Recent technological progress in high-speed planting (HSP) warrants economic analysis of its potential. A whole farm optimization model of a 1000 ha Kentucky, USA corn and soybean operation finds that operating cost savings (labor, fuel, tractor repairs) and yield increases couple in recovering annual ownership costs of HSP technology. Changes in farm net returns are positive for all 12-row planter scenarios and all double speed cases for the 16-row planter but not for a 50% increase in speed with the 16-row planter. The greatest profit potential occurred when adopting the combination of HSP and variable rate application (VRA), with increased net returns of up to 6.57% compared to conventional speed no VRA for the 12-row planter.

A whole farm economic analysis was performed to maximize net returns utilizing variable maturity groups of corn and soybeans over different soil types. Demand for drying and storage equipment throughout harvest was generated based on profit-maximizing combinations of grain types, their respective maturity groups, and yield potential over different topsoil depths. Two marketing strategies were considered: cash and futures contract sales. It was found that drying equipment became a limiting factor in the proposed system. This prevented storage facilities from reaching full capacity and additional grain from capturing value in the futures market.

Inversing the STICS crop model with remote-sensing-derived leaf area index (LAI) and yield data from the previous crop is used to retrieve some soil permanent properties and crop emergence parameters. Spatialized nitrogen (N) fertilization recommendations are provided to farmers, for the second and third N applications, following the screening of eleven N application rates under a range of possible forthcoming climates, with the objective to maximize of the gross margin while respecting some environmental constraints. As a first field validation, we show (1) the improvement brought by the assimilation of LAI and yield into STICS to simulate crop and soil variables and (2) the interest of site specific application to maximize both the gross margin and the agro-environmental criterion.

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.

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.

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

The Society of Precision Agriculture Australia Inc. (SPAA) is recognised as a leading, grower driven farming group in Australia. As an organisation it provides programs and services to its members and wider industry to promote the development and adoption of Precision Agriculture (PA) technologies as a means of enhancing the profitability and sustainability of agricultural production systems. This is achieved through publishing Australia’s only PA-dedicated magazine, delivering field days, seminars and conducting on-farm PA demonstrations and experiments. SPAA provides farmers with an independent source of advice on new concepts and equipment. The grains industry was the springboard for initial adoption, with winegrapes, horticulture and the sugar industry the focus sectors for further expansion. The purpose of this paper is to share the SPAA experience with a view to assisting the development of similar organisations in other countries

The establishment of the system Soil – Yield that occured in Latvia during the 1970–80s could be considered as the beginning of precision farming with the available technologies. The first precision farming technologies have been associated with harvesting where combines and tractors with the automatic steering were used. The precision agriculture in Latvia includes various branches. Latvia farmers are using precision crop farming, precision livestock farming, precision fruit growing, precision bee keeping, precision farming greenhouse and precision growing berries. Precision farming technologies in Latvia are introduced mainly in large scale farms, with more than 1000 ha. The most important researches in precision farming in Latvia were done in 2000s.

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.