Summary
In the paper circumstances of mutual relations between agriculture, food securi-ty and climate change and their impact on challenges for the world were presented. Next, the relevant approach for research problem solving was shown through study-ing different criteria of existstudy-ing models to define shortages of knowledge, possibili-ties of linked models, their profiling towards reaching the assumed goal that means mitigating climatic risk. One can attempt to level errors resulting from the data and model manipulations. Moreover, one can present a study on global warming proven in the Bydgoszcz region that confirmed tendencies of climatic changes and their im-pact on growth in food production risk. The UTP team, on the basis of long-term agro-climatic surveys in the Bydgoszcz region, observed changes and their interrela-tions have shown promise for creation of new modules of models for assessment of mutual impact of development of local agriculture and climatic changes and possi-bilities of their usage in area of decision making in farms.
Keywords: climate changes, agriculture, food security, models, regional study 1. Introduction
Agriculture, food security and climate change front key challenges for the world. The 2007– 2008 world food crisis was a harsh reminder that all countries need to build more flexible with regards to food systems in light of the following changes. Research must play a leading role in bringing effective solutions. Europe has and continues to develop knowledge and technologies to support sustainable and competitive food production systems. Agriculture is highly exposed to climate change – the variability of crop yields has already increased as a consequence of extreme climate events, such as the summer heat wave of 2003 and the spring drought of 2007 in Europe. In a regional context, e.g. the last winter frosts have damaged many areas of crops such as winter wheat and rape in the Kujawy & Pomorze provinces. However the agriculture and forestry sectors also offer the potential for mitigation of N2O and CH4 emissions, while reducing greenhouse gas (GHG) emissions associated with indirect land use change and the development of verifiable GHG mitigation and carbon sequestration measures. Agriculture has to meet a demand for food which is estimated to rise globally by 50% by 2030 and to double by 2050, all due to population growth, urbanisation and increased wealth in many societies [4]. The European Research Area needs to
play its role in sustainable growth within the agricultural sector (crops, livestock, fisheries, forests, biomass and commodities) to meet the growing world food demand, enhance rural livelihoods, address competing demands on land use for biomass production, stimulate sustainable economic growth, maintain and restore ecosystem function services and also make the transition to a bio-based economy.
Suitable agricultural policy and trade policy should ensure a balance between the above men-tioned goals in order to mitigate the desired climatic changes through rational farming and food trade. It can ensure enough food for our rapidly growing global population. Research centres should create appropriate models, tools and techniques to solve the specific problems that are noted both at the farm, region, country and EU levels.
Paying attention to the above mentioned global and European circumstances it is necessary to analyse specific determinants of agricultural production and agribusiness development of particu-lar local areas like Kujawy & Pomorze Province. It is even more justified that this province plays an important role in the Polish food system. On the other hand, Poland is a one of key players among food exporters in the EU countries. So, a detailed analysis of the defined factors has critical meaning not only for Poland but also for the EU societies. Hence, the findings on climatic changes in the Kujawy & Pomorze provinces and their possible effects on risk agricultural production will be also presented.
In the study a review of some model solutions will be made on the basis of the findings of se-lected EU research centres to show how use more effectively potential resulting in many differen-tiated models to reach defined earlier goals.
2. Genesis of research problem
Observed climate warming has positive and negative effects on agriculture. Among the posi-tive effects one can notice is a lengthening of the agricultural vegetation periods, providing the opportunity to generate new species and varieties as well as increase yields and biomass of after crops.
Among the negative effects one can immediately notice is a growth in the climatic risk of cropping, increasing frequency of not desirable phenomena and disturbance of climatic water conditions. Hence, one can notice growth in water shortages and expansion of new pests and dis-eases of crops.
Among remedial activities in European scale in the frame of “Europe 2020” strategy the member countries committed in order to fulfill obligations that before 2020 would limit green-house gases up to 20% and increase the share of renewable energy in energy basket up to 20% and raise energetic efficiency up to 20%.
In February 2011, to limit a global increase in the world’s temperature to 2° C, the Council of Europe confirmed its intention to reduce greenhouse gas emissions before 2050 up to 80 – 95% compared to that of 1990.
Taking attention necessary efforts form developing countries such policy let limit the world’s emissions up to 50% before 2050 through reductions in particular economic sectors.
