A decision support system for field vegetable fertilization

    Mechanization in agriculture & Conserving of the resources, Vol. 66 (2020), Issue 3, pg(s) 103-107

    Decision Support System (DSS) is a computer-supported interactive system, i.e., a software product to assist decision-making at any level of management, with an emphasis on making a directly applicable decision. The purpose of this software for the application in the process of fertilization is to organize and classify data, transform information, and choose how to make decisions and embody them. In its sophisticated form, it is an interactive computer program that uses and integrates a simulation model, a database and a decision model for optimal crop fertilization with different fertilizers. Fertilization recommendations in most countries around the world are based on years of fertilization experiments. When using DSS, the user must enter 3 data groups: 1) the type of vegetable and the planned yield; 2) results of agrochemical soil analysis (soil pH, soil organic matter content, available phosphorus and potassium, mineral nitrogen); 3) plans for organic fertilization and available fertilizer data. The flowchart of DSS main part consists of 12 steps: 1) determination of target yield 2) calculating the required amount of nutrient for the target yield; 3) calculating the optimal need of N in fertilization 4) calculating the optimal need of P and K in fertilization 5) determination of optimal organic fertilization; 6) calculation of the liming needed; 7) optimal distribution of N 8) optimal distribution of P and K with respect to fertilization dynamics; 9) the need and plan for application of micronutrients; 10) choosing the optimal form of nutrient and calculating the amount of optimum fertilizers (single and complex) 11) calculation of the nutrient balance 12) calculating the economic effect of vegetable fertilization and growing. DSS groups results into several sets of data: 1) interpretation of the results of agrochemical properties and soil fertility; 2) recommendation of the quantity of N, P, K and types of fertilizers (required quantities of primary nutrients in mineral fertilization of vegetables, types and quantities of mineral fertilizers required for the mentioned fertilization); 3) balance of primary nutrients, tips and warnings (optimal formulation of fertilizers, the balance of planned mineral fertilization, needs of fertilization with microelements). After receiving the results and guidance, the user simply obtains new output values according to the changes made by simply changing the input data (e.g., to plan a lower yield, other formulation fertilizer, cheaper fertilizer). This mode of operation enables rapid multiple comparisons of the required fertilization with different available fertilizers, on different production sites, and for different target yields and economic effects of vegetable cultivation.


    Modelling Fe, Zn and Mn availability in soils of eastern Croatia

    Mechanization in agriculture & Conserving of the resources, Vol. 66 (2020), Issue 2, pg(s) 77-80

    Iron (Fe), zinc (Zn) and manganese (Mn) are essential microelements with plant available fraction in soil, depending significantly on soil pH and soil organic matter (SOM), which is important for crop growth. The aim of this paper is to present the potential of mathematical models in order to predict the availability of microelements (Fe, Zn, Mn) in acidic and alkaline soils of eastern Croatia. The fundamental database for availability prediction contains results of 22,616 soil samples from eastern Croatia representing an area of 88,714.46 ha of arable land. The mandatory results include soil pH, SOM, available P and K, hydrolytic acidity, and carbonate content. Additional data sets, including supplementary results of total (extracted by aqua regia, AR) and available (extracted by ethylenediaminetetraacetate, EDTA) micronutrient fraction, were used for modelling of micronutrient availability and for final model validation. The modelling micronutrient available fraction was created in 3 steps: (1) regression models of total (AR) and available (EDTA) micronutrients (Fe, Zn, Mn) concentration based on analytical results of soil pH, SOM, AR and EDTA micronutrients fractions; (2) prediction of the available micronutrients fraction (EDTA) based on the soil pH and SOM; (3) model validation using new data set with analytical results of soil pH, SOM, AR and EDTA. The model predicts that moderate micronutrients availability could be expected on 48.45 % (42,972.25 out of 88,714.46 ha) of arable land on average for Fe, Zn and Mn. A high availability could be on 29,32 % (25.982 ha) of arable land on average, but a very significant difference was found among Fe (47,37 %), Mn (39,01 %) and Zn (1,57 %) arable land with high availability. The most important prediction is the one that claims insufficient availability of micronutrient could be expected on 19,579.87 ha in average, what is 22.26 % of arable land. But low Fe availability was predicted on only 2.79 % (2,479,3 ha), significantly more land (22.60 %, 20,035.40 ha) with low Mn availability and the highest percentage (41,4 %) of soil with insufficient Zn availability (36,764.91 out of 88,714.46 ha). The validation shows the highest model accuracy for Zn and the lowest for Fe availability prediction