Precision farming advances are providing opportunities in both production agriculture and agricultural research. For growers and agronomists, the benefits of identifying where crops are stressed, the location of weeds and estimating yields on a large scale are clear. Researchers, who have different needs, can benefit from a detailed focus on a specific characteristic, such as one disease (e.g. yellow rust). This paper will review how recent advances in technology are beginning to allow the development of specialised tools within research and agriculture and how current precision agriculture tools can be effective at measuring desirable traits.

The precise application of pesticides to fruit crops requires information regarding the tree or vine canopy as a system input in order to control the amount of liquid and air being applied. Variations in canopy volume and density occur due to variety, trellis system, growth stage, training system and season. Current practice is to occasionally change liquid volume but seldom to change airflow. This paper details the development and validation of an ultrasonic sensor system to measure not only canopy volume but also canopy density and presence. Sensors fitted to the sprayer can record, in real time, changes in crop characteristics as the sprayer moves along the row. Signals can then send information to variable output nozzles and adjustable air fans. Trials have been conducted and results have proven to be extremely reliable and accurate. The ability to precisely control the spray results in the optimum application rate, leading to better results in the use of pesticides, less environmental pollution (less drift and less leaf runoff) and improved economic viability for the fruit grower.

Advances in agricultural machinery, information and sensor technology have led to an increasing amount of data that is available spatially both pre and within season. The case is compelling for the spatialisation of existing, non-spatial (field-scale) crop models that can accommodate this ‘big data’ and lead to more precise predictions of yield and quality and an improved field management. This study explores the conceptual spatial models based on the potato crop models that simulate crop physical and physiological processes and predict yields and graded yields at a field-scale. Through exploring the possible spatial scales and model application approaches considering spatial variation an optimal and more effective solution is expected. Issues concerning model quality and uncertainty are also discussed.

The major histocompatibility complex region has been implicated in explaining some of the variation observed in adaptability and tick susceptibility of cattle. The bovine leukocyte antigen region of 192 cattle representing indigenous, composite and exotic breeds used in commercial beef production in Namibia and South Africa was investigated using four microsatellite markers. Ticks counted under the tail were taken as an indicator of tick susceptibility. Tick scores of all but one population was low (11 to 20 ticks), with only the South African Bonsmara population having an average score of 31 to 40 ticks per animal. The observed variation based on four microsatellite markers ranged from 5.5 alleles in Namibian Afrikaner to 7.7 alleles in South African Nguni and Bonsmara cattle. Unbiased heterozygosity values ranged from 0.66 (Namibian Afrikaner) to 0.76 (South African Bonsmara). Structure analyses grouped the five populations into three indistinct clusters with limited genetic variation between the populations.

Peanut (Arachis Hypogea) is one of the few major agronomic crops for which a yield monitor is not commercially available. This paper describes an ongoing project whose long-term goal is to adapt an optical sensor originally developed for cotton yield monitoring for use as a peanut yield monitor (PYM). The immediate objective of the work reported here was to evaluate the PYM under harvest conditions typical in southern Georgia, USA. The PYM consists of two mass-flow sensors, a data acquisition system, and a DGPS receiver. The PYM was evaluated on three fields totaling 29 ha during the 2016 harvest season. Percent error between the scale load and calculated load was 2% or better for the first field tested, but increased greatly for subsequent fields that were tested, most likely caused by damage to the sensor lens from the impact of pebbles.

Great technological advances have been made in Precision Agriculture (PA) in the past decade, yet adoption of PA in intensive grassland areas in North West Europe is low. This is despite the fact that in these areas the market structures are suitable and there are highly developed agricultural and food industries offering great potential for the application of new technology. Specific inefficiencies in plant nutrient management in soil exist, which are not only limiting grass yields but are also causing environmental deterioration. Soil nutrient management efficiency could be greatly improved using PA techniques, but the complexity of grassland systems, coupled with a lack of calibration of sensors specific to grassland, together with local barriers, appear to be the reasons why PA adoption is poor in these areas. This paper reviews new and existing technology including soil and crop sensors, navigation devices, remote sensing and unmanned aerial vehicles. The suitability and readiness of these technologies for adoption in grassland areas is discussed, along with data interpretation issues, future perspectives and research opportunities.

The use of plant growth regulators (PGRs) on winter wheat in the UK is a common practice. Significant yield benefits can be gained from its use. For the most efficient use of PGRs the rates of application must be matched to the crop canopy. The variation of crop growth was monitored during the spring growing season using vegetation indices derived from satellite imagery. Application rates of PGRs were matched to changes in biomass as measured by the imagery. Final yields were measured using a GPS combine yield meter. The variable application showed an average yield benefit of 0.4 t/ha over the uniform full recommended application rate.