Before 2050, emissions other than CO2 from agriculture can be limited up to 42–49% com-pared to 1990 (the Commission Analysis). This sector has already reached the essential level of
emissions agreed upon with forecasts. Before 2050 the agricultural sector would be responsible for one third of the total EU emissions, three times more than now.
Achievement of such strategic objectives in agriculture should be reached through growth in productivity in the land usage sector to be in agreement with the sustainable development princi-ple, anritasimonetta@primus.cad, especially through such activities as efficient fertilizer usage, bio-gasification of manure, improvement in the management of organic fertilizer, feed updating, local diversification and commercialisation of production, increase animal breeding productivity, maximization of profits coming from extensive agriculture and also updating agricultural and forestry practices.
There is a need within the framework conducted on agricultural and economic policies, to carefully manage with any negative effects of human activity for the remaining resources (e.g. water, soils, and biologic diversity). There is also a need to look into all kinds of land usage in a holistic way to mitigate the effects of climatic changes.
3. Circumstances for solving a research problem in view of the MASCUR TradeM project goals
In the context of a defined research problem one can ask the question, “What models in Eu-rope are available?”
To achieve it, within the frame of Joint Programming Initiative, the FACCE JPI – MACSUR project entitled: “A detailed climate change risk assessment for European agriculture and food security, in collaboration with international projects” will be taken attempt to ask the question regarding the way global climatic changes have an impact on the development of European agri-culture, food security and international trade in Europe and third world countries. Food security is understood as a necessity in order to balance the global food supply and demand in view of demo-graphical quick growth and threats resulted from climatic changes determined smaller global food supply.
A priority of the project TradeM theme is finding the optimal methods of generalization of the model effects that will be compatible with different levels of agriculture beginning with a farm and ending with reference to European and global levels.
Study goals include uncertainty areas considered in applied models by the project partners as key determinants of risk assessment being related to expected climatic changes and their influence on long-term economic development combined with food production in its global supply-demand system changes.
A main goal of the TradeM subtheme is an integration of different research approaches in the scope of food production and distribution efficiency in a context of their economic impact on the development of agriculture from the point of view of farms, sectors, regions, countries, and Eu-rope.
Regional and sectorial liaisons of food supply and demand should be the subject of research in the frame of themes closed to plant and animal production named respectively CropM and LiveM. Comparative analysis at the regional level is necessary for identification of cost advantages be-tween regions and sectors taking into consideration labour and land factors to set agricultural pro-duction efficiency indexes.
4. Model approach to solving a research problem1 4.1. Model structure for research problem solving
Figure 1. Model structure of research problem (AGMEMOD case)
Source: G. Woltjer, I. Bezlepkina, M. van Leeuwen, J. Helming, F. Bunte, E. Buisman, H. Luesink, G. Kruseman, N. Polman, H. van der Veen, T. Verwaart, 2011, The agricul-tural world in equations – An overview of the main models used at LEI. LEI-DLO De Hague.
Presented in Figure 1 is the model structure of research problem that points to the main goals and assumptions of the realised project in a scale of groups of countries in the context of trade globalisation.
AGMEMOD stands for 'AGricultural MEmber states MODelling' (http://www.tnet.teagasc.ie/agmemod/). Since 2001, it has been developed by the AGMEMOD Partnership, a consortium of national university institutes and research agencies from EU countries and potential accession countries [3]. AGMEMOD's main objective is to confine the heterogeneity of European agriculture across EU Member States, while enabling simulations of the CAP and national agricultural policies in a consistent and harmonized way. Yearly projections are conduct-ed for each commodity and country for a ten-year span. These serve as baselines for impact anal-yses of policy changes [1, 2, 9].
AGMEMOD runs and solves in a GAMS environment [23]. It is a dynamic, partial, multi-country, multi-market equilibrium system. It can provide significant details on the main
al sectors in each EU Member State. Most equations have been estimated econometrically at the individual Member State level. Where estimation was not feasible or meaningful, parameters have been standardized. The country models include the behavioural responses of economic agents to changes in prices, policy instruments and other exogenous variables on the agricultural market. Commodity prices clear all markets considered. A bottom-up approach has been used to integrate country models into AGMEMOD.
