This paper presents the standardized precipitation evapotranspiration index (SPEI)-based approach to agricultural drought monitoring (ADM-SPEI approach) combining well-known methods, expert’ critical opinions, and local agro-climatic specificities. The proposed approach has been described in detail in three phases. This allows its application in any region and modification according to different agro-climatic conditions. The application of the ADM-SPEI approach has resulted in obtaining a modified SPEI for different crops (agricultural drought SPEI (AD-SPEIcrop)) in the Vojvodina region. In the first phase of the proposed approach, analytical hierarchy process (AHP) was used to obtain an experts’ group decision regarding the most suitable method for calculating evapotranspiration for a particular analyzed region. In the second phase, SPEI was modified and adjusted to the conditions in Vojvodina, where ET0 was replaced by ETc. In the validation phase, the results of the application of AD-SPEIcrop were compared to crop yields and well-known indices and evaluated by the experts’ feedback. The statistically significant correlations were achieved between AD-SPEIcrop and crop yields. The highest correlations were achieved in the months when the analyzed crops were in the developmental stages when they are most sensitive to drought. The AD-SPEIcrop better correlates to the crop yields compared to SPEI. The comparison of AD-SPEIcrop to the standardized precipitation index (SPI), SPEI, and self-calibrated Palmer drought severity index (SC-PDSI) shows that it can successfully detect dry and wet periods. The results have indicated that the proposed approach can be successfully applied, and AD-SPEIcrop has shown a good performance for agricultural drought monitoring.

In the last decade, small unmanned aerial vehicles (UAVs/drones) have become increasingly popular in the airborne observation of large areas for many purposes, such as the monitoring of agricultural areas, the tracking of wild animals in their natural habitats, and the counting of livestock. Coupled with deep learning, they allow for automatic image processing and recognition. The aim of this work was to detect and count the deer population in northwestern Serbia from such images using deep neural networks, a tedious process that otherwise requires a lot of time and effort. In this paper, we present and compare the performance of several state-of-the-art network architectures, trained on a manually annotated set of images, and use it to predict the presence of objects in the rest of the dataset. We implemented three versions of the You Only Look Once (YOLO) architecture and a Single Shot Multibox Detector (SSD) to detect deer in a dense forest environment and measured their performance based on mean average precision (mAP), precision, recall, and F1 score. Moreover, we also evaluated the models based on their real-time performance. The results showed that the selected models were able to detect deer with a mean average precision of up to 70.45% and a confidence score of up to a 99%. The highest precision was achieved by the fourth version of YOLO with 86%, as well as the highest recall value of 75%. Its compressed version achieved slightly lower results, with 83% mAP in its best case, but it demonstrated four times better real-time performance. The counting function was applied on the best-performing models, providing us with the exact distribution of deer over all images. Yolov4 obtained an error of 8.3% in counting, while Yolov4-tiny mistook 12 deer, which accounted for an error of 7.1%.

Supplier selection is a complex task which assumes decision making in presence of many conflict ing criteria and various parameters. If there are more than one decision maker, the problem shifts into a group context and it requires proper approach in mediating the decision making process and use of supporting multi criteria methods and tools. This paper proposes group decision making approach for supplier selection based on analytic hierarchy process (AHP) that is combined with consensus convergence model, and two vot ing methods, non preferential approval voting and preferential Borda count. Proposed approach utilized strengths of these methods thus enabling their adaption to the specific decision problem of supplier selection. An example of selecting a supplier of irrigation equipment in the company engaged in projecting, installing and maintenance of irrigation systems is used to explain and demonstrate how proposed approach can be implemented. Furthermore, this approach is viable as sufficiently general in supporting different selection processes in a field of water planning, management, and development and it can be adapted and applied on various group decision making problems.

In the light of climate changes and in order to achieve stable crop production, irrigation represents an inevitable measure. Apart from water quantity, water quality represents a matter of concern. The paper elaborates on the presence of iron and manganese, as the main factors of causing the clogging of irrigation systems. The examined well water samples were taken mainly from Serbia. Photometric methods were applied for determining iron and manganese, and sensors for pH and conductivity. The obtained values were later subjected to a classification for irrigation water and the well water samples were classified according to the given thresholds. Precise location and presentation of the obtained results were done using the Geographic information system. The research has shown that from the analysed well water, only in 6 samples iron concentrations were increased up to a level classified as “extreme restrictions,” 4 samples as “warning,” while 31 samples of water were “adequate for irrigation.” Concerning manganese, in only one sample water was classified as “extreme restrictions,” in 14 as “warning” and in 26 as “adequate for irrigation.” pH and conductivity did not coincide with elevated concentrations of iron and manganese, but in the cases of exceeding thresholds, special attention should also be paid to these parameters.

Conserving clean and safe freshwater is a global challenge, with nitrogen (N) and phosphorus (P) as frequent limiting factors affecting water quality due to eutrophication. This paper provides a critical overview of the spatiotemporal variability in both nutrient concentrations and their total mass ratio (TN:TP) in the canal network of the Hydro system Danube–Tisza–Danube at 21 measuring locations monitored by the Environmental Protection Agency of the Republic of Serbia over a length of almost 1000 km, collected once a month during the last decade. A spatiotemporal variation in nutrient concentrations in the tested surface water samples was confirmed by correlations and cluster analyses. The highest TN concentrations were found in winter and early spring (non-vegetation season), and the highest TP concentrations in the middle of the year (vegetation season). The TN:TP mass ratio as an indicator of the eutrophication pointed out N and P co-limitation (TN:TP 8–24) in 64% of samples, N limitation (TN:TP < 8) was detected in 27% and P limitation (TN:TP > 24) in the remaining 9% of water samples. Such observations indicate slow-flowing, lowland water courses exposed to the effects of non-point and point contamination sources as nutrient runoff from the surrounding farmlands and/or urban and industrial zones, but further investigation is needed for clarification. These results are an important starting point for reducing N and P runoff loads and controlling source pollution to improve water quality and underpin recovery from eutrophication in the studied watershed.