The necessity to optimize feed crop cultivation in Kazakhstan's steppe zone is underscored by evolving climatic conditions and sustainable agriculture demands. This study, conducted from 2021 to 2023 in the Akmola region, evaluated the nutritional value and production efficiency of annual and perennial grass mixtures. A randomized complete block design was utilized for annual grasses, while a sequential scheme was applied for perennial grasses, each with three replications per plot. Statistical data processing was employed to analyze the outcomes. Results indicated that mixed-feed crops exhibited superior nutritional composition and energy value. Specific combinations of annual grasses, such as oats with peas or oats, peas, and vetch, alongside multicomponent mixtures incorporating legumes for perennial grasses, demonstrated optimal results. The ideal harvest timings for these grass mixtures were also established. It is recommended to cultivate combinations like Sudan grass with peas and vetch, or oats with peas and vetch for green feed, and for hay and pasture, combinations of red fescue, bluegrass, wheatgrass, and alfalfa, as well as red fescue, bluegrass, brome, sainfoin, and alfalfa. This research emphasizes the importance of diverse crop mixtures to enhance feed nutritional value, thereby contributing to sustainable agricultural practices, food security, and environmental resilience amid climate change.

In this study, the changes in yield, nutrient content, and amino acid levels in legume–cereal grass mixtures were qualitatively evaluated depending on the legume–cereal combination and inoculation with preparations based on Rhizobium. This study, taking into account the biological characteristics of legume forage crops, used inoculations with strains of nodule bacteria and associative nitrogen fixers to enhance the process of the nitrogen fixation of mixed crops of legumes and cereal. The aim of this study was to compare the yields and nutritional values of monocultures and mixed crops, as well as to determine the effects of preparations based on strains of nodule bacteria and the associated nitrogen fixer on the photosynthetic activity and yield of combined annual legume–grain crops. A comparative study of forage crop biomass was conducted to analyze crude protein, fiber, carotene, and amino acid content in monocultures and legume–cereal mixtures, with and without the use of nodule-bacteria-based preparations (Rhizotorphin, Mizorine, Flavobactrin, and Azolene). The combined effect of crop mixtures and biological products led to increased green mass yield, protein content, and feed productivity. Notably, two-component mixtures with Rhizotorphin inoculation increased green mass yield by 8.79%, while three-component mixtures saw a 16.49% increase. The oat–pea mixture showed the most significant amino acid improvements, with lysine increasing by 6.26% and tyrosine by 3.24%. The general conclusion reached by the two-year experiment of 2022–2023 in the hill–plain zone of northern Kazakhstan is that double grass mixtures treated with nodule bacteria are more productive than monoculture crops in this area. These results suggest that inoculation with bacterial strains can effectively enhance the productivity of forage crops in northern Kazakhstan, providing a basis for future recommendations on optimizing herbaceous crop combinations. It is recommended to grow annual forage crops in mixtures with legumes to produce highly nutritious feeds with high metabolic energy in terms of biochemical composition.

A two-year (2023–2024) multifactorial field study was conducted under the agro-climatic conditions of Northern Kazakhstan, with the objective of refining cultivation practices for hayfields of perennial legumes and grasses, including alfalfa (Medicago sativa L.), smooth brome (Bromus inermis Leyss.), and sainfoin (Onobrychis arenaria Kit). The elements targeted for optimization included the species composition and component ratios in the mixtures, as well as the regimes of pre-sowing and foliar applications of growth regulators (AminoMax, Black Jak, Miller Start, Lider-S). The integrated experimental design accounted for laboratory and field germination, biometric parameters (plant height, leafiness), phenophase dynamics, autumn survival and overwintering, indicators of photosynthetic activity, as well as yields of green biomass and dry matter, and chemical composition (crude protein, fiber, ash, fat, and nitrogen-free extract). Grass–legume mixtures ensured more stable progression of phenophases, improved overwintering, and enhanced protein value compared to monocultures; the inclusion of sainfoin contributed to improved forage quality without compromising yield. Growth regulators promoted accelerated initial plant development and enhanced the intensity of net photosynthetic productivity. The greatest effect of application was observed in the grass component with Miller Start, whereas in the legume species it was most pronounced with AminoMax. The results of the study revealed that the optimal proportion of legumes in the forage mixtures is 30–40%. Under contrasting hydrothermal conditions, the yield of fresh and dry matter ranged from 4.19 to 4.81 t ha⁻¹ and 1.27–1.51 t ha⁻¹ (2023) to 10.43–14.46 t ha⁻¹ and 3.05–4.63 t ha⁻¹ (2024). The greatest effect was observed with Miller Start and AminoMax treatments (p < 0.05), whereas the action of Black Jak and Lider-S was moderate, confirming differences in their mechanisms of action under contrasting weather conditions.

Forage crops play a vital role in ensuring livestock productivity and food security in Northern Kazakhstan, a region characterized by highly variable weather conditions. However, traditional methods for assessing crop maturity remain time-consuming and labor-intensive, underscoring the need for automated monitoring solutions. Recent advances in remote sensing and artificial intelligence (AI) offer new opportunities to address this challenge. In this study, unmanned aerial vehicle (UAV)-based multispectral imaging was used to monitor the development of forage crops—pea, sudangrass, common vetch, oat—and their mixtures under field conditions in Northern Kazakhstan. A multispectral dataset consisting of five spectral bands was collected and processed to generate vegetation indices. Using a ResNet-based neural network model, the study achieved a high predictive accuracy ($R^2$ = 0.985) for estimating the continuous maturity index. The trained model was further integrated into a web-based platform to enable real-time visualization and analysis, providing a practical tool for automated crop maturity assessment and long-term agricultural monitoring.

Sugar sorghum has emerged as a highly promising crop for bioethanol production owing to its high biomass yield potential and its remarkable capacity to accumulate fermentable sugars in the stalks. The aim of this study was to evaluate the productivity, sugar accumulation capacity, and bioethanol potential of 12 sugar sorghum accessions under the conditions of Northern Kazakhstan. Field experiments were carried out over the 2024-2025 growing seasons, during which key agronomic and technological traits were assessed, including green biomass yield, juice yield, total soluble solids (Brix), sugar concentration, and theoretical ethanol yield. The analysis of variance revealed that biomass yield was predominantly driven by weather conditions (p < 0.001), whereas sugar concentration was significantly influenced by genotype (p < 0.05). Several genotypes, namely Volonter, Kapital, Sevilya, Flagman, Chayka, and Sauri, consistently exhibited high sugar productivity and bioethanol potential across years, confirming considerable genetic variability in these traits. Storage of stem juice resulted in sugar losses of up to 30.7%, indicating the necessity for rapid processing of raw biomass after harvest. Under laboratory fermentation conditions, juice from the Sevilya genotype (17.9 Brix) achieved a sugar-to-ethanol conversion efficiency of 74.3% relative to the theoretical yield. Overall, the findings confirm the suitability of sugar sorghum for bioethanol production and highlight its strong potential as an energy crop under the arid agroclimatic conditions of Northern Kazakhstan.