Abstract Military activity in Ukraine has caused extensive soil disturbance, yet the spatial structure of contamination under active hostilities remains poorly understood. This study assesses militaryaffected agricultural soils in the Kherson region using 175 georeferenced pXRF measurements, geostatistical modelling (RBF, Kriging), and integral pollution indices to quantify the distribution of toxic metals (As, Pb, Zn, Cu, Ni, Cr, V, Mn) and indicator elements (Fe, Ti, Ba, Zr, Sr, Rb, Th).Contamination exhibits a heterogeneous, focal mosaic pattern with a spatial dependence range of 54-83 m, indicating localized, event-based inputs. Pb, Zn, and Cu form high-contrast hotspots associated with destroyed equipment, while other elements reflect background variability or mixed sources. Indicator elements confirm lithogenic control and provide a baseline for separating background conditions from localized contamination.Although 87.8% of the area is lightly contaminated, 2.2% forms high-intensity hotspots that concentrate environmental and sanitary risk.These results show that military soil contamination is structurally localized and cannot be captured by averaged or coarse-scale assessment. Monitoring and management should therefore operate below the 54-83 m scale and prioritize hotspot detection to identify and control risk in waraffected agricultural land.

The article presents the results of theoretical and experimental research into the adhesive properties of flax fiber and their impact on the scientific development of composite material production. The research established that combining natural fibers with a polymer material or matrix increases the complexity of the composite forming process and causes problems in the physicochemical processes of matrix–filler interaction. This is explained by the low wettability of flax bast (13.0–14.5 g). It was found that the presence of cutins on oil flax fibers determines their high degree of hydrophobicity. To improve the adhesive properties of the bast, it was chemically treated to remove cellulose companions and cutins, high-molecular-weight compounds. The bast was chemically treated using the oxidative method. After chemical treatment, a fiber enriched with cellulose and freed from waxy substances was obtained. Thus, the cellulose content increased from 47.67–53.33% to 90.01–97.68%, and the waxy substances were almost completely removed. Their content in the bast was 18.13–18.57%, but after chemical treatment, it decreased to 0.01–0.04%. After chemical treatment, the wettability of the fiber increased to the required levels −104.94–122.78 g, indicating that the adhesive properties were significantly improved. The results of studies on physical and mechanical indicators demonstrate the high quality of the obtained composites. In terms of fluidity, all samples were superior to the control sample reinforced with cotton fiber. The theoretical and experimental research enabled the collection of experimental samples of composite materials.

Efficient irrigation water management is critical for sustainable crop production, particularly under the challenges of climate change. This study introduces WaterQ AI, a Python-based, rule-driven chatbot that automates comprehensive irrigation water quality assessments. It analyzes key agronomic indicators – TDS, SAR, SSP, KR, and PI – while incorporating soil parameters and aligning with both Ukrainian national standards and FAO international guidelines. Designed with a user-friendly interface, WaterQ AI outputs detailed assessments with traceable references to ensure transparency and user trust. To validate its accuracy, WaterQ AI was tested on real water samples and benchmarked against expert manual assessments. For example, for Sample 1, the manual SAR value was 1.09, and WaterQ AI returned 1.09; manual TDS was 560, and WaterQ AI calculated 559.48. Across all indicators and samples, the deviation between WaterQ AI and expert analysis was consistently below 0.5%, confirming its reliability. In contrast to existing tools, WaterQ AI uniquely combines rule-based decision logic, multi-standard compliance, and soil-specific adaptation in a conversational format. This tool significantly reduces the time and expertise required for accurate water quality assessment, providing practical value to farmers, agronomists, and researchers striving for sustainable irrigation practices.

The aim of this study was to provide evidence on how Ukrainian agrarian feel about the problem of transgenic crops research and practical use both for industrial and food purposes. This is a survey-based study, accompanied with statistical analysis of the survey outcomes and. Artificial neural network-based prediction of the attitude depending on religious beliefs. Most of the respondents were Christians (83.13%), while the least number of representatives were among Islam and Buddhism religions (1.20% each, respectively). The highest general positivity towards GMOtechnologies was recorded for Buddhism and atheistic respondents. The highest negativity towards transgenic crops was recorded for Christians. To some extent, legislative persecution also impacted attitude towards GMO-crops. High data asymmetry was identified among the datasets. The general predictive accuracy of the developed artificial neural network reached 58.82% with the F1 score of 0.31, indicating moderately low accuracy of the prediction. The specificity of the prediction was very high - 89%. Further studies will be carried out to enlarge the initial dataset and make it more comprehensive in terms of diminishing data asymmetry.

