Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.

Harvest mechanization in sugarcane results in an intense vehicle traffic inside the crop areas. When using transshipment trailers, keeping them in the correct path is not simple. The aim of this study was to evaluate the error path of a set trailered with and without the use of an automatic steering system during sugarcane harvesting. We used a combination of a tractor and two transshipment trailers with three axles each. The results show that the errors of the transshipments are above the acceptable and the use of automatic steering on the tractor minimizes offset errors in the transshipments trajectory and the slope of the terrain is a factor that interferes with the displacement as a whole. Despite the use of automatic steering systems in the auxiliary tractor to minimize the errors suffered by transshipments, there is a need for active systems linked to these to be maintained in the correct route.

The probabilistic Rasch model is used to get objective measures of production potential at some locations of an olive orchard located in Badajoz, southwestern Spain. Nine soil properties (soil apparent electrical conductivity, clay, sand, and silt content, organic matter, total nitrogen, available phosphorous and potassium, and cation exchange capacity), taken at 40 locations in the field, were considered and, after being integrated in the model, a ranking of all locations according to the soil production potential and the influence on the production potential of each individual soil property were determined. Moreover, those soil samples or properties which had any anomaly where highlighted; this information can be necessary to conduct site-specific treatments, leading to a more cost-effective and sustainable field management. Additionally, estimates using geostatistical algorithms were utilised to map soil production potential and to delineate with a rational basis the management zones in the field.

Infield route planning is used to optimise field operations in order to decrease operational costs and environmental impacts. Route planners must be able to plan operations within real fields and account for real situations such as irregular shapes and obstacles. Therefore, a representative set of fields is required to robustly test the route planner. Instead of choosing randomly, which may result in a non-representative sample of the diversity of fields; a stratification strategy was used to separate the field dataset into strata. Proportional sampling from each stratum provided a representative sample of 217 fields, out of the original set of 603,218 from the Danish field database.

A classical approach in precision agriculture consists in validating within field zones defined from high spatial resolution observations by agronomic information (AI). Zones validation generally involves a two-step process. First, AI are obtained on a regular grid or following a target sampling strategy inside the field. Then, a statistical test, most often an ANOVA, is used to determine if the management zones created with the high spatial resolution auxiliary data explain differences in the AI values. Unfortunately, in precision agriculture, many of the works using such an approach omit a necessary condition for the implementation of the aforementioned ANOVA test, i.e. the observations need to be independent from each other. This condition is unfortunately seldom satisfied since AI are often spatially auto-correlated. In order to highlight this problem, simulated datasets with different and known AI spatial autocorrelation were used. Results show that as AI are more and more spatially auto-correlated, ANOVA tests almost always conclude that the management zones obtained with auxiliary data are significant whatever the zoning, i.e. even a completely random one. To overcome this problem, the paper introduces two methods directly inspired from published works in the field of ecology. Two cases were considered: the first one applies when large AI dataset (n>40) is available and the other one applies for small AI dataset (n<40). Both methods are implemented on a real precision viticulture example.

This paper presents a framework for the implementation of a decision support system that considers spatial, temporal and managerial factors in assessing the potential impact of crop amendments on the cost of a given production scenario. The proposed system includes a database and a numeric simulation model. The database is linked to previously recorded crop responses for a given agricultural input under different conditions while the numeric simulation model determines the probability of different levels of profit for each decision option. This system then determines the optimal uniform rate of application of an amendment to maximize profits, or to define the range of such rates for a case of variable rate application. Uncertainty-based treatment of each model input allows for a balance between the potential results of under-application or over-application.

Potential yield is one of the criteria used as an input to nitrogen (N) fertilizer management decisions when using SIX EASY STEPS (6ES), the fertilizer recommendation tool used in the Australian sugar industry. Most commonly, 6ES is implemented using a district yield potential (DYP). In this study, we use analysis of sugar mill and yield monitor data from the Herbert River cane growing district to demonstrate that yield is markedly spatially variable, with this variability following the same patterns from year to year. There would therefore be value in a more location specific consideration of potential yield and application of 6ES. Similar analyses could be readily conducted in other sugar producing regions with potentially important implications for fertilizer use efficiency and the minimization of nutrient accessions to the Great Barrier Reef.

This research has multidisciplinary characteristics with a focus on cotton fiber production and computational solutions to improved data exchange. The research is divided into three parts, the identification of the cotton fiber production processes, the formal ontology for identifying the data classes, and finally the proposal of a specific metadata standard for cotton fiber production. The absence of a specific standard for this segment favors the heterogeneity in the various data sources. The contribution of the research lies in improving the information exchange used in agricultural systems providing identification of each individual responsible for steps in the cotton production chain.

Aflatoxin contamination of food can cause liver cancer in humans and animals. Identification of aflatoxin risk areas allows farmers to adapt management strategies before planting, during growth and at harvest. Aflatoxin contamination is driven by high temperatures and drought conditions and crops grown on light textured soil in the south eastern USA are at particular risk. Aflatoxin assessment is expensive so a role of extension services in precision farming is to identify the areas most at risk of contamination so that farmers can adapt irrigation or planting strategies. This paper extends a county-level risk factors approach developed by Kerry et al. (2017) by investigating the use of NDVI and thermal IR data to indicate drought stress and thus aflatoxin contamination risk at the sub-county level.

Farm equipment, including sensors and mobile machinery, create increasing amounts of data, and data can also be gained from third-party services. In order to be able to fully take advantage of this a farmer needs to be able to gather, store, process, and share the data as needed. In this work we describe a prototype for open environment that can gather, combine, store, select, and share data from arbitrary sources and with external partners, as well as use the data in decision making and provide it as input for various services. The environment is built using the Service Oriented Architecture paradigm and is therefore not tied to any specific operating system or software framework. We have tested the environment on the farm scale in Finland. The system was found suitable to improve the work in all tested tasks.

This paper is focusing on how to eliminate the required time for importing all the necessary data into a farm management information system (FMIS). This process was automated by using ISO 11783 and SAE J1939 communication information from the tractor’s CAN-Bus. Using a data logger and a machine to machine (M2M) gateway inside the tractor’s cabin, CAN-Bus data were recorded and transmitted to the cloud-based server of the FMIS. There, a script was responsible for parsing and aggregating the raw machine data into specific agricultural tasks and then importing them into the FMIS. The operator could choose the type of the performed task by a number of switches connected with the digital inputs of the data logger.

This paper discusses a conceptual design of a Ag Data service for the farm industry, compares it to desktops FMIS and discusses some of the main concepts this kind of system may include. Beginning with an introduction to the current situation and how the amount and size of the data is affecting the capacity to process it efficiently, on a personal computer desktop or other devices. Following with a description of the characteristics and components, presenting a case study to demonstrate the way it may function within a farm environment.

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.