Precision agriculture (PA) may improve the sustainability of Chinese agriculture. Ten experts were interviewed and 34 farm workers surveyed regarding their understanding, attitudes and perceptions towards PA. PA technologies were considered inaccessible, unsuitable and unnecessary for smaller farms. High cost, lack of perceived benefits, and skills and capability required to adopt PA represented barriers to adoption. Financial incentives/subsidies, the need for tangible benefits and tailored solutions to be demonstrated to farmers, and agronomic and peer support were desired. Future research should further explore PA with Chinese stakeholders and end-users in China, to inform future socio-technological developments.

Precision agriculture technologies have been adopted individually and in bundles. A sample of 348 Kansas Farm Management Association farm-level observations provides insight into technology adoption patterns of precision agriculture technologies. Estimated transition probabilities shed light on how adoption paths lead to bundling of technologies. Three information intensive technologies were assigned to one of eight possible bundles, and the sequence of adoption was examined using Markov transition processes. The probability that farms remain with the same bundle or transition to a different bundle by the next time period are reported. Farms with the complete bundle of all three technologies were likely to persist with their current technology.

This paper presents schematic review for the smart agricultural model in Japan using data-on-demand information exchange based on smart agricultural machinery systems (SAMS). Four machines were developed in this study, namely Smart rice trans-planter with on-the-go soil sensor; Smart 2nd fertilizer applicator based on CropSpecTM; Yield monitor combine harvester with on-the-go lodging analysis system; and Farm Activity Record Management System (FARMS). The study obtained 450,000 datasets of topsoil accompanied by 65,000 datasets of crop status and 1 million images of lodging information from 50 ha of rice fields, taken in 2016. The results conclude that the field mapping using FARMS was available not only for manager’s decision on fertilizer application, but also for information sharing between employees. A two year feasibility study showed improvement of 20% fertilizer reduction and 30% harvest efficiency than conventional management. The study suggests that SAMS would play an important role for technology succession in the near future.

Aflatoxin is a fungal toxin contaminating corn and causing liver cancer in humans and animals. Contamination is driven by high temperatures and drought. Aflatoxin assessment is expensive so extension services need to identify high risk areas so irrigation, planting strategies and corn varieties can be adapted. This research presents a web-based decision support tool for risk illustrated with a case study from southern Georgia. The tool employs the approach, developed by Kerry et al. (2017b) where exceedance of key thresholds in temperatures, rainfall, soil type and corn production are used to determine risk. The tool also includes NDVI to indicate drought stress and could be further expanded to include new risk factors and adapted to other crops.

Producers are often faced with information from industry touting a generic return on investment of precision agriculture technologies that are inflated through the use of simple techniques that ignore the time value of money. The economics of precision technologies are as site-specific as the technology. Therefore, educating producers (and agribusiness) on how to determine the return on investment by following the correct investment analysis techniques within a proper decision-making framework helps ensure an accurate site-specific return on investment for precision agriculture technologies.

Good progress in crop husbandry and science requires that impacts of field-scale interventions can be measured, analysed and interpreted easily and with confidence. The term ‘agronōmics’ describes the arena for research created by field-scale digital technologies where these technologies can enable effective commercially relevant experimentation. Ongoing trials with ‘precision-farm research networks’, along with new statistical methods (and associated software), show that robust conclusions can be drawn from digital field-scale comparisons, but they also show significant scope for improvement in the validity, accuracy and precision of digital measurements, especially those determining crop yields.

A view into future requires maybe a new approach of current arable production systems. So far crop production systems have been adapted to the machinery available on the market. New technological progresses in the range of precision and digital farming show the possibilities meeting the requirements of plants even more small-spatial and efficiently. Especially small autonomous machinery may offer opportunities achieving sustainable intensification in crop farming. Different crop productions and cultivation systems have to be devised, analyzed and assessed in this context.