Artificial Neural Networks (ANNs) are a widely used and powerful tool for modeling and predicting various processes across society, industry, and nature. In recent decades, ANNs have found increasing application in agricultural sciences, particularly in crop yield prediction. Despite their efficiency in solving such tasks, ANNs are known for their high computational demands. Most ANNs are developed using Python—a high-level, dynamically typed, interpreted language—which often results in less efficient use of computational resources compared to low-level programming languages. The primary objective of this study is to quantify the execution time gap between C-based and Python-based implementations of a simple feedforward ANN used for soybean yield prediction, utilizing Normalized Difference Vegetation Index (NDVI) values as input. Additionally, the study aims to evaluate and compare the predictive performance of both implementations. The results indicate no statistically significant difference in the accuracy of crop yield predictions between the C and Python models. However, a substantial difference in execution time was observed: the C-based ANN was found to be 21.1 times faster than the Python-based version. In addition, the C-based ANN saved CPU power, diminishing CPU usage 3.8 times. Therefore, we recommend using C for deep learning tasks in scenarios where optimal performance under limited computational resources is critical. Conversely, Python remains a suitable and preferable choice when ease of development is prioritized, and no substantial computational load is expected.

As a member of the UN, Ukraine has joined the global process of ensuring sustainable development and fulfilling 17 Sustainable Development Goals and 169 tasks. One of the main aspects of fulfilling these tasks is the increase in the production of food protein of animal and plant origin. Unfortunately, it is not possible to rapidly increase the amount of animal protein, but it is possible to create a model of constant growth due to the cultivation of legumes, which are similar to animal protein. In this sense, the most suitable crops are chickpeas, grasspea, lentils, and beans. In the south of Ukraine, the main limiting factor in increasing the productivity of agricultural plants is the amount of moisture, its uneven distribution during the growing season, and critically high air temperatures. The specified conditions make the constant search for technological solutions to level the unfavorable conditions of the external environment difficult. Also, a method of overcoming stressful situations is the use of plant growth and development stimulants on leguminous crops. The specified environmental factors and technological ways of solving them in the conditions of the Southern Steppe of Ukraine are extremely relevant and timely, and they will be presented in the scientific work.

The chapter outlines strategic approaches to the post-war restoration of irrigated agriculture in the Southern Steppe of Ukraine, with a focus on the Kherson region, which has been severely impacted by military operations. The region has faced significant anthropogenic damage, including military degradation of soil cover, destruction of the Kakhovka Dam and Reservoir, looting of reclamation systems, and loss of fertile soil layers. The chapter proposes a comprehensive set of ecological and remedial measures, including agronomic, remedial, and technical interventions, to restore the irrigated agriculture system. One of the keys is the restoration of hydro-technical structures such as the Kakhovka Hydro Power Plant in a revised framework. This entails an evaluation of the infrastructure and the implementation of necessary upgrades or modifications to ensure reliable functioning in the post-war context. The chapter emphasizes the importance of integrating ecological considerations into restoration efforts, such as soil conservation practices and the protection of natural habitats.

The purpose of the article was to describe the outcomes of an in-field evaluation of the NDVI Converter application, which was created to assess the normalized difference vegetation index (NDVI) using estimates of the percentage of green canopy cover. The study was carried out in 2023 on fields of winter wheat, winter barley, and winter oilseed rape in the phenological stages BBCH 21-32 and BBCH 18-39, respectively. The fraction of green canopy cover was estimated using the application Canopeo. By comparing the mean absolute percentage error and Pearson's correlation coefficient to the actual values of the spatial vegetation index, as determined by the platform OneSoil, the quality of the vegetation index evaluation was assessed. Thus, it has been established that NDVI Converter offers accurate vegetation index assessment for cereal crops, with mean absolute percentage error 16.23% and Pearson's correlation coefficient within 0.99, and reasonable quality for oilseed rape (statistical indicators of 46.61% and 0.99, respectively). After the adjustment, the accuracy score of the NDVI Converter increased up to 27.62% for oilseed rape, and 8.71% for winter cereals. Therefore, NDVI Converter could be recommended for practical use in case the spatial vegetation index is not provided by satellite imagery services.