Country models are based on templates. These templates give suppleness to mirror the differ-ences in agricultural systems but guarantee that the country models can be integrated into a com-posite EU model. Analytical consistency across the country models is essential to combine them and realize the knowledge hub concept which was assumed within MACSUR. It also facilitates the comparison of policy impacts across different countries. Figure 1 presents this combined structure of AGMEMOD. The modelling systems' projections are validated by standard econometric meth-ods and through consultation with experts who are known the agricultural market in the regions under study. The AGMEMOD model includes the expertise of an extensive network of economists collaborating across the EU. This growing network brought together a level of pan-national exper-tise that would have been difficult to assemble otherwise. Their activities are supplemented by the assistance of national experts in commodity markets in the individual countries, who frequently review the models and projections produced by the national modelling teams (Salamon and Sal-putra, 2008) [29, 30].
In the framework of next subchapter some model structures have been presented to show the possibilities of integration of knowledge useful for the formulated research problem approach. 4.2. Review of selected model solutions useful in MACSUR goal attainment
MAGNET, i.e. Modular Applied General Equilibrium Tool, until 2010 called LEITAP [34]2, analyses the effect of changes in trade and agricultural policies on international trade, production, consumption, prices and use of production factors. The model is mainly used to simulate long-term scenarios and to analyse policy options within these scenarios. By coupling MAGNET with bio-physical models such as IMAGE or CLUE, results about greenhouse gasses or biodiversity may be generated. The model is used, for example, to investigate the effects of the EU agricultural policy, including second-pillar policies, and bio-fuel policies. The model uses a consistent database for the entire world and gives a complete and internally consistent description of the world economy. Both price and quantity changes are in, but not the quantities in physical units (tons, et cetera), although these can be easily added for the sectors where useful quantity indicators (such as tons of wheat, tons of coal, et cetera) are available. The MAGNET land supply curve approach provides the opportunity to analyse land use effects of policies around the world. The model is very general in character and has a tendency to use constant elasticities as much as possible. For some im-portant parts, such as consumption, some improvements have been made in MAGNET, but the empirical foundation remains weak. The Armington approach to international trade allows for bilateral trade, but it simplifies competition a lot and is not automatically guaranteed that the re-sults are consistent with quantitative supply balances in agriculture, while Armington elasticities are fixed [31].
CAPRI is a global agricultural partial equilibrium model with a focus on the EU27, including Norway and the Western Balkans. The CAPRI model consists of two interlinked components: individual regional non-linear programming models per NUTS 2 region covering up to ten farm types, and a global trade model. The supply module of CAPRI consists of a total of 1,888 inde-pendent mathematical supply models for the EU-27, of which 1,823 are farm-type models, and 65 are NUTS 2 supply models. These models cover around 50 crop and animal activities for each of the farm types and include approximately 50 different inputs and outputs [13]. The CAPRI global market model is a comparative static spatial global multi-commodity model. It covers 47 primary and secondary agricultural products and models bi-lateral trade between 60 countries grouped in 28 trade blocks.
The CAPRI market model is iteratively linked in a transparent and consistent way to the layer of non-linear regional mathematical programming models. The supply module consists of inde-pendent aggregate non-linear programming models representing activities of all farmers of a farm type in a region. The data are based on the Economic Accounts for Agriculture (EAA). The farm models have fixed input-output coefficients for each production activity with respect to land and intermediate inputs. Normally, a low and high yield variant for the diverse production activities is modelled. Requirements regarding NPK balances and feeding requirements of animals are taken into account. A land supply module allows for land leaving and entering the agricultural sector and transformation between arable and grass land in response to relative price changes [15].
DRAM is the Dutch Regionalised Agricultural Model which models regional and national ag-ricultural production and considers the interactions between agag-ricultural activities through input and output markets. DRAM concentrates on the effects of policy changes on input allocation, agricultural production, prices of animal manure and agricultural income at the sectorial and re-gional level. It is a mathematical programming agricultural sector model, which was first devel-oped at LEI in the late 1970s and early 1980s. DRAM belongs to the class of comparative static, partial equilibrium mathematical programming models. Partial equilibrium means that DRAM describes a market equilibrium for some selected (agricultural) input and output markets (e.g. manure market), and there is no feedback between the agricultural industry and the rest of the economy. Moreover, comparative static equilibrium models assume that production and consump-tion fully and immediately are adjusted to policy changes until a new equilibrium is found. Com-paring this new equilibrium with the initial situation shows medium term policy effects. Thus, comparative static equilibrium models do not illustrate a time path. Partial equilibrium models cannot be used to simulate cumulative responses to policy change. Moreover, whether this new equilibrium is actually reached also depends on the statement that exogenous variables remain constant during the adjustment period [14].