The University of Georgia (USA) is partnering with the University of Padova (Italy) for a dual Master’s degree program in sustainable agriculture, promoting collaboration on some of the biggest challenges facing agriculture today. This innovative program which was launched during 2016 provides students with outstanding training and a unique opportunity to learn about the challenges, opportunities, and leading edges of precision agriculture on another continent – an experience which will serve graduates well when they enter the job market in an increasingly global economy. This paper presents the goals of the program, the curriculum, and describes the opportunities available to prospective students. In addition it describes the process of developing the dual degree which can be used as guide by others wishing to develop similar programs.

To explore the potential for site-specific crop management in Australian potato production, soil apparent electrical conductivity (ECa) and high resolution elevation data were used to first define the variation in soil and landscape resources in two regions in Tasmania. Variation in crop production was estimated using in-season aerial multispectral VIS-NIR reflectance measurements and then measured using a first generation on-harvester yield monitoring system. During the season, soil and crop physical, chemical and pathogen properties were measured to groundtruth the sensor-derived data. Substantial within-field and between field variation was found in soil physical, chemical and pathogen properties, elevation and crop yield. The average potato yield for the study fields was 64 t/ha, with over three-fold within-field variation recorded. The in-season aerial crop reflectance significantly correlated with soil physical variability and pathogen load when gathered early in the season and to variation in plant physical and chemical properties, as well as important soil nutrient properties and crop yield when gathered from week 14 onwards. A set of general rules for instigating Site-specific crop management (SSCM) in potato production has been devised based initially on nutrient and pathogen management with irrigation management as an option.

Controlled traffic farming (CTF) systems aim to reduce soil compaction by restricting machinery field traffic to permanent traffic lanes. Grass-clover silage production is generally associated with intensive field traffic, resulting in reduced silage clover content. If CTF can increase yield and clover content in grass-clover leys, this would reduce the need for grain and expensive protein concentrate in dairy cow feed rations. A mixed integer programming model was developed to evaluate the potential profitability of CTF in a dairy farm context. Existing field trial data were used to calculate the expected yield outcome of CTF, based on reductions in trafficked area. The results revealed that CTF increased profitability by up to €50/ha. Total machinery costs are likely to increase on converting to CTF, but variable machinery costs are likely to decrease.

We investigated relationship between pasture biomass and measures of height and NDVI (normalised difference vegetation index). The pastures were tall fescue (Festuca arundinacea), perennial ryegrass (Lolium perenne), and phalaris (Phalaris aquatica) located in Tasmania, Victoria and in the Northern Tablelands of NSW, Australia. Using the Trimble® GreenSeeker® Handheld active optical sensor (AOS) to measure NDVI, and a rising plate meter, the optimal model to estimate green dry biomass (GDM) during two years was a combination of NDVI and falling plate height index. The combined index was significantly correlated with GDM in each region during winter and spring (r2=0.62–0.77, P<0.001). Regional calibrations provided a smaller error in estimates of green biomass, required for potential application in the field, compared to a single overall calibration. Data collected in a third year will be used to test the accuracy of the models.

A ‘chessboard’ field experiment set up to investigate how the yield response to nitrogen (N) fertiliser varied spatially within a field in the UK indicated that the optimum N rate varied substantially by up to 100 kg N/ha within the three hectare experimental area. Variation in N optima was negatively related to the soil N supply. However, soil N supply, yield potential and apparent fertiliser recovery rate were inter-related which meant that the influence of each element on N optima was complex. Spectral reflectance indices related well to crop N uptake and could be used to help estimate soil N supply.

Biomass production is a diagnosis tool for the evaluation of the effect of climate, crop genomic and management. The differences in biomass accumulation are necessary for the assessment of the fertilization necessities in the strategies for variable nitrogen doses. Remote sensing-based data provide a direct observation of the differences in canopy development across time and space and can be integrated into the physiological basis of crop growth models to provide estimates of biomass production at fine scales. The proposed approach was applied in a wheat field in Albacete, Spain and the results were compared with measurements of aboveground biomass and yield maps obtained by a combined-mounted grain yield monitor.

In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.

