Multifunctional agriculture includes economic activities such as providing food and energy supply to the population, as well as environmental and social activities.
In the current context of increasing globalization of economic and social processes, the role and importance of agricultural statistical information in the field of multifunctional agriculture is constantly increasing.
The rich empirical data from statistical surveys reveals opportunities for establishing patterns in the condition and dynamics of farmed agricultural land, the distribution and use of labor, the availability of agricultural equipment, the factors on which the productivity and efficiency of agricultural production depend – i.e. the factors on which the productivity and efficiency of agricultural production depend.
The availability of momentum data for individual indicators allows the use of structural and variational statistical analysis, which extends the scope of statistical surveys. The formation of time series for long periods of periodic and momentary sets is a prerequisite for analysis in dynamics, and with appropriate statistical methods important trends can be identified and consequently forecasts can be created for the development of the agricultural sector, including the multifunctional agriculture.
The purpose and importance of statistics is to serve the information needs associated with all aspects of multifunctionality of agriculture. This would allow for further development of multifunctional farms and for evaluating their performance. Statistics on the number, activity and directions of multifunctional farms in Bulgaria are missing.
In relation to this, there are three main areas of statistics: the economic direction – it consists of production, markets and farmers’ incomes. It also covers land, labor and capital that are involved in the production process and the output that result from it.
These are activities where raw materials include natural sources (solar, land, water, animals and plants), products from other industries and services (fertilizers, pesticides, energy, know-how, etc.) and labor. The results consist of food, feed and other animal and plant products, renewable energy from biomass and more.
Input and output products are exchanged in markets where they are regulated by the functioning of the price mechanism. Key elements are the quantity of production, the prices of inputs and supplies, as well as farmers’ incomes.
Specific statistical methods exist for measuring the output of the production as well as traditional markets for goods and services, but further efforts are needed in the future to develop other relevant instruments for measuring less tangible results, such as eco and socio-cultural services.
The importance of statistics is in measuring and evaluating all aspects of economic direction, providing timely information for all interconnected economic aspects of multifunctional agriculture.
The environmental dimension refers to the role of the agricultural sector as a consumer of natural resources – mainly land, soil nutrients and water – and on the other hand as a provider of environmental services and its overal environmental impact of the sector.
The relationship between agriculture and the environment is very complex. Agriculture depends 100% on the environment, as it is part of the primary production obtained directly from biological processes. Environmental impacts are broad in scope: climate, air, soil, land, water and biodiversity. The impacts are harmful and in the same time beneficial to the environment.
The importance of statistics is to accurately represent the relationships and give a realistic picture of the magnitude of the impacts as far is possible. Agri-environmental statistics is a relatively new field of agricultural statistics. The methodology and data sources are mixed and often not the same as those used in traditional agricultural statistics.
Due to the complexity of impacts, statistics, scientific measurements, evaluation and modeling are often the most appropriate tools for the development of agri-environmental statistics.
The collection of reliable statistical data for the two-way impact of agriculture on climate change is a huge challenge for statistics. The environmental direction of statistics is likely to be one of the key areas for the next 10 years, supported by a large number of emerging policies and statistical needs.
The social dimension of multifunctional agriculture and rural development includes both issues of vulnerability (food security) and the conditions and quality of life of farmers and rural and household people in a wider context. The vulnerability is the result of a combination of environmental and economic risks.
The extreme climate conditions that have become more common as a result of climate change and the increasing production of biofuels in the agricultural sector have increased changes in production levels and thus made the process more unstable. This has an impact on both food security and on the livelihood of the agricultural population.
The social dimension also covers the living conditions of farmers and the rural population. Declining income levels, coupled with new responsibilities stemming from different policies, leads to smaller scale of agricultural development, less profits and thus negatively affecting the traditional family farming as a way of life.
Educational aspects are also part of the social dimension. In order to expand and strengthen the social trend in agricultural statistics, it is necessary to apply the methods of social statistics.
The need for data for different directions is different in nature and it would be effective to collect data intended for statistical analyzes for each of the domains separately. A specific approach should be applied here to enable statisticians and data users to understand the direction in which multifunctionality is developing. The problem lies not only in the collection of statistical data and in the ways in which they are collected, but in the correct summarization, analysis and interpretation of them in order to reveal the significance of the information collected.