Towards a methodology to consider the environmental impacts of digital agriculture
- URL: http://arxiv.org/abs/2305.09250v2
- Date: Wed, 11 Sep 2024 08:34:53 GMT
- Title: Towards a methodology to consider the environmental impacts of digital agriculture
- Authors: Pierre La Rocca,
- Abstract summary: Agriculture affects global warming, while its yields are threatened by it. Information and communication technology (ICT) is often considered as a potential lever to mitigate this tension.
This research aims at defining a methodology taking into account the environmental footprint of agricultural ICT systems and their required infrastructures.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Agriculture affects global warming, while its yields are threatened by it. Information and communication technology (ICT) is often considered as a potential lever to mitigate this tension, through monitoring and process optimization. However, while agricultural ICT is actively promoted, its environmental impact appears to be overlooked. Possible rebound effects could put at stake its net expected benefits and hamper agriculture sustainability. By adapting environmental footprint assessment methods to digital agriculture context, this research aims at defining a methodology taking into account the environmental footprint of agricultural ICT systems and their required infrastructures. The expected contribution is to propose present and prospective models based on possible digitalization scenarios, in order to assess effects and consequences of different technological paths on agriculture sustainability, sufficiency and resilience. The final results could be useful to enlighten societal debates and political decisions.
Related papers
- LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions [40.08908132533476]
The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices.
This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems.
arXiv Detail & Related papers (2024-09-17T13:55:44Z) - Harnessing Artificial Intelligence for Sustainable Agricultural
Development in Africa: Opportunities, Challenges, and Impact [0.0]
The study navigates through the dynamic landscape of AI applications in agriculture.
Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined.
Ethical considerations and policy implications are also discussed.
arXiv Detail & Related papers (2024-01-03T23:02:13Z) - Artificial Intelligence in Sustainable Vertical Farming [0.0]
The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming.
The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics.
The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security.
arXiv Detail & Related papers (2023-11-17T22:15:41Z) - Climate Change Impact on Agricultural Land Suitability: An Interpretable
Machine Learning-Based Eurasia Case Study [94.07737890568644]
As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition.
Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages.
Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
arXiv Detail & Related papers (2023-10-24T15:15:28Z) - Predictable Artificial Intelligence [77.1127726638209]
This paper introduces the ideas and challenges of Predictable AI.
It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.
We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
arXiv Detail & Related papers (2023-10-09T21:36:21Z) - Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry.
We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery.
We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
arXiv Detail & Related papers (2023-05-03T05:16:54Z) - Towards assessing agricultural land suitability with causal machine
learning [0.0]
We use causal machine learning to estimate the effect of crop rotation and landscape crop diversity on Net Primary Productivity in the Flanders region of Belgium.
We find that the effect of crop rotation was insignificant, while landscape crop diversity had a small negative effect on NPP.
arXiv Detail & Related papers (2022-04-27T14:13:47Z) - Role of Information and ICTs as Determinants of Farmer's Adaptive
Capacity to Climate Risk: An Empirical Study From Haryana, India [0.0]
We use path analysis technique using the lavaan package in RStudio to empirically test the role of information.
We find that information is a direct and significant contributor to enhancing farmers' adaptive capacity.
We take an ensemble view of ICTs operationalized using ICT ecosystem and find significant interlinkages between information, technology and the ICT ecosystem.
arXiv Detail & Related papers (2021-08-22T16:00:06Z) - A web-tool for calculating the economic performance of precision
agriculture technology [0.0]
The web-tool is designed to provide guidelines for farmers over their decisions to invest in selected PA technologies.
It increases the knowledge level about novel technologies characteristics and the related benefits.
arXiv Detail & Related papers (2020-12-09T12:51:15Z) - Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity.
Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
arXiv Detail & Related papers (2020-12-07T16:23:13Z) - A survey on applications of augmented, mixed and virtual reality for
nature and environment [114.4879749449579]
Augmented reality (AR), virtual reality (VR) and mixed reality (MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing.
However, the possibilities that AR, VR and MR offer in the area of environmental applications are not yet widely explored.
We present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues.
arXiv Detail & Related papers (2020-08-27T09:59:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.