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Stand evaluation, crop estimation and yield analysis of winter wheat for the optimization of yields
103-109Views:135The authors have been carrying out stand evaluation, crop estimation and yield analysis in winter wheat since 2012. The sampling areas were assigned at the fields of the Training Farm of the Faculty of Agricultural and Food Sciences of Széchenyi István University Mosonmagyaróvár according to the structure of the cropping system. According to their observations the value of field emergence is always lower than the laboratory germination. The weak emergence is important because the lower plant density cannot be compensated by the increased tillering in spite of having larger plant growth space. It is proven by the fact that they detected strong productive tillering even at 5 and 10 mm plant spacing while there were single-spiked plants at 40-50 mm plant spacing as well. The analysis revealed that the total ear mass and grain mass of wheat plants bearing two or more ears is almost the double than that of the single-spiked plants. It was a further basic experience that the largest ear of ”multiple-spiked” plants is always heavier than the single ear of one-spiked plants. Plants with intense tillering and more ears demonstrate the importance of proper seedbed preparation and drilling and the significance of sowing good quality seeds. These are the factors that determine field germination and emergence, influence the speed and intensity of initial development and by all these factors the sufficient productive tillering. The authors emphasize the use of exact and objective methods at crop estimation, e.g. the relationship between the ear mass and the yield which is in strong correlation whilst ear length and grain mass are not suitable for a precise estimation. The authors conclude that crop estimation and yield analysis must be inevitable tools of modern crop production and will be particularly important in precision agriculture. These tools also qualify the job done by farmers and helps to identify the areas that require special attention.
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Stability and development of Transdanubian agricultural enterprises
77-82Views:136Personal fulfilment, financial security, flexibility, relationships, information, rules - these are all hallmarks of entrepreneurship. Furthermore, one more important factor should be added to the list, which enables satisfaction resulting from reliable income and self-fulfillment: this is openness. An open mind to changes, to novelties, and to the workforce is necessary. The central question of the present research is how to effectively develop Hungarian small and medium-sized agricultural enterprises, especially in the Transdanubian region, by utilizing these factors. In addition to production, institutional and price risks, agricultural enterprises, like other sectors, are also affected by massive labour shortages and resource-intensive development objectives. In the research, primary agricultural producers, micro, small and medium-sized enterprises were surveyed through questionnaire in the second and third quarters of 2019. Using the snowball method, both the development opportunities and the risks were mapped in this sector, mostly among growers. The research results show that there is a correlation between satisfaction and development and favorable workplace relationships. These correlations were presented by demonstrating the relationship between technological development, income satisfaction, stable job creation, and the need to try new developments. However, there seems to be an invisible boundary to development in the examined field, which may stem from uncertainty, and yet, it is important to maintain development and learning activity so that the right knowledge and know-how is available to the business when needed. Since the results show that there is a lack of openness to new technologies among the farmers in the studied region, and this may pose a problem in the future in terms of meeting the expectations of precision farming, it is recommended to focus on innovation in the agricultural sector in Hungary.
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Precision crop production and artificial intelligence – the future of sustainable agriculture
47-58Views:452According to Kay et al. (2004, in Shockley et al., 2017), there are seven steps to the decision-making process: 1) Identify the problem or opportunity, 2) Identify the alternative solution, 3) Collect all data and information, 4) Analyse the alternatives and make a decision, 5) Implement the decision, 6) Monitor the results of the decision, 7) Accept responsibility for the decision. The basic question is what kind of tasks we can perform in the decision-making process and what to leave for Artificial Intelligence (AI).
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Spatially Continuous GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.)
56-61Views:151The homogeneity of a study area of 20x20 m used for beetroot production in North-West Hungary was analysed with geo-statistical methods on the basis of measured plant and soil parameters. Based on variogram calculations (Equation 1 and 2), the yield surface showed homogeneity in North-South direction. Considering the results, decrease of sampling distance to 17 m can be suggested. The direction of the variability of yield (Figure 1) could be modelled with a direction variogram based on analysis of the variogram surface. In the study, developed methodological processes are presented for the analysis of spatial relationship between measured production and soil parameters. 5 spatial evaluation methods for yield surface were compared (Table 1). On the basis of the analysed methods, it can be stated that different methods (LP, RBF) should be used when the reasons for locally extreme yields are in focus than in case when the yield surface of the whole area is estimated (IDW, GP). Using adequate parameters the kriging method is applicable for both functions. Similarly to the results of an ordinary Pearson correlation analysis, spatial correlation analysis was shown using soil pH and Cu concentration data. The results of cross variogram analysis (Equation 2) and the North-South direction of the variogram surface showed negative correlation (Figure 3). Based on simulation calculations, decrease of 30% in sampling points resulted in increase of 12% in error for the total sample number considering Cu concentration. The method provides a tool to decrease the cost of sampling and sample analyses of spatially correlating features, and to increase the reliability of spatial estimation using a better sampling strategy with the same sample number.