The Financial-Economic Simulation model (FES) based on FADN data was elaborated by LEI as an instrument for financial analysis of agricultural economic developments and policies: The first FES model was developed by Mulder [26]. The FES model may be used to answer questions such as:
– How many of the agricultural and horticultural holdings have a large chance to have finan-cial difficulties in the near future?
– What characteristics of agricultural and horticultural holdings decide their chance on sur-vival?
– How does a change in fiscal policy or agricultural policy change the financial perspectives of agricultural and horticultural firms?
– What are the effects of the financial economic development of agricultural and horticultural firms on the tax payments of these firms?
– What are the effects of diminishing market prices on the income of agricultural firms? – What are the effects of high energy prices on viewpoints of the greenhouse sector?
The model is about farms in the FADN database. Accounting is a reflection of the develop-ment of a firm in the past and its developdevelop-ment possibilities in the future. Accounting is therefore an outstanding framework for analysing the development of a firm. By means of simulation of the various possible yearly events, financial characteristics of a firm are updated from year to year. Both the events during the various years and the financial characteristics at the beginning of each year are returned in financial statements, the profit account and the balance sheet, respectively. The financial characteristics of a firm consist of the value and composition of assets and liabilities and the modernity of the assets. Examples of the yearly events which are simulated are farm ex-penditures, sales of products, tax payments, family exex-penditures, off-farm income, investments and loans. The events over the years are the result of such elements as the characteristics of the firm at the beginning of the year, the developments in the environment of the firm (e.g. in the sales market, the capital market and government policy) and the decisions of the farmers. FES calculates for each farm in (a sample of) the FADN database results and scales the results up to relevant aggregates. FES calculates results on a yearly base, and the standard simulation period is between 5 and 10 years in advance. Although the original FES model was developed for the Dutch FADN, the latest version was developed for all FADN countries. In 2008, the model has been used for calculating the EU 15.
FARM DSS model’s main objective is to support farmers’ decisions about efficient crop pro-duction technology selection paying attention to the owned and hired recourses in their quantita-tive and qualitaquantita-tive aspects, production structure, production scale, etc. The model was elaborated by Bojar [5].
On the basis of FARM DSS model an Expert System MOWM, based on algorithms used deci-sion, interpretation rules, functions, farmer-expert’s knowledge, external databases (FADN, EU-ROSTAT, Agricultural CENSUS, GUS, etc.) was elaborated upon. AI tools (AITECH package) allow for the application of model solutions to simulate different decision scenarios satisfying all potential end-users: farmers, manufacturers of production means, all interested organizations in sustainable development of agriculture and rural areas. FARM DSS is an expert system of branched structure giving satisfactory solutions. Modular structure of this model enables easy connection with others over the input of some data through the data aggregates that can decrease the laboriousness of input data preparation. It can model farms of any countries, any commodities in set up periods and let consider such endowments like land, capital, labour, sectors, etc. Aggre-gation of regions is possible over up-bottom strategy by aggreAggre-gation of some parameters of a given region to farm models (prices, climatic conditions, technical standards, other exogenous variables) through, inter alia, prices of raw food materials and their first level of processing. Each model is based on databases and knowledge bases and includes many products. Domain knowledge bases and databases include technical farm machinery and exploitation parameters, crop technologies, prices of products and means production, labour payments, bank interest rates, etc. Trustworthy data sources such as world prices (FAPRI, OECD-FAO), EUROSTAT data, policy parameters,
macro variables, e.g. on labour payment (national sources) also are used within the model. For every farm and business crops are calculated financial and natural inputs and outputs to find out the most satisfactory solutions and know how to select farm machinery under specific circum-stances [7].
