Data-driven agricultural paradigms and sustainability are inextricably linked. However, even though the economic, social and environmental implications of data-driven paradigms such as precision farming and Agriculture 4.0 have been addressed extensively by existing scientific research, the topic is immature from a managerial point of view. This dissertation proposes a sustainability-oriented clustering of data-driven business models in the agricultural sector. The clustering identifies seven archetypes of business models within the sector, covering four macro-areas of sustainability (input efficiency, smallholders’ inclusion, food traceability and supply chain transparency, animal welfare). The clustering fills a gap between academia and adoption, shedding light on the role of business models as a link between experimental findings and field application. A second contribution is a list of barriers hampering the adoption of data-driven solutions within the sector. The list was formed and validated through a multi-method analysis involving technology providers, academia and farmers. What emerges from this second part of this work is a multifaceted context in which several issues contribute to the backwardness of the sector. Technology providers, institutions and academia should join their forces so to overcome barriers related to how data-driven technologies are communicated, commercialized and integrated within the existing infrastructure. Finally, this dissertation analyzes the suitableness for agriculture of the available multispectral satellite technologies. Spatial, spectral and temporal requirements were benchmarked with the existing supply, finding needs for improvement especially for spectral and temporal resolution. This research has several implications for both policymakers and businesses, as it gives them an instrument for assessing firms in terms of sustainability orientation. Moreover, by generating a comprehensive list of factors hindering adoption, this dissertation is a support for selecting factors to be prioritized. Finally, it generates inputs for the space industry by identifying gaps in the current supply of multispectral imagery.
L’utilizzo di dati per l’ottimizzazione delle pratiche agricole è inestricabilmente collegato al concetto di agricoltura sostenibile. Tuttavia, nonostante i benefici economici, sociali ed ambientali di paradigmi come agricoltura di precisione e agricoltura 4.0 siano stati largamente dimostrati dalla letteratura scientifica, il tema è ancora immaturo dal punto di vista imprenditoriale e manageriale. Questa tesi contribuisce a colmare tale gap identificando sette macro-tipologie di business model basati sulla raccolta e l’analisi di dati agricoli. Tali categorie, suddivise in base al loro diverso orientamento di sostenibilità, presentano caratteristiche molto differenti anche in termini strategici e tecnologici. Il secondo contributo di questa tesi è un elenco di barriere che ostacolano l’adozione di tecnologie data-driven in agricoltura. La lista è stata formata e validata tramite un’analisi multi-metodologica che ha coinvolto provider tecnologici, accademia e potenziali utenti. Da tale analisi emerge un contesto ancora immaturo, dove il combinato disposto di molteplici fattori danneggia il potenziale innovativo del settore. Sforzi congiunti da parte di provider tecnologici, ricerca e istituzioni sono necessari per superare tali sfide. L’ultima parte della tesi analizza l’attuale offerta di immagini satellitari multispettrali, identificando potenziali discrepanze tra i requisiti pratici dell’utente e le specifiche attualmente a disposizione. Il risultato è un quadro ancora immaturo. Miglioramenti a livello di risoluzione spettrale e temporale sono necessari affinché i satelliti siano utilizzabili su larga scala in agricoltura. Questa tesi fornisce ai manager e agli enti istituzionali uno strumento per classificare i business model in base al loro orientamento alla sostenibilità. Inoltre, identificando fattori che limitano l’adozione di soluzioni data-driven, questa tesi è un supporto per la prioritizzazione di misure atte a favorire l’innovazione del settore agricolo. Infine, genera indicazioni per il settore spaziale, identificando aree di potenziale miglioramento dell’attuale offerta.
Diffusion and implementation of data-driven business models for sustainable agriculture : a multi-method empirical analysis
PIOVANI, LORENZO
2018/2019
Abstract
Data-driven agricultural paradigms and sustainability are inextricably linked. However, even though the economic, social and environmental implications of data-driven paradigms such as precision farming and Agriculture 4.0 have been addressed extensively by existing scientific research, the topic is immature from a managerial point of view. This dissertation proposes a sustainability-oriented clustering of data-driven business models in the agricultural sector. The clustering identifies seven archetypes of business models within the sector, covering four macro-areas of sustainability (input efficiency, smallholders’ inclusion, food traceability and supply chain transparency, animal welfare). The clustering fills a gap between academia and adoption, shedding light on the role of business models as a link between experimental findings and field application. A second contribution is a list of barriers hampering the adoption of data-driven solutions within the sector. The list was formed and validated through a multi-method analysis involving technology providers, academia and farmers. What emerges from this second part of this work is a multifaceted context in which several issues contribute to the backwardness of the sector. Technology providers, institutions and academia should join their forces so to overcome barriers related to how data-driven technologies are communicated, commercialized and integrated within the existing infrastructure. Finally, this dissertation analyzes the suitableness for agriculture of the available multispectral satellite technologies. Spatial, spectral and temporal requirements were benchmarked with the existing supply, finding needs for improvement especially for spectral and temporal resolution. This research has several implications for both policymakers and businesses, as it gives them an instrument for assessing firms in terms of sustainability orientation. Moreover, by generating a comprehensive list of factors hindering adoption, this dissertation is a support for selecting factors to be prioritized. Finally, it generates inputs for the space industry by identifying gaps in the current supply of multispectral imagery.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/154501