A number of studies have been done to examine the factors that impact the level of foreign direct investment in African countries. However, most of them have not considered the effect corporate governance structures have on foreign direct investment (FDI) in their estimations. This research therefore pursued the investigation of the relationship between corporate governance structures at the national level and foreign direct investment concentrating mainly on West African economies for the period 2009–2018. The study constructed a panel, sampling annual data from 17 West African countries. The System generalized method of moments (GMM) was used in analyzing the panel data to attain the objective of the research. The results of the study reveal that countries characterized by greater protection of the interest of non-controlling parties are able to accumulate progressive FDIs. Economies with firms portraying high ethical values also generally generate increasing foreign direct investment, and the existence of effective boards also significantly improves the country’s FDI inflows. Finally, the findings report that the impact of regulations in securities and the stock exchange on FDI is insignificant. The study recommends that West African countries institute corporate governance structures purely independent of political influences in order to ensure effective utilization of foreign direct investment to mitigate poverty.

The paper deals with the analysis of selected profitability indicators of Czech agricultural businesses of legal entities and their subcomponents, based on DuPont analysis. The effect of asset turnover ratio, net profit margin ratio and equity multiplier on the value of return on equity (ROE) and return on assets (ROA) is studied in relation to the legal form and size group. The analysis of the effect of sub-indicators is performed by way of a correlation analysis. Furthermore, the assumption about the influence of sub-components indicators on synthetic profitability ratios is verified. The panel data set we used was obtained from the Amadeus database. The analytical section is based upon accounting statements of agricultural businesses of legal entities in the Czech Republic within the period of 2011-2015. The analysis is based on the calculated values of ROA and ROE, including partial values of the sub-components indicator, by using DuPont analysis. The differences between the groups of businesses are tested through correlation analysis and subsequently evaluated with respect to the specifics of each group. The object of examination was more than 3000 companies annually, on average. The evaluated companies were divided, in terms of legal form, into joint-stock companies, cooperatives and limited liability companies, and in terms of size structure into small, medium and large businesses.

Abstract:The main aim of the paper was a partial analysis of the production potential for pig fattening in the czech republic. This aim was achieved by econometric modelling of the production function, which was specified as a cobb-Douglas function, with the level of average daily increase as the dependent variable, and feed compounds, mortality and weight of new stock as independent variables. The model was specified as a fixed effect model, and the parameters of the function were estimated by the method of least squares dummy variable, based on the ordinary least squares method. Verification of the estimated model was based on a t-test, coefficient of determination, Wald test, autoregressive test, and test of normality distribution of residuals. Subsequently, the estimated function was analysed and significant determinants of production were identified. The behaviour of the production functions was analysed for the average and marginal productions. The functions were also illustrated in graphs of production surfaces, from which the maps of isoproduction functions were derived. The isoproduction functions were used for the final analysis of the potential for pork production. The analysis was based on panel data from 32 farms focused on pig fattening, collected by our own survey. The research indicated significant differences between the surveyed farms. it also declared the most important factor of final production to be, with 99% probability, the new stock weight. The second most important determinant of final production is the feed compound A3, which is used in the final stage of fattening. For maximized production, the farmer should focus on the weight of pigs coming into fattening, choose the biggest one, and introduce the use of the feed compound A3. The results in the submitted paper should also be used by farmers to evaluate their production activity, and to compare their actual output with the theoretical value enumerated by the production function.Key words: production function, maps of isoproduction functions, allocation of production factors, exchange rate, rational stage Supported by the Ministry of Education, Youth and Sports of the czech republic (Projects no. MSM 6046070906 -Economics of resources of czech agriculture and their efficient use in the context of multifunctional agri-food systems).

Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield.

The paper deals with an economic assessment of impacts of precision agriculture (PA) on crop production economy. Based on a questionnaire survey and a FADN agricultural product expense-to-revenue ratio survey, it analyses a set of agricultural businesses the structure of which essentially copies the composition of business forms in the Czech Republic's agricultural sector. The economic assessment applies economic analysis methods based on cost calculations and a calculation formula that considers the commodity and species production structure. Based on an analysis of a number of scientific studies, it determines specific cost savings and makes a quantification of the effect of precision agriculture techniques on costs. In all the production areas, the greatest effect caused by application of precision agriculture techniques was quantified for winter wheat. Conversely, the lowest financial effects are shown in the analysed production areas for spring wheat. We also identified differences in the cost savings between spring and winter barley; the greater savings occur for winter barley. Financial effects in the form of reduced production costs were also found for other analysed crops cultivated by the businesses studied. The financial savings for the pea plant are almost comparable to those for winter barley. The greatest financial savings were achieved for sugar beet.