The cereal industry in the aspect of ensuring the food security of the country, is a strategically important component of the grain food market, but requires solving a number of important problems in achieving sustainable development, increasing production volumes and increasing export potential on the world market. The production of cereals remains a problem industry, as demonstrated by the significant fluctuation and high level of prices for the main types of cereals. Mixed research methods were used in the analysis of cereal crop market data. The theoretical and methodological basis of the study was a dialectical method of cognition, a systematic approach to the use of general provisions of economic theory. Conducted studies of cereal crop cultivation in Ukraine evidenced that gross yield of buckwheat increases on an intensive basis - with a decrease of 11.3 per cent, an increase in yield of 21 per cent; tends to decrease in gross millet yield (62.3 per cent) under the influence of reduced acreage and yields, rice production remains stable. Growing and processing of cereals are concentrated in the Eastern and central regions of Ukraine, and rice - in the Southern part. Increase the efficiency of cereal crop production in Ukraine can be achieved through the use of production reserves associated with the intensification and optimization of the organizational and economic mechanism of the industry.

The research is devoted to a comprehensive approach to the study of soybean productivity depending on the different tillage technologies and plants' spraying of salicylic acid of climate change in Southen Ukraine. The proposed measures will save water resources up to 30% through the use of portable moisture meters to control soil moisture and the no-till technology; increase the soybean yield by up to 14% by spraying plants of salicylic acid. It was determined that the density of soybean plants during the growing season decreased on variants with traditional technologies in comparison with no-till the deviation makes almost 6%. The soybean water consumption coefficient in the experiment varied significantly in the range from 1,710 to 2,330 m(3)/t. Soybean plants used water reserves most rationally in variants where traditional technologies were used. The average increase in yield according to the experiment under traditional technologies was 5.4%, with no-till was 20.3%. In terms of economic and bioenergy efficiency, no-till does not have significant advantages over traditional technologies. Reduction of costs in the introduction of no-till for energy consumption of machinery, fuel, electricity is fully offset by the growth of indirect energy costs, in particular, the cost of herbicides, due to high weed infestation of uncultivated crops.

In the context of climate change, there is a growing need to study the impact of environmental factors on plant life. This, in turn, will allow the reasonable use of agricultural techniques, to form high-yielding crops, and increase crop productivity. The following variants of tillage and irrigation technologies were included in the research scheme (i) conventional tillage without irrigation; (ii) no-till without irrigation; (iii) no-till with drip irrigation system; (iv) no-till with system of subsoil drip irrigation. Corn yield varied significantly from the studied factors of cultivation technology. The highest productivity of 12.1 t/ha was obtained under no-till with a system of subsurface drip irrigation and 12.0 t/ha under no-till with drip irrigation system. During the statistical processing of the obtained experimental data, no significant difference between the studied variants was found. Carrying out an economic analysis of corn growing in the arid climate of the Southern Steppe of Ukraine testifies to the high efficiency of application of the new tillage and irrigation systems in the cultivation technology. The application of no-till with drip irrigation system allowed to obtain grain with a cost of 3.72 UAH/t, to obtain a profit of 50.13 UAH/ha with a level of production profitability of 112.2%. It should be noted that the use of no-till with system of subsoil drip irrigation led to slightly lower indicators of economic efficiency of corn cultivation, under which the level of production profitability was 109.0%.

The scientific paper presents the experimental data obtained in the course of the environmental and reclamation monitoring of the hydro-geological and reclamation condition of irrigated soils in the southern steppe of Ukraine, their chemical composition is determined. On the basis of the analysis of water and salt regime of the soils in the areas under study for reclamation and water supply construction, on the basis of many years’ research, the study suggests the method for determining agrolandscape typicality considering the properties of soil chemical composition. The basis of this method is the following principle: the confidence intervals of neighboring regression lines coincide (overlap) on a certain interval of values, so they are typical on this interval of values. The study establishes that according to this method the landscapes of Kherson Prysyvashshia and Askania steppes are typical by the specificity of the formation of soil chemical composition by 83.5% that makes it possible to compare the research results obtained on both landscapes and proves the typicality of the field experiment.