It can be applied to simulate farm effect resulted in consideration of expected parameters de-fined in CAP implementation in Member States, CAP reform proposal 2011 or Roadmap 2050. Analytical consistency across the country farm models is essential to combine them and it also facilitates the comparison of policy impacts across different farm country models. The agro-climatic model for the Kujawy & Pomorze provinces, through intelligent agents (autonomous software applications), are able to search, link and co-operate with other model applications on their own).
Descriptions and objectives of review-selected models showed differentiated and broad re-search areas and solutions, which after their relevant integration and updating, can approach the assumed goals concerning climatic risk in the context of agriculture sustainable development, food security and environmental protection [6].
4.3. Problems of integration (scaling) of different models to reach assumed goals
Fig 2. Classification of scaling methods for different models
Source: Geert Woltjer, Irina Bezlepkina, Myrna van Leeuwen, John Helming, Frank Bunte, Erik Buisman, Harry Luesink, Gideon Kruseman, Nico Polman, Hennie van der Veen, Tim
Verwaart, 2011, The agricultural world in equations – An overview of the main models used at LEI. LEI-DLO De Hague: http://www.lei.wur.nl/NL/publicaties+en+product en/LEIpublicaties/?id=1394.
In Fig. 2 one can list some examples of the scaling methods application. First, we refer to problems of interpolation and sampling (Ib). In agriculture, data from the Farm Accountancy Data Network (FADN) provides the most detailed and harmonised data on the economic performance of farms and are often used to assess population characteristics [33]. The use of FADN data in a number of regional studies is often problematic due to the low number of observations. Several methods have been developed (e.g. Dol [11]) to use supplementary information to increase the trustworthiness of estimates. A statistical matching method has been applied in various studies to make use of additional information from the census to generate more consistent estimates in re-gional studies [32; 33]. The basic idea behind the method is that sample farms are matched to population farms based on the imputation variables, i.e. variables which are used to decide whether a farm is similar to a sample farm. The imputation variables should be known for all farms in the sample and the population and the distance between the population farm and the sample should be the smallest.
Next, we refer to the problem of upscaling and downscaling (IIc). Market level models, such as CAPRI or MAGNET, consider prices as endogenous variables and are able to capture price effects from simulated policies. However, market level models provide less detail in modelling agricultural production and production externalities than farm level models, and are therefore less fitting for integration with biophysical models. The primary reason for this is that most aggregate models derive the supply behaviour on the basis of representative cost or profit functions. A way of upscaling of farm supplies through newly-established market prices, that account for changes in supply, has been achieved in linking farm model FSSIM – Farm Systems Simulation Model [24] and market model CAPRI in SEAMLESS project (Pérez Dominguez et al., 2009 [27]). The basic model linking principle of the EXtraPolation and Aggregation MODel (EXPAMOD) is to parame-terise one model (CAPRI) using the simulated response behaviour of the other (FSSIM). EXPAMOD is, therefore, a statistical meta-model that describes the price-quantity responses of farms given specific farm resources and biophysical characteristics that are available EU-wide.
A meta-model, in this context, is an approximation of the input-output behaviour of the under-lying simulation model, i.e. it describes the main relationships between key FSSIM variables and the supply of products. Thus, the meta-model is estimated using simulated price-quantity data for farm types in regions for which FSSIM models exist and then applied to project supply responses of other farm types and regions. The MAGNET model has already a very flexible system of (dis)aggregating spatial units (countries) into groups, as well as sectors and their groups. A downscaling procedure has been developed and applied enabling to disaggregate model output to regions [31]. In this study the results from the MAGNET model that operates at the country level are scaled down to NUTS 2 regions of the EU Member States to compute the effects of poli-cy measures at a lower scale. The downscaling method builds up its complexity in a step-wise manner. It starts from a simple but consistent step assuming that regional percentage growth equals national percentage growth.
Next, hypotheses are formulated regarding factors that may explain the inequality in the per-centage of growth and market equations are added to allow for adjustment processes. For example,
both migration and allocation of production reacts to changes in wages and employment. Empiri-cal work to quantify differences between regional and national growth developments is carried out. The results of such econometric panel data estimations are integrated into the dynamic equations of the downscaling method, but also information from the literature or experts can be used. The sectorial aggregation on NUTS 2 level depends on the available data. It is also interesting problem how the scale and extent of a model chain can benefit the scope of the study if otherwise done with a stand-alone model only.
