Cereals in Europe are mainly grown with intensive management. This often leads to the deterioration of the physical properties of the soil, especially increasing bulk density due to heavy machinery traffic, which causes excessive soil compaction. Controlled traffic farming (CTF) technology has the potential to address these issues, as it should be advantageous technology for growing cereals during climate change. The aim of this study was to compare the yield potential of CTF and standardly used random traffic farming (RTF) technology using yield maps obtained from combine harvester and satellite imagery as a remote sensing method. The experiment was performed on a 16-hectare experimental field with a CTF system established in 2009 (with conversion from a conventional (ploughing) to conservation tillage system). Yield was compared in years when small cereals were grown, a total of 7 years within a 13-year period (2009–2021). The results show that CTF technology was advantageous in dry years. Cereals grown in the years 2016, 2017 and 2019 had significantly higher yields under CTF technology. On the contrary, in years with higher precipitation, RTF technology had slightly better results—up to 4%. This confirms higher productivity when using CTF technology in times of climate change.

Increase in machinery size and its random traffic at fields cause soil compaction resulting in damage of soil structure and degradation of soil functions. Nowadays, rapid methods to detect soil compaction at fields are of high interest, especially proximal sensing methods such as electrical conductivity measurements. The aim of this work was to investigate whether electromagnetic induction (EMI) could be used to determine trafficked areas in silty clay soil. Results of randomized block experiment showed a high significant difference ( p <0.01) in EMI data measured between compacted and non-compacted areas. EMI readings from compacted areas were, on average, 11% (shallow range) and 9% (deep range) higher than non-compacted areas, respectively. This difference was determined in both shallow and deep measuring ranges, indicating that the difference in soil compaction was detected in both topsoil and subsoil. Furthermore, the data was found to have a significant spatial variability, suggesting that, in order to detect the increase in EMI (which shows the increase in soil compaction), data within close surrounding area should be included in the analyses. Correlation coefficient of EMI and penetration resistance (average moisture content 32.5% and 30.8% for topsoil and subsoil) was found to be 0.66.

Controlled traffic farming (CTF) is used to confine soil compaction to the least possible area of the field, thereby achieving economic and environmental benefits. In the context of climate change, soil erosion is one of the most discussed topics, and there is a research gap in understanding the effects of CTF on soil erosion in Central Europe. The aim of this work was to show the potential of CTF to reduce water erosion, in terms of water runoff and soil loss on steep land. A 16 ha experimental field with a CTF technology implemented since 2009 at the Slovak University of Agriculture was used in this research. Three traffic intensity locations were selected and watered using a rainfall simulator. The results showed that the soil which had not been wheeled for 12 years had the lowest water runoff: its intensity after 20 min of simulated rain was 10 times lower compared to the multiple traffic treatment. The soil loss, expressed as the total soil sediments collected after 35 min, in the no traffic area was lower by 70%, compared to the soil with one-pass treatment and only 25% of the loss in the multiple traffic areas. These results show that CTF can significantly reduce soil loss through water runoff on steep land.

One of the basic tools used in precision agriculture technology is yield monitoring and mapping. Yield maps are used to monitor the efficiency of applied inputs, to assess the variability of field and have potential to be used in farm records and farm management software. The paper deals with comparison of yield maps with information on yield obtained from hand sampling. Analyses are based on five-year data from one field with spring barley, oil seed rape, winter wheat and maize crop rotation. Results show that the correlation coefficient across the time period reached a value of 0.89 (p <0.05), which means that the yield maps show the yield trend of the field. However, looking at the data in more detail and comparing the pairs of data for selected (discrete) monitoring points, the error of estimation was calculated. This error showed that the yield monitor underestimated or overestimated the yield in average by 25 %. Therefore, the data cannot be used to evaluate the absolute yield at selected place of the field