# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
###### Abstract  
Profitability is not given the necessary attention in contemporary precision agriculture. In this work, a new tool, namely ProFit, is developed within a pre-existing farm management system, namely ifarma, to assess the profitability of precision agriculture applications in extended crops, as most of the current solutions available on the market respond inadequately to this need. ProFit offers an easy-to-use interface to enter financial records, while it uses the dynamic map view environment of ifarma to display the profitability maps. Worked examples reveal that profitability maps end up being quite different from yield maps in site-specific applications. The module is regulated at a 5 m spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of ifarma, together with a flexible architecture allowing future interventions and improvements, renders ProFit an innovative commercial tool.  
precision agriculture; site-specific fertilization; digital agriculture; ifarma +
Footnote †: journal:  
Precision Agriculture (PA) is one of several methodologies that can improve farm management by providing timely, thorough, site-specific crop information within a decision-making framework. Data can be retrieved from a variety of sources, such as soil sampling, sensors, weather stations, satellite or drone images, and yield monitors.  
As a tool facilitating farmers to more efficient management of their land, precision agriculture significantly and variously impacts farm management. According to global trends, the application of precision agriculture worldwide is estimated to increase in the next four years, with the market value doubling from USD 17.41 billion in 2022 to USD 34.1 billion in 2026 [5].  
The main goal of precision agriculture research is to define a decision support system (DSS) for whole farm management with the aim of optimizing returns on inputs while preserving resources [6; 7; 8]. However, farming is a complex endeavor involving many factors and inputs, such as land cost, labor, expensive machines and various tools, fertilizers, pesticides, and irrigation. In most cases, farming activities are not properly logged, at least not in a systematic and analytic way, and most data are fragmented, dispersed, and difficult to use [9].  
According to a recent review based on a 23-year meta-analysis [10], profitability, consultancy, and computer use had only a moderate effect on the adoption of precision agriculture. However, these findings should be viewed cautiously due to issues of sample size and heterogeneity embedded in some of the reference studies, while at the same time, other factors had a negligible effect on adoption. Precision agriculture must be distinguished from \"smart agriculture\", regarding the concept of site-specificity. Smart agriculture is associated with various types of sensors used (soil, moisture, climatic, etc.) to derive crop information (and potentially return a decision), regardless of satisfaction with within-field spatial variability, as it happens with precision agriculture practices.