Currently horizontal drainage is a key ecological reclamation measure aimed at regulating water and salt regimes of soils, especially under conditions of irrigated agro-landscapes. Taking this measure becomes especially important under conditions of global and regional climate change. The operating conditions for horizontal drainage have changed resulting in changes in its efficiency. It is especially important for durable drainage, when its efficiency decreases it could even stop functioning. It requires a considerable number of research activities, but very few Ukrainian farmers can carry out monitoring research on the performance of horizontal drainage, constructed on their plots. It prevents from finding out negative aspects in horizontal drainage in time and taking appropriate measures. These difficulties can be avoided in case of using the developed and approbated methodology of recovering a number of horizontal drainage expenses through expenses of the research plots, determining the performance of horizontal drainage by these results and developing appropriate ecological reclamation measures to increase productivity of irrigated drained agro-landscapes.

The article presents the results of many years of field research of the Bromus inermis productivity with different feed .systems. The studies were conducted during 2011-2014 on gray forest soils. The following factors and their variants were included in the research scheme: the rate of mineral fertilizers and the time of foliar feeding with water-soluble fertilizer Master 18.18.18+3 at a dose of 5 kg/ha. Mars and Vseslav varieties were grown during the experiments. The experimental data from Bromus inermis were processed by the standard ANOVA procedure within MS Excel software. The most effective system of Bromus inermis fertilizer is the combination of mineral fertilizers N60P45K45 with the application of water-soluble fertilizer Master in the tillering stage (1-2 ten-days periods of April) and in the heading stage (1-2 ten-days periods of May) at a dose of 5 kg/ha, which, during the years of experiments in average, provided 355 kg/ha of the seeds yield of the Mars variety and 370 kg/ha of the Vseslav variety, that is 55 and 60 kg/ha more than with the use of mineral fertilizers and 225 and 245 kg/ha more than in variant without fertilizers.

The paper presents the results of the research examining the effect of seed treatment with plant growth regulators and seeding rates on the productivity of hard winter wheat varieties. The research was conducted during 2016-2019 in the southern black soil. The research scheme included the following factors and their variants: the varieties Dnipriana, Kassiopeia and Kreiser; the seed treatment with a plant growth regulator – without using it (water seed treatment), Kvadrostym and Nertus PlantaPeh; the seeding rate – 3, 4, 5 and 6 million seeds per hectare. The experimental data were processed by the standard ANOVA procedure within MS Excel software. In order to obtain the grain yield of hard winter wheat at the level of 4.72–4.86 t/ha with high indexes of growth capacity, laboratory and field germination capacity in nonirrigated conditions of the Southern Steppe of Ukraine it is recommended that the varieties Kassiopeia and Kreiser should be grown with the seeding rate of 5 million seeds per hectare and the seed treatment with the plant growth regulator Kvadrostym at the rate of 0.5 kg/t. Keywords— plant growth regulator, productivity, quality, seeding rate, Triticum durum, variety

The work is devoted to the development of an early grain yield prediction model for winter cereals (wheat and barley) for Kherson oblast by the values of spatial indices (normalized differential vegetation index NDVI and enhanced vegetation index EVI), calculated on the base of satellite data. For this purpose, we conducted a regression analysis of the data to determine the correlation between the yields of the above-mentioned crops by years (for the period of 2012-2019) with the values of the vegetation indices by the months of the studied period. The region-averaged vegetation indices were calculated based on a smoothed 250-m MODIS Terrain NDVI and MODIS Terrain EVI imaginary series using GDAL QGIS 3.10 raster analysis toolkit, excluding vegetation-free zones. Based on the results of the work, models of early (30 - 45 days before the start of mass harvesting) high-precision winter wheat and barley grain yields according to the regional NDVI and EVI are proposed. The novelty of the research is in the development of a convenient and high-precision forecasting tool for the identification of the risks connected with food shortage and planning a strategy for the region's export capabilities and calculation of the level of local grain supply.

