https://www.alice.cnptia.embrapa.br/alice/handle/item/17 Embrapa Hortaliças (CNPH) 2026-06-24T00:28:16Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187696 Título: Research trends and knowledge gaps in sustainable urban agriculture: a scientometric analysis. Autoria: MARTINS, T. C.; CESAR, T. Q. Z.; SILVA, F. B. da; GUEDES, I. M. R.; SOUCHIE, E. L.; DAMKE, C. da R.; DORO, V. da C.; SILVA, F. G. Conteúdo: Urban agriculture plays a strategic role in sustainability, food security, and climate adaptation in cities, where temperature emerges as a key variable. This study conducted a scientometric and qualitative analysis to investigate how temperature has been addressed in the scientific literature on urban agriculture between 2020 and 2025. A total of 244 documents were retrieved from Scopus and Web of Science, followed by a qualitative screening that resulted in 20 articles with high thematic relevance. The results reveal a strong geographic concentration of research in Asia–Pacific countries and a rapid expansion of publications after 2022. The qualitative analysis enabled the classification of studies into three main groups: Group A (open and semi-open systems), Group B (building-integrated and protected systems), and Group C (fully controlled indoor environments). Group C represents the majority of studies (55%), indicating a strong research focus on high-technology systems such as plant factories. Group B accounts for (30%), highlighting growing interest in energy integration between agriculture and buildings, while Group A represents only (15%), showing that open-field urban agriculture remains underexplored in terms of temperature. Temperature is addressed at different scales: as a microclimatic regulator in open environments, as a mediator of energy exchange in building-integrated systems, and as a high-precision control variable in fully controlled systems. Despite its central role, temperature-focused studies remain limited, revealing gaps in empirical validation and multi-scale integration. These findings highlight a technological shift toward controlled environment agriculture and the need for integrated approaches combining microclimate regulation, energy efficiency, and precision control. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187361 Título: An open-architecture precision vertical farming system for sesame microgreens: audit-ready telemetry for dynamic lighting and energy–biomass benchmarking. Autoria: SILVA, M. D.; VASCONCELOS, J. M.; SILVA, F. B. da; BAILÃO, A. S. de O.; GUEDES, I. M. R.; VILELA, M. da S.; COSTA, A. C.; ROSA, M.; LOURENÇO, L. L.; SILVA, F. G. Conteúdo: The reproducibility of lighting protocols in Plant Factories with Artificial Lighting (PFALs) is frequently constrained by the scarcity of auditable operational data. This study presents and validates an open-architecture Precision Vertical Farming System (PVFS) capable of executing dynamic photosynthetic photon flux density (PPFD) profiles under controlled Daily Light Integral (DLI), ensuring traceability via IoT telemetry. The system was applied to sesame microgreens grown using a 2×4 factorial design, combining temporal profiles (Constant vs. Gaussian) and light spectra (White, Blue, Red, and RBW), with DLI equalized at 10.8molm−2 d−1. Telemetric validation demonstrated high stability (jitter ≈ 0) and data completeness (> 98%), enabling the precise calculation of Specific Energy Consumption (SEC) and Energy-to-Mass Efficiency (EEMS). Results indicated that, under equivalent DLI, the Gaussian profile increased energy costs (higher SEC) without proportional biomass gains for most spectra. The Red–Constant treatment achieved the highest efficiency (10.02gkWh−1), whereas white light exhibited the highest energy cost. Principal Component Analysis (PCA) reinforced that energy performance (SEC/EEMS) was more strongly associated with production outcomes (biomass) than with instantaneous photosynthetic metrics (e.g., ����∕����), underscoring the importance of continuous monitoring of energy use and yield. The PVFS proved to be a robust tool for energy benchmarking and the standardization of lighting recipes in vertical farming. 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187280 Título: Diversity of Capsicum in Brazil and its genetic and agronomic potential. Autoria: REIFSCHNEIDER, F. J. B. Conteúdo: The Embrapa-based Capsicum research program has had activities that go from expeditions to collect germplasm in remote areas of the country to molecular markers, volatile determination and processing characteristics, bridging knowledge and technology gaps of relevance to both public and private sectors; its focus has been the development of disease-resistant lines, cultivars and hybrids of both hot as well as sweet peppers. 2012-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1187068 Título: Low-cost lettuce phenotyping platforms powered by generative AI: development, validation, and methodological democratization for climate-just brazilian lettuce crops. Autoria: FONTENELLE, M. R.; GUEDES, I. M. R.; SUINAGA, F. A.; SILVA, J. da; BRAGA, M. B.; MARTINS, S. C. V.; LIMA, C. E. P. Conteúdo: In this context, low-cost digital technologies capable of democratizing access to technical and scientific tools are essential to enhance the resilience and adaptive capacity of vegetable production systems. This study aimed to develop and validate low-cost lettuce phenotyping platforms powered by generative artificial intelligence (AI), focusing on thermal physiological disorders, using Prompt Engineering and Prompt Chaining as core methodologies. The framework comprised: (i) compilation and mapping of climate information and thermal risk/severity for lettuce in Brazil; (ii) identification of key physiological disorders expected under GCC scenarios based on Lima et al.(2024) and a systematic literature review; (iii) design of an analytical pipeline using Prompt Engineering and Prompt Chaining for generative Ais; and (iv) extraction of Python scripts and generation of SHA-256 hash codes in Visual Studio Code (VSC), followed by validation through comparison between AI-generated reports and the reference results of Lima et al.(2024). The resulting set of scripts reproduced, with high consistency, the patterns reported in the benchmark study and were made publicly available under FAIR principles. These results represent an important tool to accelerate the development of technological solutions for increasing resilience and climate adaptation in Brazilian lettuce production, while remaining replicable and adaptable via open-source code to other regions of the world. 2026-01-01T00:00:00Z