Spyros Fountas

Farm management information systems

Farm management information systems centered around the farm manager in open-field crop production.Prevailing differences between academic and commercial farm management information systems.Grouping of farm management information systems based on cluster analysis. Farm Management Information Systems (FMIS) in agriculture have evolved from simple farm recordkeeping into sophisticated and complex systems to support production management. The purpose of current FMIS is to meet the increased demands to reduce production costs, comply with agricultural standards, and maintain high product quality and safety. This paper presents current advancements in the functionality of academic and commercial FMIS. The study focuses on open-field crop production and centeres on farm managers as the primary users and decision makers. Core system architectures and application domains, adoption and profitability, and FMIS solutions for precision agriculture as the most information-intensive application area were analyzed. Our review of commercial solutions involved the analysis of 141 international software packages, categorized into 11 functions. Cluster analysis was used to group current commercial FMIS as well as examine possible avenues for further development. Academic FMIS involved more sophisticated systems covering compliance to standards applications, automated data capture as well as interoperability between different software packages. Conversely, commercial FMIS applications targeted everyday farm office tasks related to budgeting and finance, such as recordkeeping, machinery management, and documentation, with emerging trends showing new functions related to traceability, quality assurance and sales.

Farm machinery management information system

Graphical abstractDisplay Omitted Outline concerns for the use of information generated and pertaining to the tractor and the farm implements.Specify the pathways for the organization of data generated by the tractor ISOBUS protocol.Facilitate further development of required sensors, communication technology, and information processing capabilities. Management of farming operations is currently rapidly changing toward a systems perspective integrating the surroundings in terms of environmental impact, public entities and documentation of quality and growing conditions. The latest developments in Information and Communication Technologies (ICT) and the prevailing lack of interoperability between agricultural tractors, implements and on-board computers has led to the development of ISO 11783 (ISOBUS) international standard for securing a more effective communication between these entities. Precision agriculture requires an increasing amount of information in order to be sufficiently managed and the abilities of the ISOBUS protocol is a significant step toward this goal as it will provide a wealth of automated data acquisition for improving the management of crop production. However, there is an urgent need to organize and specify the pathways of this large amount of information as prerequisites for subsequently turning it into knowledge and decision support. The aim of this study was to analyze and design a future farm machinery management information system to handle tractor and implement data together with the interactions with their surroundings. Soft systems methodology was used to analyze the human activities and to identify user requirements in relation to the use of farm machinery and the management of the information generated and pertaining to the tractor and the farm implements. The empirical data to extract this information was gathered from 30 targeted interviews with tractor operators and farm managers located in Greece and Denmark, and pertaining to questions about the optimal use of farm machinery data and tractor-implement performance. A rich picture of the whole system was developed and from that a conceptual model that infers to daily operations with the tractor, implement and the interactions with the surroundings. The conceptual models were developed for both conventional farm machinery and agricultural robots. The conceptual model function will serve as a blueprint for further development of required sensors, communication technology, and information processing capabilities. The developed conceptual models were tested and validated with 15 farm managers from the initial reviewing panel in order to reveal supplemental additions and concerns.

A fuzzy inference system to model grape quality in vineyards

Fuzzy inference systems (FIS) are particularly suited for aggregating multiple data to feed multi-variables decision support systems. Moreover, grape quality is a complex concept that refers to the simultaneous achievement of optimal levels in many parameters, thus single berry attributes spatial data are not adequate to define grape suitability for a specific end use. The aim of the present study was to develop and validate a FIS to classify grape quality based on selected grape attributes in a commercial vineyard in Central Greece planted with Vitis vinifera cv. Agiorgitiko, during 2010, 2011 and 2012. The vineyard was sectioned in 48 cells sized 10xa0×xa020xa0m; total soluble solids, titratable acidity, total skin anthocyanins and berry fresh weight were measured at harvest on the same grid and were used in the FIS as inputs to build linguistic rules based on expert knowledge. The result of the FIS was a numerical value (Grape Total Quality, GTQ) which corresponded to a fuzzy set of grape quality classes (very poor, poor, average, good, and excellent). The validation process for the proposed FIS consisted of two parts: a comparison of GTQ with an independent set of data by viticulture experts and a comparison with soil and grapevine properties to verify its spatial relevancy. The evaluation process showed high general agreement between GTQ and expert evaluation suggesting that the FIS was able to model expert knowledge successfully. Moreover, GTQ exhibited higher variability than the individual grape quality attributes in all years. Among individual grape components, anthocyanins and berry weight seemed to be more important in determining GTQ than total soluble solids and titratable acidity. According to the results, FIS could allow the aggregation of grape quality parameters into a single index providing grape growers with a valuable tool for classifying grape quality at harvest.

Performance and Emissions of Sunflower, Rapeseed, and Cottonseed Oils as Fuels in an Agricultural Tractor Engine

A comparative experimental investigation was conducted to evaluate the performance and exhaust emissions of an agricultural tractor engine when fueled with sunflower oil, rapeseed oil, and cottonseed oil and their blends with diesel fuel (20/80, 40/60 and 70/30 volumetrically). Tests were also carried out with diesel fuel to be used as a reference point. Engine power, torque, BSFC, thermal efficiency, NOx and CO2 were recorded for each tested fuel. All vegetable oils resulted in normal operation without problems during the short-term experiments. The 20/80 blends showed unstable results, in comparison to higher oil content fuels. Power, Torque and BSFC were higher as oil content was increased in the fuel. Rapeseed oil fuels showed increased power, torque and thermal efficiency with simultaneous lower BSFC in comparison to the other two vegetable oils. Cottonseed oil fuels gave better engine performance than sunflower oil fuels. In all oil types, NOx emissions were augmented when fuel oil percentage was increased. Cottonseed oil fuels led to higher NOx emission increase compared to rapeseed oil fuels. CO2 emissions showed a tendency to be increased as the oil content was evolved. The highest CO2 emissions were given by cottonseed oil fuels, followed by rapeseed and sunflower oil.

