Agronomy

Big data and artificial intelligence technologies are driving a paradigm shift in smart farming, yet intelligent decision-making faces critical bottlenecks. At the data level, challenges include fragmentation, high acquisition costs, and inadequate secure sharing; at the model level, issues involve regional heterogeneity, weak adaptability, and insufficient explainability. To address these, this paper systematically reviews global research to establish a theoretical framework spanning the entire production cycle. Regarding data governance, trends favor federated systems with unified metadata and layered storage, utilizing technologies like federated learning for secure lifecycle management. For decision-making, approaches are evolving from experience-based to data-driven intelligence. Pre-harvest planning now integrates mechanistic models and transfer learning for suitability and variety optimization. In-season management leverages deep reinforcement learning (DRL) and model predictive control (MPC) for precise regulation of seedlings, water, fertilizer, and pests. Post-harvest evaluation strategies utilize spatio-temporal deep learning architectures (e.g., Transformers or LSTMs) and intelligent optimization algorithms for yield prediction and machinery scheduling. Finally, a staged development pathway is proposed: prioritizing standardized data governance and foundation models in the short term; advancing federated learning and human–machine collaboration in the mid-term; and achieving real-time, ethical edge AI in the long term. This framework supports the transition toward precise, transparent, and sustainable smart agriculture.

Against the backdrop of growing climatic variability, the identification of genotypes combining high yield with stability and resilience to stress factors has become a central objective of contemporary wheat breeding. Therefore, the objective of this work was to assess the stability and adaptability of a collection of 13 wheat–wheatgrass hybrids (WWHs, lines) (Triticum aestivum L. (2n = 42)) in comparison with 10 commercial spring bread wheat (Tr. aestivum L.) cultivars under various meteorological conditions. This study was conducted in one location (Moscow region, Russia) over three growing seasons (2020, 2021, and 2022), which included a highly stressful year (2021) characterized by a severe combination of drought and heat during critical growth stages. Statistical analysis employed analysis of variance (ANOVA), clustering, and modern models for assessing the genotype-by-environment interaction (GEI)—AMMI (Additive Main Effects and Multiplicative Interaction). The results showed a significant effect of year conditions on all yield components. Under the stressful conditions of 2021, most genotypes exhibited a 30–70% decrease in productivity. Cluster analysis revealed a dynamic regrouping of genotypes depending on the conditions of the growing season. The AMMI model identified genotypes with high stability, such as Sudarinya (ASV = 9.3) and WWH 200 (ASV = 11.2), as well as genotypes specifically adapted to certain conditions: KWS Akvilon (ASV = 52.1) to stressful conditions and WWH 127 (ASV = 55.9) to favorable conditions. Under stress, lines WWH 107, WWH 127, and WWH 2430 exhibited the most adaptive strategies, including compensatory mechanisms, making these hybrids promising for further breeding. In conclusion, although wheat–wheatgrass hybrids demonstrate high productive potential under favorable conditions, their successful use in breeding requires the selection of genotypes that combine productivity and stress resistance. The identified stable and adaptive genotypes are valuable for developing new competitive cultivars under changing climatic conditions.

Integrating perennial grain crops into agricultural systems can become a key milestone for increasing the provision of ecosystem services of food production systems. Intermediate wheatgrass is a novel perennial grain and forage crop that is undergoing domestication. Potential trade-offs between resource allocation and reproductive and vegetative plant structures can challenge the response to selection for both grain and forage production under dual-purpose use. Our goal was to understand the genetic relationship between grain and forage yield components, quantify potential trade-offs between vegetative and reproductive allocation, and optimize the response to selection under dual-purpose management. Phenological, grain, and forage traits were evaluated in 30 half-sib families across two field experiments conducted over three years. No trade-offs were detected between grain and forage yield traits, indicating that the simultaneous improvement of both traits is feasible. Grain yield per spike and spikes per plant are promising secondary traits for indirect selection, given their moderate-to-high heritability (h² = 0.58 and 0.41) and strong Pearson correlation coefficients with grain yield per plant (0.68 and 0.82). These traits could be assessed in the first year, increasing genetic gain per unit time. Intermediate wheatgrass germplasm could therefore be efficiently developed by shortening the time to first evaluation, using secondary traits, and performing selection under dual-purpose management.