## 1 Introduction  
Modern agricultural techniques have been strongly criticized about their negative environmental impacts. Examples [1] of such impacts include:  
* Loss of biodiversity among plants and animals caused by monocultures;
* Soil and groundwater pollution due to the use of chemical pesticides and fertilizers;
* Soil eroding at a much faster pace than it can be replenished;
* Fish die-offs;
* Use of water and fossil fuels at unsustainable rates.  
In the next few decades, the global population is expected to grow substantially, followed by a subsequent increase in global food demand [2]. The prices of fertilizers and pesticides are also expected to rise. Precision agriculture (PA) (also known as site-specific management, precision farming, prescription farming, etc.) [3] can have an important contribution in addressing the aforementioned challenges. It can also serve as part of an environmentally sustainable agricultural system while still maintaining profitability [4].  
According to a recent definition of PA, developed by the International Society of Precision Agriculture, the term refers to a management strategy, which uses acquisition, processing, analysis, and combination with other information of temporal, spatial, and other data in order to facilitate decision-making based on the estimated variability. Some benefits of PA solutions encompass increased efficiency and productivity, bigger profits, higher quality of agricultural products, optimized resource use, and smaller environmental footprint [5; 6]. PA is not limited to just estimating variability. The term includes a wide range of applications, such as farm management information systems, cyber-physical systems (CPS) for agriculture, farm machinery management, etc. [7] PA is included in the broader term of digital agriculture. Digital agriculture encompasses the utilization of advanced technologies integrated into one system in order to enable farmers and/or other stakeholders to improve food production in different stages of the agriculture value chain [8]. Unlike PA, digital agriculture is not limited to the on-farm production but also spans the whole value chain, including stages before and after production [9]. The term information and communication technology (ICT) refers to technological resources and solutions of different kinds used for creation, processing, transmission, storage, and exchange of information. Some examples of ICT technologies include: computers, the internet, emails, smartphones, robotic systems, broadcasting and storage solutions, televisions, radios, etc. [10; 11].  
Data lifecycle models provide a high-level framework for representing all stages of data throughout their life, from the creation to the consumption/reuse/destruction stages. As there is no widely accepted framework regarding the data lifecycle, various frameworks and models are deployed and adapted according to specific needs of business and academic communities [12].  
The present literature review has two main objectives. The first objective is to present a review of the existing literature in order to indicate ICT elements and solutions which support different stages of the PA-related data lifecycle. The second objective is to facilitate similar future research works in this area, which can utilize the two-fold categorization developed in the context of this paper.  
The remainder of this paper is organized as follows: Section 2 demonstrates our proposed data lifecycle model, which is used for the categorization of the reviewed ICT solutions. Existing data lifecycle models, which served as a basis for the development of our approach, are also described. Section 3 presents the ICT solutions which were analyzed for the purposes of this literature review, focusing on their contributions to PA. Section 4 encompasses an analysis of the results and conclusions.