Evaluation of the effectiveness of support allocated to the agricultural sector

The report provides an analysis of the agricultural sector from 2015-2022, and it is implemented as part of the ongoing evaluation of the Latvian 2014-2022 Rural Development Programme (RDP).

The scope of the work is to evaluate the distribution and effectiveness of national and EU support provided to agriculture and rural development in Latvia. The objective is to assess how various agricultural sectors contribute to the national economy and define key indicators for analysing future state and EU investments and their returns. The tasks involve an analysis of economic indicators, including turnover, employment and income within each of the agricultural sectors; reviewing the taxes paid in the agricultural sector and its contribution to the economy; and comparing the situation with neighbouring countries Estonia, Lithuania and Poland using available FADN data. Together with conclusions and recommendations, the work proposes a list of indicators to analyse the effectiveness of state and EU investments in agriculture and rural development, broken down by agricultural sectors.

The study used publicly available SUDAT (Latvian Farm Accountancy Data Network) and FADN (Farm Accountancy Data Network) data on agricultural economic indicators in Latvia and neighbouring countries, as well as data prepared by the state revenue service on taxpayers, workforce, and taxes in Latvian agricultural sectors according to the NACE 2 classification (statistical classification of economic activities). The obtained information was analysed to provide an international comparison of agricultural economic indicators by specialisation and assess the overall sector's contribution to Latvia’s economy.

The study utilised mathematical and statistical data analysis of dynamics/time series and the analysis of average and relative indicators. A linear regression model was used to forecast property tax. The sector's contribution to agriculture was determined by a multi-criteria ranking method, within which several indices were calculated, showing the relative importance of each specialisation. The study identified that data availability is fragmented, sourced from different origins and compiled using different methodologies.

For example, the state revenue service collects information on taxpayers defined by agricultural activity according to the NACE 2 classification. However, it does not administer all taxes. The rural support service compiles data on beneficiaries, but it does not group them according to NACE 2 or SUDAT classifications, limiting its applicability for this study. FADN data provide an overview of specialisations based on SUDAT. Still, for Latvia, data on 4-5 out of 7 specialisations are available depending on the year, and it includes only production taxes, not the total tax amounts. The study used multiple information sources to complement each other, as no single source provided a complete picture of all aspects of the study. Missing data were supplemented with calculated estimates based on assumptions. Given these limitations, the conclusions reflect general trends and are inappropriate for formulating categorical judgments.

A comparison of Latvia’s agricultural data with other countries using FADN data revealed that Latvia neither leads nor falls behind significantly in any indicator or specialisation. Developments in farm size and labour input have mainly occurred in arable farming and permanent crop specialisations since 2015, with other sectors experiencing declines. Latvia’s agricultural efficiency, measured by output and net value added per unit of area or labour, generally lags behind Poland and Estonia, demonstrating Poland’s ability to exploit its agro-climatic advantages even despite the average size of smaller farms, obtaining higher yields and values from the main resource (land) and Estonia’s more intensive farming model. Latvia is consistent with neighbouring countries, with payments per one-hectare fluctuating on average at +/-20%. The analysis also indicates a decline in the overall financial return on agriculture, particularly crop farming, with taxes showing a downward trend. The largest taxpayers include pig/poultry, vegetable and dairy farming. Recommendations include improving data collection methods, enhancing SUDAT sampling, and refining rural support service beneficiary databases to increase data representativeness.

The study recommends using a multi-factor ranking method, including socioeconomic indicators such as managed land area, wage funds, tax-to-support ratios and output value per FTE (full-time equivalent), to assess the economic performance of agricultural sectors/specialisations. It also recommends adding an environmental service component to future indicators to determine the objectives of reducing environmental pollution and preserving biodiversity. Improved data from SUDAT and rural support services would significantly impact analysis quality and provide a comprehensive understanding of the support’s effectiveness.