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.
Highlights
The obtained results, as it will be shown below, considered the data collected according to the pre-established protocol, and the available data provided by farmers regarding prior campaigns
Coupling the collected data with the production data recorded by the farmer in previous campaigns, in which the highest average production levels of the plot were approximately 15 T/ha—in areas where the plants presented high vigorosity, and the lowest levels were around 12 T/ha—it was possible to estimate the production according to the normalized difference vegetation index (NDVI) map (Figure 5)
The obtained results, based on the assessment methods used throughout this research, highlight the fact that an efficient combination of unmanned aerial vehicles (UAV)/remotely piloted aircraft systems (RPAS) and NDVI enables important savings in productivity factors, promoting sustainable agriculture both in ecological and economic terms
Summary
Climate change, urbanization and agricultural overexploitation will contribute to considerable losses of the arable land available for food production, causing serious problems for the production-consumption balance [3,4,5,6,7,8] These facts, coupled with the need to produce food in an increasingly sustainable manner, in terms of crop efficiency, and in terms of land use and biodiversity conservation in natural ecosystems, highlight the need to envision the use of new technologies in different productive systems and to assess their environmental, economic and social impact. Precision agriculture, a farming management model based on observing, determining and responding to inter and intra-field productive variability enables the definition of a decision support system (DSS) for specific farm management with the goal of optimizing returns on inputs while preserving resources [13]
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