Adamski Mariusz’s scientific contributions

The purpose of the following work is to review the methods used in predicting plant yields, with particular emphasis on potato production. The article refers to the histological methods of estimating plant yields and prevailing trends: ground-based remote sensing, which is often associated with regression calculus, multiple regression, artificial intelligence and image analysis. There are also two popular models SUBSTOR and LINTUL-POTATO, which are the foundation for developing more and more accurate tools of potato yield estimation. There are many methods that allow to predict yields before the end of the growing season. The most important element in creating prediction models is choosing the appropriate number of independent variables that actually shape the yielding of potatoes. Timely and accurate prediction of crop yields improve the management of agricultural production as well as limit financial, quantitative and qualitative losses of crops.

The paper presents the application of a high precision positioning system ASG-EUPOS and its service NAWGEO for agricultural machines positioning. A measurement set was mounted on a cereal combine harvester and consisted of a GNSS antenna and receiver with a GSM modem for RTK corrections transfer. The positioning system was validated in a field during the 2011 harvest period in selected farms in southern and western Wielkopolska region in Poland. The total area of the field under study was 75 hectares. The quality of determining the machine's position was monitored. It was understood as standard deviation values for longitude, latitude and altitude above the mean sea level. The hypothesis about the importance of impact of the adopted criteria on the level of changes in the recorded deviation errors was tested. Field tests show usefulness of the ASG-EUPOS network and its VRS NAWGEO service for precise positioning of agricultural machinery in dynamic conditions. The obtained data can be used to create numerical models of fields on-line, for example, in selective cereals harvesting technology, but they require filtration to remove the points affected by positioning error exceeding the acceptable value.

Wheat, corn and rapeseed straw can be used as a raw material for the production of biofuels used to generate heat energy. Due to the growing interest in renewable fuels, including straw, it is reasonable to test the energy suitability of these fuels. The aim of the study was to compare the combustion heat and calorific value of particular straw species. The most important parameters enabling comparison of the above mentioned fuels were determined, which include: humidity, combustion heat and calorific value. The moisture content of individual wood samples was: 6.32% for wheat straw, 8.40% for corn straw and 8.49% for rapeseed straw. The analysis showed that the moisture content of the straw samples tested was at a similar level. Combustion heat analysis allowed to obtain the following results: 16.10 MJ·kg-1 for wheat straw, 16.60 MJ·kg-1 for rapeseed straw and 17.30 MJ·kg-1 for maize straw. The highest combustion heat was observed for corn straw. The same parameter for rapeseed straw was lower by 0.7 MJ·kg-1 than for maize straw. The lowest combustion heat of 16.10 MJ·kg-1 was observed for wheat straw. The calorific value of the samples tested was: 14.80 MJ·kg-1 for wheat straw, 15.30 MJ·kg-1 for rapeseed straw and 16.10 MJ·kg-1 for maize straw. The highest calorific value was found for corn straw. The calorific value of wheat straw was lower by about 9%, while rapeseed straw was lower by about 5% in comparison to maize straw. On the basis of the conducted research and literature data concerning the yields of straw of various cereal species, the amount of energy possible to obtain per hectare of cultivation was estimated. With the yield per hectare for: wheat straw 3.2 Mg d.m., rapeseed straw 3.3 Mg d.m., maize straw 12.5 Mg d.m. can be obtained for: wheat straw 47.36 GJ·ha-1 , rapeseed straw 50.49 GJ·ha-1 , maize straw 201.25 GJ·ha-1.

The aim of the work is to create a model for prediction and simulation of winter rapeseed yield. The model made it possible to perform a yield forecast on 30 June, directly before harvest in the current agrotechnical season. The prediction model was built using the multiple regression method (MLR). The model was based on meteorological data (air temperature and precipitation) and information about mineral fertilization. The data were collected from the years 2008-2017 from 291 production fields located in Poland, in the southern Opole region. The assessment of the quality of forecasts generated on the basis of the regression model was verified by determining prediction errors using RAE, RMS, MAE and MAPE error meters. An important feature of the created prediction model concerns the possibility of making the forecast in the current agrotechnical year on the basis of the current weather and fertilizer information.

The purpose of the following work is to review the methods used in predicting plant yields, with particular emphasis on potato production. The article refers to the histological methods of estimating plant yields and prevailing trends: ground-based remote sensing, which is often associated with regression calculus, multiple regression, artificial intelligence and image analysis. There are also two popular models SUBSTOR and LINTUL-POTATO, which are the foundation for developing more and more accurate tools of potato yield estimation. There are many methods that allow to predict yields before the end of the growing season. The most important element in creating prediction models is choosing the appropriate number of independent variables that actually shape the yielding of potatoes. Timely and accurate prediction of crop yields improve the management of agricultural production as well as limit financial, quantitative and qualitative losses of crops.