Modern cultivation technologies should provide resource and energy saving. There is an evident tendency to energy and climate smart agriculture strengthening because of the necessity of natural and energy resource saving. However, very little attention is paid to the questions of scientific substantiation of energy-saving in agriculture at the expense of crops cultivation technologies optimization Therefore, we consider the subject of energy expenditures optimization at sweet corn production an actual one for modern Ukrainian and international agrarian science, especially because there is a lack of information in modern literature on this question. We studied different options of tillage depth, fertilization and plants density within for sweet corn grown in the drip-irrigated conditions of the semi-arid zone of the South of Ukraine. The study was performed during 2014-2016 in four replications, and provided for the following factors: plowing depth (20-22, 28-30 cm). fertilization doses (no fertilizers, NP 60 and NP 120 kg/ha of active substance applied), plants density (35, 50, 65, 80 thousands of plants/ha). The results of the study proved significant difference in the crop productivity and energy efficiency of the agrotechnology due to the changes in the studied parameters. The best productivity of 10.93 t/ha of marketable ears combined with the highest energy efficiency of the crop cultivation with the coefficient of energy efficiency of 2.44 were provided by the complex with plowing at the depth of 20-22 cm, fertilization dose of NP 120 kg/ha of active substance, 65 thousands of plants/ha.

The scientific paper presents the experimental data on the impact of different cultivation technologies on the indexes of assimilation surface area, chlorophyll content, its fractional composition (fractions "A" and "B") and the yields of sunflower hybrids under conditions of the Southern Ukrainian Steppe. The research was conducted in 2018-2019 on dark-chestnut soil in the semi-arid conditions. The research scheme consisted of the following factors and their variants: sunflower hybrids (PR64F66, Tunca); cultivation technologies (intensive; organic). The research results proved that the best conditions for the formation of the largest area of assimilation apparatus by sunflower plants were created in the organic technology (treatment of soil + seeds + plants with organic preparations during the growing season) when growing the hybrid Tunca: at the stage of 3 pairs of true leaves - 2.9; the formation of heads - 27.1; flowering - 37.1 and milky ripeness - 29.0 thousand m(2)/ha. Under the same conditions there was maximum amount of chlorophyll (9.71 mg per 1 g of dry weight) and enzymes. Sunflower cultivation under the organic technology caused the formation of maximum yield (PR64F66 F-1 - 2.42 and Tunca F-1 - 2.41 t/ha), realization of biological productivity (90.9 and 90.7%) and fat content (48.7 and 49.8%).

The paper presents the results of the study dedicated to the investigation of sweet corn leaf apparatus growth and development in dependence to the crop cultivation technology elements, viz. depth of plowing, mineral fertilizers application doses, plants density, in the drip-irrigated conditions of the South of Ukraine. The field trials were carried out during three years (2014-2016) by using the split-plot design method in four replications with accordance to the modern requirements of the experimental work in agronomy. The results of the study showed the significant effect of all the studied agro technological treatments on the crop leaf apparatus. The highest Leaf Area Index (LAI) that averaged to 3.72 was obtained under moldboard plowing at the depth of 20-22 cm, fertilization with N120P120, and plants density of 80000 ha(-1). The total amplitude of fluctuation of LAI averaged to 2.42. Increased plowing depth improved LAI of sweet corn only under the non-fertilized conditions, however, increased fertilizers doses and thickening of the crops positively affected the studied bio-metric index.

The water supply deficit requires agro-environmental rationale for the use of alternative water sources to feed agricultural crops, viz.: industrial wastes, municipal drains, farm animal waste, drainage and escape water of rice irrigation systems. We analyzed the quality of irrigation water from different sources, with regard to the content of cations, anions, water-soluble salts, power of hydrogen (рН), sodium adsorption ratio (SAR), etc. in it. In the course of the greenhouse trial, we diagnosed its impact on the indicator crop (maize) (Zea mays L.) with its herbage crop stage of 10 leaves, supplied with water of varying quality. We proved the viability of improved drainage and escape water from rice irrigation systems in irrigated agriculture, owing to which maize herbage was diminished, on average, by 5.82%. We verified the negative impact of irrigation water, which contains the effluent disposals of metallurgical production, on croppers – it had contributed to diminishing the watered maize herb, on average, by 39.27%. A correlation analysis of the test data proved the closely interrelated feedback between the maize herbage amount and the content of cations, anions and water-soluble salts in irrigation water (coefficient of correlation r varied between 0.88 and 0.98). The worked-out linear regressive model for maize herbage, based on the content of water-soluble salts in irrigation water, together with SAR index (Y=2342.71–1.82×x1+366.78×x2), affirmed the validity of the pattern, discovered by means of the correlation analysis.