Agriculture is one of the most important industries in the world. In this context, the importance of Brazil as a strategic country to meet a range of SDG’s targets linked to food security, fighting against hunger, and poverty reduction is undeniable. This study aimed to highlight the production and dissemination of scientific research developed by Brazilian institutions, and to identify prominent authors and institutions based on articles related to sustainability, agriculture, livestock, and agribusiness. A bibliometric analysis was developed based on a sample of 3139 documents published between 2000 and 2022, comprising 21,380 authors that were then analyzed using the Biblioshiny package. As result, the term “sustainability” showed growth as it branched out to semantically similar terms, such as “sustainable agriculture” and “sustainable intensification”; and “crop–livestock integration” and “agroforestry” were highlighted as important in the development of future research. The majority of documents were produced by the University of São Paulo (~33%), the State University of São Paulo (~15%), and the Federal University of Rio Grande do Sul (~11%), suggesting that their researchers could act as coordinators in future research through the formation of multi-collaborative groups to jointly lead to the participatory elaboration of public policies that promote more sustainable paths for agricultural production.

Evidence shows that diversity and spatial distributions of biological communities are largely driven by the race of living organisms in their adaptation to chemicals synthesized by their neighbors. In this report, the emergence of mathematical models on pure spatial self-organization induced by biochemical suppression (allelopathy) and competition between species were investigated through numerical analysis. For both random and patched initial spatial distributions of species, we demonstrate that warfare survivors are self-organized on the landscape in Turing-like patterns driven by diffusive instabilities of allelochemicals. These patterns are simple; either all species coexist at low diffusion rates or are massively extinct, except for a few at high diffusivities, but they are complex and biodiversity-sustained at intermediate diffusion rates. “Defensive alliances” and ecotones seem to be basic mechanisms that sustain great biodiversity in our hybrid cellular automata model. Moreover, species coexistence and extinction exhibit multi-stationarity.

Evidence is mounting that the race of living organisms for adaptation to the chemicals synthesized by their neighbors may drive community structures. Here, an ecoevolutionary model for community assembly through resource competition, toxin-mediated interactions (allelopathy), and evolutionary branching is investigated. We found that stable communities with increasing biodiversity can emerge at weak allelopathic suppression, but strong chemical warfare drastically impairs diversity. For successive invasion events, the allelopathic interaction networks exhibit, respectively, Gaussian and Weibull degree distributions at weak and strong allelopathy. For the branching process dynamics, degrees scale as power laws truncated by stretched exponentials in both regimes. In addition, allelochemical interactions tend to be arranged in modules with low clustering coefficients and disassortative behavior to ensure community stability. So, in a homogeneous environment, species-rich communities can be assembled only at the context of a weak biochemical warfare between organisms, and even under this regime species interact with only a few others.

Government plans are crucial for strengthening democratic regimes by defining clear policies and priorities, and by serving as essential channels of communication with voters and social groups within electoral systems. The political commitment to align these plans with the United Nations Sustainable Development Goals (SDGs) of the 2030 Agenda reflects an effort to integrate global priorities into local agendas. Such alignment is critical for fostering sustainable and inclusive progress. However, despite its importance, research on the intersection between government plans and the SDGs remains limited. In this study, we used natural language processing (NLP), artificial intelligence, and statistical analysis to examine the government plans of ten candidates in the 2022 gubernatorial election in the Brazilian state of São Paulo. Our analysis identified key policy topics, explored patterns and trends, and assessed alignment with the 17 SDGs across the social (SDGs 1, 2, 3, 4, 5, 10, and 16), environmental (SDGs 6, 13, 14, and 15), and economic (SDGs 7, 8, 9, 11, and 12) dimensions. The findings emphasize the need for government plans that are more closely aligned with sustainable development goals and that demonstrate a strong political commitment to addressing critical issues, particularly the impacts of climate change. This is especially urgent for the state of São Paulo, which faces significant challenges, including natural disasters, heat waves, flooding, water scarcity, and infrastructure deficiencies.