In many countries, especially in Eastern Europe, there is much discussion about how grasslands are managed in order to prevent pollution of soils, rivers, lakes and to maintain and conserve wild flora and fauna. These objectives favor the maintenance of high biodiversity in High Natural Value (HNV) grasslands through their extensive use, with a low intake of organic fertilizers in line with EU CAP policies. In Romania, in the High Natural Value (HNV) grasslands, subsidies are allocated for "environmentally friendly practices", through the Agri-environment and climate program, but the amount of organic fertilizer is restricted up to 40 kg nitrogen active element/ha regardless of the type fertilizer) but also their administration. Currently, through remarkable advances in areas such as GIS and remote sensing, applications for agriculture provide complex information with high accuracy, but also the ability to provide predictions for various parameters. In this context, the paper aims to optimize the application of manure in HNV grasslands, in accordance with Agri-environment policies, by improving and adapting in practice the OneSoil application based on NDVI map generation from Sentinel satellite images. The validation of the "adjustments" brought to the application was done by field visits with the Phantome 4 UAV equipment. An experimental site was considered an HNV grassland located in Brădişoru de Jos (ATU Oraviţa) which has an area of 391 ha. In the study area the vegetation is mosaic, the grassland being fragmented by hedges, forest curtains and trees in clumps that will be excluded from fertilization. The usable area for fodder at the level of 2020 was 265.41 ha, but in the absence of cleaning works in the coming years, there is a risk of substantial reductions. The proposed optimization model is finally presented in the form of a “coverage” map that indicates both the required quantity, differentiated according to the vegetation characteristics suggested by NDVI, and the spatial location of these quantities on subzones. Thus, on an area of 60.73 ha, 4 t.ha-1 of manure are required, on 94.66 ha it is necessary to administer 5 t.ha-1 of manure, and on an area of 110.02 ha, where the vegetation of the grassland suffers, it is necessary to administer 8 t.ha-1 of cattle manure. The major advantage of this model is that the user can import, on different devices (phone, tablet or laptop) vector files with plot outline or GPS points for location, can differentiate the dose of fertilizer on the surface of the plot, under specific conditions and has at its disposal the map with the "location" of the different quantities of fertilizers.

Triticale is a cereal widely used lately due to its high production potential of both grains and biomass and its multiple uses in both animal feed (feed) and humans (flour, flakes, alcohol). The development of the livestock sector will also have the effect of increasing the areas cultivated with fodder plants. In addition to increasing the cultivated areas, a diversification of the assortment of crops with multiple uses is also desired. Increasing the production capacity per unit area, the study of several varieties (lines) of triticale (Triticosecale Witt.) And the selection of the most competitive contributing to the achievement of this objective.. The purpose of this research is to study varieties (lines) of triticale (Triticosecale Witt.) In terms of their production capacity (green mass) and adaptation to pedo-climatic conditions specific to the Banat Plain, in order to enrich the current range of forage plants for silo, given the ever-increasing needs for feed in the context of a growing demand for animal products. We chose for this study nine varieties (lines) of triticale (Triticosecale Witt.), Noted: TMV1, TMV2, TMV3, TMV4, TMV5, TMV6, TMV7, TMV8, TMV9, in order to evaluate them in terms of the main characters that contribute to the achievement green mass production for silage and selection of the most valuable genotypes for breeding. The green mass productions of the nine varieties of triticale were directly influenced by the values of the studied productivity traits. Regarding the yield per plant, the character, the height of the plants contributes to the greatest extent; the TMV7 triticale variety has the highest value in terms of this production character. From the analysis of the synthesis of mass productions in order to ensilage obtained by the studied triticale varieties, in 2018 - 2020, it is found that the best production results, in the conditions of Timisoara were recorded by TMV7 registering an average production of 44.53 t.ha-1 green mass. Based on the results recorded, under the given conditions, the TMV7 triticale line is classified for future improvement programs.

The study evaluated the fertility and spatial variability of some agricultural lands, in the area of Cenei locality, Timis county, Romania. The soil samples, representative for the studied agricultural lands, were taken from nine different locations. The soil reaction (pH, H2O), total nitrogen content (Nt), mobile potassium content (K, ppm) were considered and studied, and the degree of saturation in basic cations (V,%) was calculated. The soil reaction showed values between pH = 6.29 - 7.97 ±0.20, the degree of saturation in basic cations (V,%) had values between V = 74.80 - 100.00 ±3.13%, total nitrogen showed values between Nt = 0.11 - 0.35 ±0.03%, and the potassium content showed values between K = 275 - 390 ±15.49 ppm. High variability was recorded in the Nt index values (CVNt = 38.3598) and low variability in the soil reaction (CVpH = 8.3248). For the other soil indices studied, intermediate values were recorded, respectively CVV = 9.9760 for the degree of saturation in basic cations (V%), and CVK = 13.7177 in the case of mobile potassium content (Kmobil). The variation of the mobile K content in relation to the soil reaction (pH) was described by a polynomial equation of degree 2, equation (1), in statistical safety conditions, at the level of R2 = 0.779, p = 0.0121. Under PCA, PC1 explained 69.593% of variance, and PC2 explained 25.919% of variance. Cluster analysis facilitated the grouping of soil samples into two separate clusters, with three sub-clusters, under Coph. coeff. = 0.859.