5. The agro-climatic findings from the Kujawy & Pomorze provinces
On the basis of the Intergovernmental Panel on Climate Change (IPCC) and IPCC Reports from 1990, 1995, 2001 as well as the 2007 survey and Report IV prepared by 600 authors from 40 countries reviewed by 620 experts, one can conclude that the probability that climatic changes are caused by anthropogenic gas emissions is about 5 percent, There is also a 90 percent probability that this extreme phenomena will increase.
5.1. Identification of climatic changes in Bydgoszcz region
The paper identifies the current climate changes drawing upon the results of the meteorologi-cal measurements and observations. Performing such research, one should focus on the homoge-nous series of meteorological data; the results of the measurements taken comply with the princi-ple of the comparability of results. It is assumed that the principrinci-ple of comparability is met if the measurement-taking site and its surroundings have not changed, especially due to progressing urbanization. With that in mind, one shall preferably focus on the results provided by the stations out of the city, located in the open area.
The aim of this paper was to evaluate the direction, range and degree of significance of the changes of the basic component determining the climate and agroclimate of the region of the city of Bydgoszcz from 1951 to 2010: the mean air temperature. The working hypothesis assumed that due to the climate changes, the region of the city of Bydgoszcz records changes in the temperature conditions of agricultural production as well as the climatic risk posed to crop growing. The paper uses the results of air temperature measurements taken following the standard procedure at the Experiment Station of the Faculty of Agriculture and Biotechnology, the University of Technology and Life Sciences in Bydgoszcz, at Mochełek, in the vicinity of about 20 km away from Byd-goszcz, in the south eastern margin of the Krajna Plateau (ij=53o13’, Ȝ=17o51’, h=98.5 m above sea level).
The station is located in a poorly-urbanized and poorly-industrialized area, far from the effects of local and municipal anthropogenic factors. The research covered a 60-year normal period from 1951 to 2010. The paper involves the uses of statistical methods as well as the methods of results presentation commonly applied in agroclimatology. Most notable were the mean multi-year values and extreme values in the multi-year period. For each parameter, namely the air temperature in the time step assumed (month, half-year, year), the standard deviation to evaluate its time variation was also determined. To determine the changes in respective indices and with time from 1951 to 2010, the trend method with the use of linear regression equations was applied [18]. To define a potential widening of the time variation (extremities) of respective indices, their variations were compared for 30-year periods: 1951–1980 and 1981–2010.
Table 1. Statistics of the indices of mean air temperature in the Bydgoszcz region from 1951–2010 [°C]
Okres ĝrednio Max Min Zmiana na 10
lat R2 I -2.3 3.4 1983 -11.4 1987 +0.18 0.009 VII 18.1 22.7 1994 15.1 1974 +0.31 0.089* I-XII 7.8 9. 6 1989 6. 0 1956 +0.16 0.107* IV-IX 14.2 16.0 1992 12.7 1956 +0.18 0.163* * X-III 1.4 4.5 1990 -1.6 1963 +0.14 0.036
Source: own investigations.
The mean multi-year annual air temperature in the Bydgoszcz region from 1951–2010 was 7.8oC (Table 1). Its annual pattern was typical for Poland’s temperate and transitional climate, showing on average the lowest air temperature in January (an average of –2.3oC) and the highest in July (an average of 18.1oC). Air temperature in all the analysed months and periods recorded a very high time variation. The highest mean annual temperature was recorded in 1989 (9.6oC) and the lowest one in 1956 (6.0oC). The highest mean monthly temperature for the multi-year period analysed was 22.7oC (July 1994) and the lowest –11.4oC (January 1987). A greater time variation concerned the temperature of winter months, mostly January and February, followed by December and March, whereas the lowest – June, August and September. The multi-year extreme mean air temperature occurred in all the decades of years. However, interestingly, the most extreme maxi-mum mean temperatures (12 out of 15 analysed) were recorded in the 30-year period of 1981– 2010, while the minimum extremes (10 out of 15 analysed) in the 30-year period of 1951–1980. Two extremes (December being the warmest in the 60-year period investigated and October – being the coldest one) were noted in the last ten-year period.