Grassland silage management is generally ad hoc resulting in soil compaction damage. Literature suggests grass yield reductions of 5 to 74% through compaction (UK mean 13%), while a 2015 study, reported here, comparing grass dry matter (DM) yield between controlled traffic farming (CTF) and normal management (N), found a 13.5% (0.80 t ha−1) increase for CTF. Commercially available grass forage equipment with widths of 3 to 12 m set up for CTF reduced trafficked areas from 80%–90% for N to 40%–13%. Economic analysis based on 13% increase in DM for 2 and 3 cut systems, gave an increased grass value between £38 ha−1 and £98 ha−1. CTF for multi-cut grass silage effectively increases yields by reducing compaction and sward damage.

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.

Yield and landscape are commonly used to guide management zone delineation. However, production system choice and management can interact with landscape attributes and weather. The objective of this study was to evaluate forage yield and soil properties in three landscape defined (elevation based) management zones, and under two different grazing systems. Changes in soil properties (soil strength, bulk density, moisture, bioavailable nutrients) and forage productivity (biomass), as related to grazing management and management zone, were measured. Bulk density, moisture, and forage biomass were greater at higher elevation. Soil strength decreased as elevation increased, and was greater near-surface after winter grazing ended. The response of landscape delineated management zones varied with extreme weather conditions and treatment. Lower zones were more sensitive to weather extremes than higher elevations, directly affecting biomass accumulation. In conclusion, we observed interactions between the grazing treatments and the management zones.

This study aims to evaluate actual biomass and N-uptake estimates with the Yara N-Sensor in intensively managed grass swards across several trial sites in Europe. The dataset was split by location into an independent calibration data (UK and Finland) and a validation data (Germany) for the first two cuts. Yara N-Sensor readings were better correlated with N-uptake (R2=0.71) than actual biomass (R2=0.53) for the 1st cut. At the 2nd cut, the R2 values for both parameters were higher (0.80 and 0.56). A cross-validation with a German grass trial indicated the potential for predicting N-uptake (R2>0.8). It can be concluded that the technology has the potential to guide management decisions and variable rate nitrogen application on European grass swards.

The main objective of this work was to evaluate technologies that have potential for monitoring aspects related to spatial and temporal variability of soil nutrients and pasture yield and for support to decision making by the farmers. Three types of sensors were evaluated: an electromagnetic induction sensor, an active optical sensor and a capacitance probe. The results are relevant for the selection of the adequate sensing system for each particular application and to open new perspectives for other works that would allow the testing, calibration and validation of the sensors in a wider range of pasture production conditions and rainfall patterns, characteristic of the Mediterranean region.

As the interest in Unmanned Aerial Systems (UAS) has increased, so has the interest in the application of these systems for use in agriculture. A variety of sensors, including Multi-Spectral, Near-Infrared, Thermal, and True-Color have the potential to benefit farmers when mounted to a UAS. But as this is an emerging field, there is little data available to demonstrate their use for early detection of plant diseases in crop production. In 2016, a preliminary study was launched to examine the potential of using aerial imagery from UAS to detect diseases in soybean crops. Two irrigated fields in Alabama were selected: Experiment 1, a 50-hectare field, and Experiment 2, a 5-hectare field. Each trial consisted of replicated plots using two foliar fungicide treatments and an untreated control. Aerial imagery (multi-spectral and true-color) was collected on a biweekly basis during this study. Using multi-spectral imagery, both the Normalized Difference Vegetative Index (NDVI) and Normalized Difference Red Edge Index (NDRE) were generated and compared to direct observations in the field. Disease severity of soybean rust, charcoal rot and Cercospora leaf blight were monitored on a biweekly basis and correlated to the UAS imagery. Preliminary results indicated plant stress can be detected using UAS imagery. In Experiment 1, stress associated with charcoal rot was visible in the NDRE imagery. This was of interest because at the time of flight, while it was noted that plants were yellowing, the root and stem disease itself had not been identified by direct observation. In Experiment 2, soybean rust was observed by direct observation and in both the NDRE and NDVI imagery. Soybean rust did have a negative impact on yield in Experiment 2, however severe drought conditions may have negated the yield loss likely caused by the development of charcoal rot in Experiment 1.