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dc.contributor.authorLERMAN, L. V.
dc.contributor.authorGALLO, B. B.
dc.contributor.authorSILVA, R. F. da
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorSILVA, M. O. da
dc.contributor.authorALVES, T. M.
dc.contributor.authorPORCINO, T. M.
dc.contributor.authorPEREIRA, C. E.
dc.date.accessioned2026-08-04T18:49:25Z-
dc.date.available2026-08-04T18:49:25Z-
dc.date.created2026-08-04
dc.date.issued2026
dc.identifier.citationIn: INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE, 17., CONGRESSO BRASILEIRO DE AGRICULTURA DE PRECISÃO DIGITAL, 11., 2026, Porto Alegre. Proceedings [...]. Monticello: International Society of Precision Agriculture, 2026.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1188924-
dc.descriptionDigital and precision agriculture are expected to play a central role in shaping future sustainable food production systems by enabling more intelligent, adaptive, and resource-efficient processes. This study adopted a literature review, composed of bibliometric and systematic aspects, to propose a conceptual framework for Smart Resource Use as an emerging paradigm for sustainable development in digitally enabled precision agriculture, extending the concept of smart consumption beyond its traditional origins in digital manufacturing. This study examined how nextgeneration technologies can progressively transform the management of critical agricultural resources such as water, energy, soil, and inputs. Our analysis included a conceptual synthesis of recent advances in digital agriculture, including remote sensing, artificial intelligence, machine learning, automation, robotics, big data analytics, decision support systems, and agricultural digital twins. It indicated that agricultural systems are moving beyond efficiency-based approaches towards predictive, autonomous, integrated, and system-level resource management models capable of dynamically allocating resources at field and sub-field scales, reducing environmental impacts, and enhancing resilience to climate variability. A shift from only maximizing yield to improving resource-use efficiency and long-term sustainability outcomes was also observed. From a sustainability perspective, the Smart Resource Use paradigm is positioned as a key enabler of the United Nations Sustainable Development Goals, particularly SDG 2 by supporting long-term food security and productivity growth, SDG 6 through advanced water-use optimization, SDG 12 by minimizing input waste and promoting circular resource use, and SDG 13 by enabling climate-smart and low-carbon agricultural practices. As a contribution to future research, the conceptual framework presented could be used as a basis to strategically plan the transition toward digitally enabled sustainable agricultural systems. It also provides strategic insights for researchers, policymakers, and practitioners seeking to design the next generation of resource-efficient agricultural solutions. Lastly, the main research gaps observed in the current literature and practice in the relevant areas analyzed were also described.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAgricultura digital
dc.subjectEficiência no uso de recursos
dc.subjectInteligência artificial
dc.subjectGêmeos digitais agrícolas
dc.subjectAgricultura inteligente em relação ao clima
dc.subjectDigital agriculture
dc.subjectResource-use efficiency
dc.subjectAgricultural digital twins
dc.subjectClimate-smart agriculture
dc.titleSmart resource use in precision agriculture: a conceptual framework for digital sustainability development.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAutomação
dc.subject.thesagroDesenvolvimento Sustentável
dc.subject.thesagroAgricultura de Precisão
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusSustainable development
dc.subject.nalthesaurusPrecision agriculture
dc.subject.nalthesaurusAutomation
dc.subject.nalthesaurusArtificial intelligence
dc.description.notesICPA 2026. ConBAP 2026.
dc.format.extent215 p.
riaa.ainfo.id1188924
riaa.ainfo.lastupdate2026-08-04
dc.contributor.institutionLAURA VISINTAINER LERMAN, TECHNICAL UNIVERSITY OF MUNICH; BETTY BRAGA GALLO, CENTER FOR EMBEDDED DEVICES AND RESEARCH IN DIGITAL AGRICULTURE; ROBERTO FRAY DA SILVA, UNIVERSIDADE DE SÃO PAULO; EDSON LUIS BOLFE, CNPTIA; MANOELA OLIVEIRA DA SILVA, UNIVERSIDADE DE PERNAMBUCO; TAVVS MICAEL ALVES, INSTITUTO FEDERAL GOIANO; THIAGO MALHEIROS PORCINO, LABORATÓRIO NACIONAL DE COMPUTAÇÃO CIENTÍFICA; CARLOS EDUARDO PEREIRA, CENTER FOR EMBEDDED DEVICES AND RESEARCH IN DIGITAL AGRICULTURE.
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