<p>Soil bacterial communities play an important role in soil health, carbon (C), and nutrient cycling, as well as in soil-plant relationships in agroecosystems. However, our understanding of the drivers and distribution of soil bacterial communities across landscapes is limited. For example, it is not clear how changes in soil management practices (i.e. Till vs No-till vs cover crop), soil diagnostic units, and their associated physical-chemical properties interact to influence the composition and abundance of soil bacterial communities at a larger scale. Here, using samples collected in a countrywide soil survey in Hungary, we combined soil metagenomic sequencing, soil management practices, and soil geochemical data to develop a mechanistic understanding of the drivers of bacterial communities in contrasting agroecosystems. We found that bacterial community composition and distribution significantly differed between soil management practices. Furthermore, we found that soil geochemical properties influenced soil bacterial composition and abundance under similar soil diagnostic units, suggesting that the effects of soil management practices on bacterial communities outweighed the ones of pedogenic processes. Together, these results suggest that soil management practices influence soil geochemical properties that drive the composition and spatial distribution of soil bacterial communities. Consequently, effects and types of soil management should be taken into account when developing soil health indicators for agroecosystems.</p>

Waterlogging in agriculture poses severe threats to soil properties, crop yields, and farm profitability. Remote sensing data coupled with drainage systems offer solutions to monitor and manage waterlogging in agricultural systems. However, implementing agricultural projects such as drainage is associated with high uncertainty and risk, with substantial negative impacts on farm profitability if not well planned. Cost–benefit analyses can help allocate resources more effectively; however, data scarcity, high uncertainty, and risks in the agricultural sector make it difficult to use traditional approaches. Here, we combined a wide range of field and remote sensing data, unsupervised machine learning, and Bayesian probabilistic models to: (1) identify potential sites susceptible to waterlogging at the farm scale, and (2) test whether the installation of drainage systems would yield a positive benefit for the farmer. Using the K-means clustering algorithm on water and vegetation indices derived from Sentinel-2 multispectral imagery, we were able to detect potential waterlogging sites in the investigated field (elbow point = 2, silhouette coefficient = 0.46). Using a combination of the Bayesian statistical model and the A/B test, we show that the installation of a drainage system can increase farm profitability by 1.7 times per year compared to the existing farm management. The posterior effect size associated with yield, cropping area, and time (year) was 0.5, 1.5, and 1.9, respectively. Altogether, our results emphasize the importance of data-driven decision-making for agriculture project planning and resource management in the wake of smart agriculture for food security and adaptation to climate change.

The incredible scale of development in the world requires constant innovation of technologies used in agriculture. Precision agriculture is also one of the most defining trends of our time in drone technology, which represents a revolutionary innovation in arable crop production. Its continuous monitoring provides many opportunities to assess the specific culture, estimate yields, determine water shortages, nutrient deficiencies, and apply other decision support. Frequent flights provide farmers with up-to-date information so that they can respond to changes in status quickly and cost-effectively. The use of drones is also responsible for improving the competitiveness of agriculture, drones and solutions based on them are spreading rapidly around the world. After reviewing the relevant international literature, publications and materials published by stakeholders involved in drone technology, we conducted a questionnaire survey among farmers based in northern Hungary. Commitment and willingness to precision developments were discovered how open they are to modern agriculture achievements, to what extent are they willing to sacrifice technology, especially spray drones, to what extent do they keep in mind aspects related to the environmental load and with what willingness to invest. overall, the perception of precision elements is positive, farmers 71% considers the use of drones in plant protection, for example, a correct and useful idea, which reduces treading damage, chemical use and soil use.

The data available at the right time and in the right way and the economic calculations that can be produced from them, as well as the presentation of the produced information to managers, are crucial in the decision-making process for the organizations. Wide range of digital solutions has an important role to support the business activities and the decision making in the case of farm management as well. It is important to determine the structure of the data required to base the economic calculations, build a data model and create a multidimensional database, this lays the foundation for the calculation of economic and financial indicators, which can help in making economic decisions. In our article, we present the structure of a process-oriented data warehouse created according to the OLAP (online analytical processing) principle, which is suitable for displaying immediate results and creating analytical queries, as well as for displaying different combinations of variables for making economic decisions.