Precision seeding plays a critical role in optimizing crop yield and resource efficiency. This study evaluates the application of a Seed Miss Prevention System (SMPS) integrated with a spoon-wheel precision metering device to mitigate seed misses and enhance its performance. A combination of Discrete Element Method (DEM) simulations, electrical hardware design, mechanical retrofitting, software development and laboratory experiments was employed to assess the effectiveness of the system across multiple seed cultivars and operating speeds. Experimental results demonstrated that the SMPS significantly reduced seed misses at lower operational speeds (3–10 rpm), with the implementation of a dual-sensor configuration further improving detection accuracy by filtering out false positives. At higher speeds (≥15 rpm), however, seed miss rates increased, particularly for irregularly shaped seeds like white beans ‘Great Northern’, due to the mechanical limitations of the metering device. Statistical analyses, including Tukey’s HSD test, confirmed the effectiveness of the SMPS in reducing miss rates across different seed types. Despite these improvements, complete elimination of seed misses was not achieved, highlighting the need for further optimization in seed miss detection. Future research should explore adaptations for higher-speed metering devices and field-scale validations. The findings underscore the potential of SMPS technology in advancing precision agriculture by improving seeding accuracy and operational efficiency.

One of the main challenges in modern animal husbandry in North Kazakhstan is ensuring an uninterrupted supply of sufficient fodder crops. This research, conducted from 2019 to 2023, aimed to develop strategies for cultivating environmentally sustainable fodder crops capable of providing a stable fodder crop base under the changing climatic conditions of the North Kazakhstan region. The studies included analysis of air temperature and precipitation data as well as monitoring of fodder grass mixtures within a green fodder conveyor system. Different sowing dates for fodder crops and mixtures were selected for the development of the conveyor system. The range of experimental variants included fodder crops and their mixtures from various botanical families. The experiment involved both perennial (alfalfa and festulolium) and annual (corn, pea, sunflower, Sudan grass, oats, and rapeseed) crops. The highest green mass yields were achieved by the following variants: fodder crops of corn + pea—74.40 c/ha; mixtures of annual legume–grass crops in the pea + oats variant of the first sowing date—43.64 c/ha; Sudan grass + pea—45.72 c/ha; mixtures of perennial grasses in the second utilization term of alfalfa + festulolium—64.9 c/ha; and rapeseed sown at the first sowing date—46.61 c/ha. In terms of crude and digestible protein content, the best among the annual grass variants was the mixture of Sudan grass and pea (crude protein—33.59 g/kg, digestible protein—24.5 g/kg), and the best among the perennials was the variant of the first utilization term (crude protein—50.42 g/kg, digestible protein—38.2 g/kg). Regarding metabolizable energy content, the annual crop variant of corn + pea had a yield of 1.92 MJ/kg, and in the perennial variant, the mixture of alfalfa and festulolium in the first utilization term had a yield of 2.68 MJ/kg. Such an approach to creating green fodder conveyors can be crucial for developing effective strategies for adapting agriculture to climate change, including the selection of promising fodder crops and optimization of their placement. The results obtained can contribute to enhancing the productivity and sustainability of agricultural production in the North Kazakhstan region.

Accurate seed singulation is critical for uniform crop establishment and yield optimization in precision agriculture. This study presents the development and evaluation of a Hall sensor-based Seed Miss Prevention System (SMPS) integrated into a long-belt finger-clip precision metering device for corn (‘Dekalb DKC5032’) and sunflower (‘Astana’). The system utilizes neodymium magnets mounted on seed-picking fingers to trigger a Hall sensor that detects missed seeds in real time and initiates immediate compensation. Laboratory tests across rotational speeds from 10 to 80 rpm showed that the SMPS significantly reduced seed misses, especially within the 10–30 rpm range, where near-perfect singulation was achieved for corn (Miss Index < 0.01). For sunflower, although performance at very low speeds was limited by mechanical variability, the SMPS effectively reduced the miss index by up to 50 % at medium speeds. Statistical analysis (Tukey HSD) confirmed significant improvements in single and double miss prevention across both crops. The proposed Hall sensor-based approach demonstrated a robust, cost-effective, and dust-resistant solution for enhancing seed placement accuracy, providing a strong foundation for the development of intelligent and adaptive precision seeding systems.

Low forage productivity of natural grasslands remains a major limitation for sustainable livestock production in the forest–steppe zone of Northern Kazakhstan, highlighting the need for high-yield, locally adapted forage systems. This study evaluated nine forage agrophytocenoses, including perennial grasses and legume–grass mixtures, established in 2024 and assessed over two growing seasons on leached chernozem soils. Plant height, stand density, and biomass yields were quantified at optimal harvest stages, with statistical differences tested using one-way ANOVA and Tukey’s HSD (p < 0.05). Legume-containing agrophytocenoses consistently outperformed natural grass cover and grass monocultures in canopy development and biomass accumulation. The highest productivity was achieved in Lolium multiflorum + Medicago sativa (I+A), Medicago sativa + Festuca arundinacea (A+TF), and Onobrychis viciifolia + Festulolium + Phleum pratense (S+F+T), reaching up to ~19.66 t ha⁻¹ green biomass and ~5.24 t ha⁻¹ dry matter. In contrast, Agropyron cristatum monoculture yielded minimally during establishment, while ryegrass mixtures with annuals declined in the second year. Optimized legume–grass agrophytocenoses represent the most productive and agronomically reliable strategy to enhance forage supply and improve environmental resilience in Northern Kazakhstan.