Today agricultural practice is faced with a paradigm shift. In terms of natural resources, the World’s growing population calls for rational management and environment-conscious behaviour. Precision farming may provide a solution for the above mentioned criteria and problems. It has an...

Meta-response functions for corn yields and nitrogen losses were estimated from EPIC-generated data for three soil types and three weather scenarios. These metamodels were used to evaluate variable rate (VRT) versus uniform rate (URT) nitrogen application technologies for alternative weather...

A theoretical model identified ranges of spatial variability required within multiple-land-class fields for economically viable variable rate technology (VRT) and the spatial variability required for maximum return to VRT. An example illustrated that return to VRT and the viable range of spatial...

The use of meta-response functions based on EPIC-generated data resulted in comparisons between variable (VRAT) and uniform rate application technologies for 36 simulated fields. VRAT was more profitable and less nitrogen was lost to the environment in most cases. When spatial variability was...

The paper develops a mathematical programming model for assessing the impact of Environmental Policy instruments on French winegrowing farm’s adoption of pesticides-saving technologies. We model choices with regards to investment in precision farming and plant protection practices, in a...

Contractors will play a vital role in providing farms with access to new precision farming technologies, especially in small-scale farming systems. We investigate the impact of spatial competition among contractors on the uptake of precision farming and the effectiveness of policy interventions,...

The adoption of innovations may boost the transition to sustainable agricultural models. Among these innovations, precision farming offers a fundamental contribution to sustainable soil management and the improvement in product quality. The work is set against this background and aims to analyse...

The goal of this paper is to develop a deep learning model for predicting citrus yield. The data used consists of two sources: (1) field data that includes information on fertilization and phytosanitary treatment products, water quantities used for irrigation, climatic data (temperature,...