Introduction: Biofertilizers are considered one of the pillars of sustainable agriculture, aiming to reduce dependence on chemical inputs and enhance the productivity of agroecosystems. However, one of the major challenges in the application of these fertilizers is their poor or unsuccessful performance under real field conditions compared to controlled laboratory environments. This inconsistency, known in the scientific literature as the "lab-to-farm gap", is associated with the inherent complexities of agricultural systems; as beneficial microbes are transferred from isolated and optimal conditions into the dynamic "soil-plant-climate-management" system, their efficiency declines. This issue is of particular sensitivity in arid and semi-arid regions, where rainfed legumes play a key role in food security and farmers' livelihoods, due to the occurrence of concurrent environmental stresses. Therefore, an in-depth understanding of the factors creating this gap and the presentation of an analytical framework for identifying critical points is an essential step toward enhancing the predictability and success of biofertilizers in such fragile ecosystems.
Methodology: This article was conducted with an analytical-diagnostic review approach, utilizing a qualitative meta-analysis method of previous studies. For this purpose, all indexed articles published between 2015 and 2025 in reputable scientific databases such as SID, Web of Science, Scopus, and ScienceDirect were extracted using keywords related to biofertilizer performance in rainfed legumes and biotic and abiotic stresses. Subsequently, by applying inclusion and exclusion criteria, approximately 100 studies were selected and examined in terms of three main components: microbial strain characteristics, environmental and farm management conditions, and formulation and application methods. In the next step, all variables affecting the reduction of biofertilizer efficiency were categorized into three groups of environmental, biological, and technical-managerial filters. For each of these filters, based on a systematic review of empirical evidence, failure mechanisms were identified and prioritized. Finally, the proposed analytical-diagnostic model (failure filters) was developed and structured with the aim of providing an operational roadmap for the targeted design of strains, formulations, and management strategies.
Research findings: The beneficial mechanisms of biofertilizers (such as biological nitrogen fixation, nutrient solubilization, hormonal regulation, and induction of systemic resistance) must sequentially pass through three filters to be effective in the field: environmental filters (drought stress, temperature, and soil chemistry), biological filters (competition with native microbiome and predation), and technical-managerial filters (formulation problems, application method, and incompatibility with inputs). Together, these filters synergistically reduce the efficiency of biofertilizers. The central insight emerging from this analysis is the critical need for a paradigm shift—away from a laboratory‑centric, strain‑centric screening logic, and toward a systems‑based, field‑tailored design framework. In this revised approach, strain selection, formulation strategy, and application method are not treated as independent steps but as interdependent components that must be co‑optimized according to the specific edaphic, climatic, and agronomic context of each target field. Within this framework, the primary criterion for strain prioritization shifts from maximal in vitro functionality to robust in situ survivability and competitive fitness under realistic field conditions. Concomitantly, advanced formulation technologies and complementary agronomic interventions should be purposefully engineered to shield microbial inoculants from the identified filters. As a forward‑looking research agenda, this study advocates the integration of omics‑based tools for intelligent strain discovery, the establishment of multi‑tiered field‑scale evaluation pipelines, and the adoption of digital agriculture platforms to enable site‑specific and precision‑guided biofertilizer deployment. |