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Timely and accurate monitoring of pasture biomass and ground cover isnecessary in livestock production systems to ensure productive andsustainable management. Interest in the use of proximal sensors formonitoring pasture status in grazing systems has increased, since data can bereturned in near real time. Proximal sensors have the potential fordeployment on large properties where remote sensing may not be suitable dueto issues such as spatial scale or cloud cover. There are unresolvedchallenges in gathering reliable sensor data and in calibrating raw sensordata to values such as pasture biomass or vegetation ground cover, whichallow meaningful interpretation of sensor data by livestock producers.  Our goal was to assess whether a combination of proximal sensors could bereliably deployed to monitor tropical pasture status in an operational beefproduction system, as a precursor to designing a full sensor deployment. Weuse this pilot project to (1) illustrate practical issues around sensordeployment, (2) develop the methods necessary for the quality control of thesensor data, and (3) assess the strength of the relationships betweenvegetation indices derived from the proximal sensors and field observationsacross the wet and dry seasons.    Proximal sensors were deployed at two sites in a tropical pasture on a beefproduction property near Townsville, Australia. Each site was monitored by aSkye SKR-four-band multispectral sensor (every 1 min), a digital camera(every 30 min), and a soil moisture sensor (every 1 min), each of which were operatedover 18 months. Raw data from each sensor was processed to calculatemultispectral vegetation indices. The data capture from the digital cameraswas more reliable than the multispectral sensors, which had up to 67 % ofdata discarded after data cleaning and quality control for technical issuesrelated to the sensor design, as well as environmental issues such as waterincursion and insect infestations. We recommend having a system with bothsensor types to aid in data interpretation and troubleshooting technicalissues. Non-destructive observations of pasture characteristics, includingabove-ground standing biomass and fractional ground cover, were made every2 weeks. This simplified data collection was designed for multiple years ofsampling at the remote site, but had the disadvantage of high measurementuncertainty.  A bootstrapping method was used to explore the strength of the relationshipsbetween sensor and pasture observations. Due to the uncertainty in the fieldobservations, the relationships between sensor and field data are notconfirmational and should be used only to inform the design of future work.We found the strongest relationships occurred during the wet season period ofmaximum pasture growth (January to April), with generally poor relationshipsoutside of this period. Strong relationships were found with multispectralindices that were sensitive to the green and dry components of thevegetation, such as those containing the band in the lower shortwave infrared(SWIR) region of the electromagnetic spectrum. During the wet season thebias-adjusted bootstrap point estimate of the <i>R</i><sup>2</sup> between above-groundbiomass and the normalized ratio between the SWIR and red bands (NVI-SR) was0.72 (95 % CI of 0.28 to 0.98), while that for the percentage of greenvegetation observed in three dimensions and a simple ratio between the nearinfrared and SWIR bands (RatioNS34) was 0.81 (95 % CI of 0.53 to 1.00).Relationships between field data and the vegetation index derived from thedigital camera images were generally weaker than from the multispectralsensor data, except for green vegetation observations in two and threedimensions.  Our successful pilot of multiple proximal sensors supports the design offuture deployments in tropical pastures and their potential for operationaluse. The stringent rules we developed for data cleaning can be more broadlyapplied to other sensor projects to ensure quality data. Although proximalsensors observe only a small area of the pasture, they deliver continual andtimely pasture measurements to inform timely on-farm decision-making

A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures