Ruzica Stricevic

Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

Application of the AquaCrop model to simulate the biomass of Miscanthus x giganteus under different nutrient supply conditions

There are conflicting opinions about the need to fertilize Miscanthus and, also, the question has been raised whether Miscanthus should be irrigated, especially if water resources are limited. Crop growth modeling can help answer such questions. In this article the FAO AquaCrop water‐driven model was selected to simulate Miscanthus biomass under different nutrient and water supply conditions. The article reports the outcomes of 6‐year experiments with Miscanthus on two locations in Serbia: Zemun, where three fertilizer treatments were applied (Nl – 100 kg ha−1, Nopt 50 kg ha−1 and Nf nonfertilized), and Ralja, where only Nl 100 kg ha−1 was applied. Model calibration focused on the measured data (root depth, crop phenology, and the above‐ground biomass by year of growth. Calibration results showed a very good match between measured and simulated values. The largest and only significant difference was noted in 2008, when the crop was establishing and exhibited uneven radication. The simulation results for the next 5 years showed a variance from −4 to 5.7%, believed to be a very good match. A high coefficient of determination (R2 = 0.995) and high Willmott index of agreement (0.998) were also indicative of a good match between simulated and recorded biomass yields. The measured and simulated results for validated datasets at both locations were good. The average RMSE was 2.89 Mg ha−1; when compared to the deviations noted at the test site itself, it was apparent that they were smaller in all the years of research except the first year. The index of agreement was 0.97 and the coefficient of determination R2 0.947. The AquaCrop model can be used with a high degree of reliability in strategic planning of Miscanthus cultivation in new areas, under different nutrient and water supply and local weather and soil conditions.

Assessment of AquaCrop model in the simulation of seed yield and biomass of Italian ryegrass

ABSTRACT Given that the optimal sowing rate and inter-row spacing of Italian ryegrass raised for seed have not been determined, the objective of this research was to assess the effect of crop density on biomass and seed yields under different climate conditions, applying the AquaCrop model. The data came from experiments conducted under moderate continental climate conditions at Stitar (Serbia) and Mediterranean climate conditions at Cukurova (Turkey). At Stitar, there were three different inter-row spacings (high (Sd), medium (Sm) and low (Sw) crop densities), while at Cukurova there was only high crop density (Sn). In the calibration process, the initial canopy cover, canopy expansion and maximal canopy cover were adapted to each crop density, while the other conservative parameters were adjusted to correspond to all climate conditions. Calibration results showed a very good match between measured and simulated seed yields; the values of the coefficient of determination (0.922). The biomass simulation was very good for Cukurova (R2 = 0.97), but somewhat poorer for Stitar (R2 = 0.72). Other statistical indicators were high such as Willmott index of agreement of both the calibrated and validated data sets, for both study areas >0.916 and normalized root mean square error in the range from 9–18%. The AquaCrop model was found to be more reliable for Italian ryegrass biomass and seed yield predictions under mild winter climate conditions, with adequate water supply, compared with moderate climate and water shortage conditions.

Application of Kraijenhoff Van de Leur - Maasland’s method in drainage

The aim of this work is to show some properties of the application of Kraijenhoff Van de Leur-Maasland’s method for drain spacing determination in unsteady state of flow. The analysis of the method is based on data obtained from drainage field with 10 m of drain spacing which dries out eugley soil. The results of analysis show the range of method applicability as well as certain limitations in the case of non-modelled dynamics of ground water recharges.