to normal distribution from which prices are taken randomly (no trends are accounted in this analysis). Frequency distributions show, how often the average variable costs are above or below the stochastic producer prices. The redistribution of frequency gives information about likely impacts of climate change on the profitability of crop production in the Marchfeld region.Results and Discussion
The simulation results of both crop rotations show that soil organic carbon stocks are larger under minimum tillage and without straw removal. On average, the soil organic carbon stock is 110 t/ha in this region. Nitrogen leaching is lowest with minimum tillage and with straw removal and is on average 22 kg/ha. Under c.p. both environmental indicators are decreasing over time on average. The decrease of soil organic carbon stock is related to the increase of soil temperature and thereby to higher CO2 respiration. Moreover, an increase in annual precipitation leads to higher sediment transportation rates. The combination of an average annual increase of temperature by 1.3 °C with an increase of CO2 concentration to 443 ppm in 2038 and an increase of annual precipitation by 100 mm leads to less nitrogen leaching in this region on average. It is mainly due to higher biomass production and therefore higher nitrogen uptakes by crops. All crop yields show a positive trend over time on average. Corn yields increase by about 0.5 t/ha, and spring barley and sunflower yields by about 0.02 to 0.07 t/ha (dry matter). Using irrigation, the variability of crop yields as measured by the standard deviations is somewhat reduced in most cases. The profitability analysis (Table 1) indicates that production of corn is most profitable with minimum tillage and irrigation. The percentages describe how often average variable costs are below stochastic producer prices. The production of sugar beets is most profitable in this region. The production of winter wheat, sunflower, field peas and spring barley is most profitable with minimum tillage and without irrigation. Not removing straw from the field has positive crop yield effects for winter wheat and sunflower. The production of field peas does not seem to be economically viable in this region, however, within a crop rotation system other benefits account obviously more. The comparison between the periods 1975 to 2006 and 2007 to 2038 shows that the profitability of corn is increasing without irrigation and decreasing with irrigation, but the production of corn is profitable in both periods. The profitability of winter wheat, spring barley and field peas is slightly increasing whereas the profitability of sugar beet and sunflower remains nearly unchanged between the two time periods. The major conclusion of this analysis is that minimum tillage represents a profitable and environmentally sound production system, especially under the impact of climate change.
Table 1. Profitability scores in % (SB=sugar beet, C=corn, W=winter wheat, P=field peas, SF=sunflower, SB=spring barley) for different tillage operations (conventional, reduced or minimum) and crop managements (I=irrigation, nI=no irrigation, S=straw removal, nS=no straw removal)
References
- Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft, 2008. Deckungsbeiträge und Daten für die Betriebsplanung, Berger, Horn (BMLFUW).
- Izaurralde, R.C. et al., 2006. Simulating soil C dynamics with EPIC Model description and testing against long-term data. Ecological Modelling 192(3-4), 362-384.
- Rischbeck, P., 2007. Der Einfluss von Klimaänderung, Bodenbearbeitung und Saattermin auf den Wasserhaushalt und das Ertragspotential von Getreide im Marchfeld. Dissertation at University of Natural Resources and Applied Life Sciences, Vienna, 1-155.
- Williams, J.R., 1995. The EPIC Model. In: Singh, V.P. (eds.), Computer Models of Watershed Hydrology. Water Resource Publications, Highlands Ranch, Colorado, 909-1000.