Abstract

Increasing nutrient use efficiency on farms; improving land management; changing land use to capture more carbon, along with boosting renewable energy and the wider bio economy are practices that have been identified as key mechanisms by which the ambitious goal of achieving carbon net zero by 2050 can be achieved [1,2]. In order to increase nutrient use efficiency on farms, it requires knowledge and data collection to manage inputs, outputs, emissions and productivity. Soil and crop sensors can play an important role in improving the precision of agricultural practices while minimising harmful emissions to the environment. Rapid advances in technology mean that today there are many soil and crop sensors which provide a fast, powerful, non-destructive means of measuring a large number of chemical and physical properties. However, disentangling the data provided by soil and crop sensors can often be a challenge, particularly as some sensors and proximal sensor systems can be good proxies for more than one soil property. While it is possible to create very accurate and detailed soil maps using proximal sensors, there is nearly always a requirement to calibrate with local samples, as multiple factors can affect sensor measurements [3]. Good processing and calibration are key, and the best results will be achieved when there is a wide variation of in-field properties [4]. This mini review identifies two important case examples where proximal sensors can improve land management and farm nutrient use efficiency, which are both important concepts towards carbon net zero.