The trend analysis showed that over 1951–2010 the mean air temperature of the Bydgoszcz region in most (10 out of 15 analysed) time spans recorded an increase over time (Table 1). The equations of linear trends were significant in four cases at the confidence level of 0.05 and con-cerned April, July, August and the entire year as well as in two cases (May and summer half-year) at the confidence level of 0.01.
Table 2. Comparison of the time variation of air temperature indices in the Bydgoszcz region from 1951–1980 and 1981–2010 Period SD 1951-80 SD 1981-10 Difference max-min 1951-80 Difference max-min 1981-10 Growth in temporal variability I 2.74 3.71 13.8 14.8 + VII 1.57 1.89 5.3 7.4 + I-XII 0.74 0.88 3.0 3.4 + IV-IX 0.71 0.73 2.4 2.9 + X-III 1.24 1.30 4.9 5.5 +
Source: own investigations.
Simple statistical calculations demonstrated that over the 30-year period of 1981–2010, as compared with the previous 30-year period of 1951–1980, the time variation became wider in most of the air temperature indices (Table 2). A greater standard deviation and the range were observed for the mean annual temperature of both half-years and the monthly temperature in January, Feb-ruary, May, July, August and October.
Following the fourth IPCC report [2007], the mean air temperature in Europe over the last century (1906–2005) increased by 0.74ºC. Those changes were noted in all the seasons: the high-est was March through May and the lowhigh-est was September through November. As specified in the report, the temperature changes on Earth are mostly due to a growing concentration of greenhouse gas emissions in the atmosphere as a result of human activity. KoĪuchowski [18] also quotes other facts pointing to climate warming; most importantly glacier melting, the shrinking range of perma-frost and the area covered with snow as well as an increase in the level of the world’s oceans. The same author summarised the research performed all across Poland or regionally which also shows the increasing air temperature tendencies over the periods of the second half or the last ten years of the 20th century. According to the author, the results comply with the observations reported in other countries and lead to a general conclusion that the climate is getting warmer both globally and regionally. According to Boryczka and Stopa-Boryczka [8], the 19th-to-20th-century climate warming was triggered by an increase in the activity of the sun, a decrease in the volcanic activity on the Earth and an increase in the greenhouse effect of the atmosphere. According to Przybylak and Maszewski [28], today’s climate warming in Poland is due to an increase in the frequency of cyclone events in a cool season (0.60 day/10 years) and, at the same time, anticyclone events in the warm season.
Climate changes and their forecasted effect on agriculture in Poland have been investigated by many research centres [10; 16, 17, 21, 22, 35, 36]. Climate change forecast scenarios for Poland assume an increase in temperature variation by more than 25%, which is of special importance since it is equal to the occurrence of a series of days with high temperature fluctuations (e.g. ground frost, extremely hot weather) [19]. Most climate change scenarios also assume a further increase in air temperature in Poland in the 21st century. For example, according to the climate forecast resulting from the HadCM2(GS) model, the mean annual air temperature until the mid-21st century will increase by 1.4oC and the mean air temperature in winter by as much as 3.3oC [12]. Similar values are reported by ŁabĊdzki [25], based on various sources, also pointing to a
probable increase in the intensity of extreme phenomena, including the frequency of a rainless hot-day sequence.
The research results presented in this paper on the tendencies of changes in the air temperature of the Bydgoszcz region over 1951–2000 coincide, in general, with the applicable literature, sig-nalling climate warming in Poland, thus suggesting the following conclusions:
The 1951–2010 multi-year mean air temperatures comply with the normative ones reported in publications defining Poland’s climate and agroclimate, which points to the representative na-ture of the Mochełek measurement site for the Bydgoszcz region.
The results of the research have confirmed a very high time variation of the air temperature in the Bydgoszcz region, specific for the temperate and transitional climate of Poland. The val-ues similar to the mean (normative) temperature valval-ues in the Bydgoszcz region can be ex-pected in about 40% of the years, namely on average once every 2.5 years. In the other years the temperatures show a deviation from the standard, which is often considered a symptom of climate changes.
Over 1951–2010 in the Bydgoszcz region there was reported a significant increase in the an-nual mean, the summer half-year and the mean monthly air temperature of April, May, July and August. In most cases (10 out of 15 analysed) a widened time variation of the air tempera-ture over 1981–2010 was found, as compared with the previous 30-year period of 1951–1980. Such widening can be interpreted as a symptom of climate changes which have been observed and, most of all, forecasted.
