Assessing the Effectiveness of Precision Agriculture Management Systems in Mediterranean Small Farms

Abstract

While the world population continues to grow, increasing the need to produce more and better-quality food, climate change, urban growth and unsustainable agricultural practices accelerate the loss of available arable land, compromising the sustainability of agricultural lands both in terms of productivity and environmental resilience, and causing serious problems for the production-consumption balance. This scenario highlights the urgent need for agricultural modernization as a crucial step to face forthcoming difficulties. Precision agriculture techniques appear as a feasible option to help solve these problems. However, their use needs to be reinvented and tested according to different parameters, in order to define both the environmental and the economic impact of these new technologies not only on agricultural production, but also on agricultural sustainability. This paper intends, therefore, to contribute to a better understanding of the impact of precision agriculture through the use of unmanned aerial vehicles (UAV)/remotely piloted aircraft systems (RPAS) and normalized difference vegetation index (NDVI) techniques in small Mediterranean farms. We present specific data obtained through the application of the aforementioned techniques in three farms located along the Portuguese-Spanish border, considering three parameters (seeding failure, differentiated irrigation and differentiated fertilization) in order to determine not only the ecological benefits of these methods, but also their economic and productivity aspects. The obtained results, based on these methods, highlight the fact that an efficient combination of UAV/RPAS and NDVI techniques allows for important economic savings in productivity factors, thus promoting a sustainable agriculture both in ecological and economic terms. Additionally, contrary to what is generally defended, even in small farms, as the ones assessed in this study (less than 50 ha), the costs associated with the application of the aforementioned precision agriculture processes are largely surpassed by the economic gains achieved with their application, regardless of the notorious environmental benefits introduced by the reduction of crucial production inputs as water and fertilizers.

Analyzing Precision Agriculture Adoption across the Globe: A Systematic Review of Scholarship from 1999–2020

“…PA applies traditional farming practices with new technology, practices, and economic drivers to enhance sustainability in a dynamic balance [15]. Studies have reported positive outcomes from PA adoption, including economic savings in productivity factors [16], increasing yield and environmental sustainability [17], and improving food security and community economic vitality in developing regions [18]. The International Society for Precision Agriculture (ISPA) provides clarity and guidance on this important concept, to achieve the agricultural production quality, create production profits, and improve the efficiency of resource use and environmental sustainability [19].…”

Design of a Data Management Reference Architecture for Sustainable Agriculture

“…Recent technologies, such as the Internet of Things (IoT), make digital data acquisition and hence, smart farming possible [6]. In recent years, many studies have been performed in smart farming and precision agriculture [7][8][9][10][11][12][13]. Currently, Industry 4.0 acts as a transformative force on smart farming processes.…”

Productivity and Efficiency of Precision Farming: The Case of Czech Cereal Production

“…Precision farming utilizes information technology, sensor technologies, satellite technology, Artificial Intelligence (AI), and the Internet of Things for enhancing all functions and services of the agriculture sector (Khanal et al, 2017 andSchrijver et al, 2016). Moreover, precision farming implements techniques and technologies that highlight the relevance of integrating specific ecological principles and biodiversity management procedures into agrospace management, while optimizing inputs to maximize yields (Loures et al, 2020). For example, machine learning technology can be integrated with remote sensing for accurate forecasting of crop production and estimation of nitrogen levels in precision farming (Torky and Hassanein, 2020).…”