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The trials were carried out in the Estonian University of Life Sciences (58°23'N, 26°44'E), and studied to what extent green manure crops bind nutrients and the effect and stability of biologically fixed nitrogen (N). Our research covered more species than most of the earlier studies in the Nordic countries. Compared with biomass from unfertilized barley, legume undersowing, straws plus roots added up to 4 times more N, 2.8 times more phosphorus (P) and 2.5 times more potassium (K) returning to the soil. Red clover, hybrid lucerne and white melilot as pure sows produced the highest biomass, amounts of N, P, and K being up to 206, 24 and 144 kg/ha, respectively. The effect of additional N in soil was measured by weighing successive grain yields. In the first test year, 1 kg of N from green manure had the effect of producing 8.6 kg grain and this relation did not change even for higher N amounts. Green manure had a significant effect even in the third year after the green manure was ploughed into soil.

Soil organic carbon (SOC) concentration is an essential factor in biomass production and soil functioning. SOC concentration values are often obtained by prediction but the prediction accuracy depends much on the method used. Currently, there is a lack of evidence in the soil science literature as to the advantages and shortcomings of the different commonly used prediction methods. Therefore, we compared and evaluated the merits of the median approach, analysis of covariance, mixed models and random forests in the context of prediction of SOC concentrations of mineral soils under arable management in the A‐horizon. Three soil properties were used in all of the developed models: soil type, physical clay content (particle size <0.01 mm) and A‐horizon thickness. We found that the mixed model predicted SOC concentrations with the smallest mean squared error (0.05%2), suggesting that a mixed‐model approach is appropriate if the study design has a hierarchical structure as in our scenario. We used the Estonian National Soil Monitoring data on arable lands to predict SOC concentrations of mineral soils. Subsequently, the model with the best prediction accuracy was applied to the Estonian digital soil map for the case study area of Tartu County where the SOC predictions ranged from 0.6 to 4.8%. Our study indicates that predictions using legacy soil maps can be used in national inventories and for up‐scaling estimates of carbon concentrations from county to country scales.

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The aim of this study is to analyse the efficiency of Estonian grain farms after Estonia's transition to a market economy and during the accession period to the European Union (EU). The non-parametric method Data Envelopment Analysis (DEA) was used to estimate the total technical, pure technical and scale efficiency of Estonian grain farms in 2000-2004. Mean total technical efficiency varied from 0.70 to 0.78. Of the grain farms 62% are operating under increasing returns to scale. Solely based on the DEA model it is not possible to determine optimum farm scale and the range of Estonian farm sizes operating efficiently is extensive. The most pure technically efficient farms were the smallest and the largest but the productivity of small farms is low compared to larger farms because of their small scale. Therefore, they are the least competitive. Since pre-accession period to the EU, large input slacks of capital have replaced the former excessive use of labour and land. This raises the question about the effects on efficiency of the EU's investment support schemes in new member states.