6. Conclusions
Risk assessment closed to interrelations of climatic changes, food demand and supply balanc-ing and possibilities of their mitigation with agricultural and trade policies will be made with pro-posed in the frame of the project model tools.
Elaborated in scientific and research centres models (which cases were presented), not fully up to now used, over proposed Knowledge Hub Center will be helpful in mitigating defined cli-matic risk.
Solving research problem will follow through studying geographical, substantial and other cri-teria scopes of former models to define shortages of knowledge, possibilities of models linking, their profiling towards reaching assumed goals.
One can make an attempt to level the errors that resulted from the data and the model manipu-lations (aggregation, disaggregation, interpolation, extrapolation, input stratification, output strati-fication, nesting, etc.).
Climate warming proved in Bydgoszcz region confirms tendencies of climatic changes and possibility of their impact on growth in food production risk. The UTP team, on the basis of long-term agro-climatic surveys in the Bydgoszcz region, observed changes and their interrelations will show the premise for the creation of new modules of models for the assessment of the mutual impact of the development of local agriculture and climatic changes and the possibilities for their application in the area of decision-making in farms.
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Research was made within the framework of the FACCE JPI – MACSUR project titled: ‘A de-tailed climate change risk assessment for European agriculture and food security, in collaboration with international projects’ – Contract no. FACCE JPI/04/2012; a portion of the P100 PARTNER (UTP) project is financed by NCBiR, Poland and a portion of the P192 PARTNER (LEI, part of Wageningen UR) is financed by Ministry of Economic Affairs (EZ), Wageningen UR, The Nether-lands.
UWARUNKOWANIA WPŁYWU ZMIAN KLIMATYCZNYCH NA PRODUKCJĉ ROL-NĄ PRZY UWZGLĉDNIENIU SPECYFIKI REGIONU
Streszczenie
W artykule przedstawiono wzajemne relacje rolnictwa, bezpiecznej ĪywnoĞci i zmian klimatu oraz ich wpływu na wyzwania globalne. NastĊpnie naĞwietlona zo-stała metoda rozwiązania problemu badawczego poprzez analizĊ róĪnych kryteriów istniejących modeli w celu zdefiniowania niedoborów wiedzy, moĪliwoĞci łączenia tych modeli oraz tworzenia ich profili, aby osiągnąü załoĪony cel, tzn. złagodziü ry-zyko klimatyczne. PodjĊto próbĊ eliminacji równowaĪenia błĊdów wynikających z manipulacji danymi i modelami. Po drugie, przedstawiono badania związane z ociepleniem klimatu w regionie Bydgoszczy, co pozwoliło potwierdziü tendencje zmian klimatycznych i ich wpływu na wzrost ryzyka produkcji ĪywnoĞci. Zespół UTP na podstawie długoterminowych badaĔ agroklimatycznych w regionie Bydgoszczy, zaobserwowanych zmian i ich wzajemnych relacji wskazał przesłanki tworzenia no-wych modułów modeli do oceny wzajemnego wpływu rozwoju lokalnego rolnictwa i zmian klimatu oraz moĪliwoĞci ich wykorzystania w obszarze podejmowania decyzji w gospodarstwach rolnych.
Słowa kluczowe: zmiany klimatu, rolnictwo, bezpieczeĔstwo ĪywnoĞci, modele, stadium regionalne
Waldemar Bojar
Faculty of Management; Department of Management Engineering University of Technology and Life Sciences in Bydgoszcz ul. FordoĔska 430 85-790 Bydgoszcz
e-mail: wald@up.edu.pl Jacek ĩarski
Faculty of Agriculture and Biotechnology
Department of Land Reclamation and Agrometeorology University of Technology and Life Sciences in Bydgoszcz ul. BernardyĔska 6/8, 85-029 Bydgoszcz
e-mail: zarski@utp.edu.pl Floor Brouwer
Rene Verburg LEI Wageningen UR
Subdivision, Section Environment and Nature PO box 29703 2502LS 'S GRAVENHAGE
Alexanderveld 5 2585DB 'S-GRAVENHAGE, Netherlands e-mail: floor.brouwer@wur.nl
