Eutrophication is one of the biggest environmental problems facing wetlands. However, its effect on soil functioning is not yet well understood. We tested the hypothesis that increased nutrient loading into wet grassland ecosystems accelerates soil C and N cycles and decreases microbial immobilization of C and N. Experimental sites were established on two wet grasslands, with either mineral or peaty soils, and fertilized by NPK fertilizer for 3 years. Soils were analyzed for soluble and microbial C and N contents and their transformations, profile of phospholipid fatty acids and number of nirK denitrifiers. Fertilization affected C more than N transformations. Opposite to what was predicted, decomposition was retarded, the soil C cycle was based more on labile C compounds, and the soil was more susceptible to C losses in fertilized versus unfertilized treatments in both soils. Fertilization resulted in lower microbial biomass C and microbial C immobilization and also decreased the activity of lignin-degrading enzymes. Shifts in the composition of the microbial communities led to decreased (1) decomposition of complex organic compounds and (2) immobilization of transformed C. Net nitrification and microbial N immobilization tended to increase in fertilized treatments indicating an acceleration of soil N cycling and losses, but only in the more vulnerable organic soil.
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.
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