Weather and climate have a fundamental impact on plant development. Monitoring key observables, e.g. temperature and precipitation, is paramount for the interpretation of agricultural experiments and simulation of plant development. Whereas the presence of appropriate sensors in a research environment can be expected, the situation can be different in commercial agricultural settings. Local air temperature from online weather forecasts is investigated as a substitute for local weather station data. Hourly air temperature forecast and station data for several locations in Scotland and North East England are aggregated into daily air temperature values spanning a period of several months. Dates for key growth stages using temperatures from weather stations and weather forecast data are compared. For the examples discussed here the date differences in modelled key growth stages did not exceed 3 days indicating that temperature forecast data is suitable for farm-specific applications.

Pasture-based livestock systems, often located in High Value Nature farmland areas, hold the greatest potential to deliver public goods across European agricultural systems. They play an important role in preserving agricultural landscapes, farmland biodiversity, cultural heritage, and in sustaining rural development. However, many of these functions are ignored in evaluation frameworks because public goods do not have market price and are often ignored in policy design, so farmers do not get the appropriate incentives to provide them. Different conceptual frameworks can be utilized to evaluate the multiple functions or services of these systems: Multifunctional Agriculture, Ecosystem Services, and Total Economic Value. We analyze the common characteristics of these concepts (e.g. they place human benefits and societal demands at the core of their definitions), their specificities (e.g. use of different units of analysis and spatial-temporal scales), and how they can be embedded in the wider concept of sustainability. Finally, we illustrate how the different concepts can be combined to evaluate pasture-based livestock farming systems from a socio-cultural and economic perspective. The public goods (ecosystem services) provided by representative case studies in Mediterranean and Nordic regions are quantified (also in monetary terms) under different environmental/policy scenarios. The results show that there is a clear underestimation of the socio-cultural and economic values of ecosystem services provided by these farming systems. They also show that the social welfare loss linked to further abandonment of livestock farming, and the associated environmental degradation, is very large. From a societal perspective, it is necessary to jointly measure the biophysical, socio-cultural and monetary values of ecosystem services (market and nonmarket) in order to promote the sustainability of pasture-based livestock systems.

Assessment of Tar Spot Complex (TSC) severity in maize breeding experiments is conducted visually and may sometimes result in inconsistencies due to human interpretation. Disease scoring using remote sensing technologies may help bring more precision to the phenotyping process. An experiment for assessment of grain yield losses due to TSC was conducted at the Aguafria Experimental Station of the International Center for Wheat and Maize Improvement – CIMMYT in Mexico. Twenty-five maize genotypes were planted in spring of 2016 under a fungicide treatment to control TSC development and no fungicide treatment in a square lattice design with three replications. Four flights were carried out using an Unmanned Aerial Vehicle (UAV) equipped with a multispectral (550, 660, 735, 790 nm) and a thermal camera, simultaneously with the visual disease scorings and the yield was measured after harvesting. The preliminary results of the study indicated that the use of remote sensing in disease resistance phenotyping may be as effective as visual disease scoring since both correlate highly with the grain yield. Structural and chlorophyll vegetation indices (VIs) proved to be a good alternative for the estimation of yield losses caused by TSC in experimental field conditions, which may be potentially used for screening for resistance to this disease in maize genotypes, hypothetically reducing the need for visual disease scoring in the field.

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.

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.

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.

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.