Sunflower Oil Fuel for Diesel Engines: An Experimental Investigation and Optimum Engine Setting Evaluation Using a Multi-Criteria Decision Making Approach

An experimental investigation on a diesel engine was conducted to evaluate the performance and exhaust emissions of sunflower oil and three blends with diesel fuel (20, 40, and 70% oil content volumetrically) in comparison to diesel fuel. Three injection timing and three injector protrusion settings were tested to study engine performance and exhaust emissions. The work was conducted in a direct injection agricultural tractor engine. Engine operation with sunflower oil-based fuels was unproblematic during the short-term experiments. Torque, brake-specific fuel consumption and NOx were enhanced as oil content was increased in the tested fuel. Early injection timing improved torque output, reduced BSFC, increased thermal efficiency, and NOx emissions. Deep injector protrusion increased torque release in low oil content fuels and shallow injector protrusion and increased torque in high oil content fuels. The experimental results were evaluated using two multi-criteria decision-making techniques (Analytical Hierarchy Process-AHP and Technique for Order Preference by Similarity to the Ideal Solution-TOPSIS) and the optimal fuel type-injection timing-injector protrusion configuration was selected. AHP and TOPSIS were run for three groups of criteria that were focusing on higher engine performance, lower environmental impact, and a balance between the first two, respectively. The results of the two techniques were compared. AHP and TOPSIS gave the same attribute ranking in all three groups of criteria, but did not give the same classification of Fuel/Injection Timing/Injector Protrusion configuration. The 70% oil content blend was selected from both techniques as optimal in the examined groups of criteria.

Evaluation of the use of LIDAR laser scanner to map pruning wood in vineyards and its potential for management zones delineation

Vine vigour assessment has been a major concern of precision viticulture studies in order to identify areas of uniform vine performance within vineyards. Moreover, the counting and weighing of winter dormant canes is considered as the most informative measurement to indicate vine balance and is commonly performed manually by grape growers for management purposes. The main concern of this measurement is that it is time consuming and laborious and it cannot accommodate detailed sampling density. In the present study, the potential of using laser scanner technology as an automated, easy and rapid way to perform mapping of the winter pruning wood across the vineyard was investigated. The study was conducted during 2010 and 2011, in a one hectare commercial vineyard in central Greece, planted with cv. Agiorgitiko, a traditional Greek variety for the production of red wine. Parameters of topography, soil depth, soil texture, canopy properties (NDVI), yield, and grape quality were mapped and analysed in conjunction to winter canes weighing at pruning time. The mapping of the dormant canes was carried out using a 2D laser scanner sensor prior to pruning and manually measuring the pruning weight on a 10xa0×xa020xa0m grid. Laser scanner measurements showed significant relationship in both 2010 and 2011 with pruning weight (rxa0=xa00.809 and rxa0=xa00.829 respectively, pxa0<xa00.001), yield and early season NDVI, showing the potential of using laser scanner measurements to assess variability in vine vigour within vineyards. These results suggest that laser scanners offer great promise to characterize within field variability in vine performance.

Robotic Seeding: Economic Perspectives

Agricultural robotics has received attention for approximately 20 years, but today there are only a few examples of the application of robots in agricultural practice. The lack of uptake may be (at least partly) because in many cases there is either no compelling economic benefit, or there is a benefit but it is not recognized. The aim of this chapter is to quantify the economic benefits from the application of agricultural robots under a specific condition where such a benefit is assumed to exist, namely the case of early seeding and re-seeding in sugar beet. With some predefined assumptions with regard to speed, capacity and seed mapping, we found that among these two technical systems both early seeding with a small robot and re-seeding using a robot for a smaller part of the field appear to be financially viable solutions in sugar beet production.

Smart Farming Technologies – Description, Taxonomy and Economic Impact

Precision Agriculture is a cyclic optimization process where data have to be collected from the field, analysed and evaluated and finally used for decision making for site-specific management of the field. Smart farming technologies (SFT ) cover all these aspects of precision agriculture and can be categorized in data acquisition, data analysis and evaluation and precision application technologies. Data acquisition technologies include GNSS technologies, mapping technologies, data acquisition of environmental properties and machines and their properties. Data analysis and evaluation technologies comprise the delineation of management zones, decision support systems and farm management information system s. Finally, precision application technologies embrace variable-rate application technologies, precision irrigation and weeding and machine guidance. In this chapter, the reader can find a technical description of the technologies included in each category accompanied by a taxonomy of all SFT in terms of farming system type, cropping system, availability, level of investment and farmers’ motives to adopt them. Finally, the economic impact that each SFT has compared to conventional agricultural practices is given.

Future Perspectives of Farm Management Information Systems

Farm Management Information Systems (FMIS) have evolved from simple record keeping to sophisticated solutions able to capture new trends involving spatial and temporal management, distributed sensors involving interoperability of sensing devices, future internet applications and web services. The FMIS were initially designed to deal with the farmer as the main focus of the system, whereas now data flow from and to the tractor information board, and connections with other pieces of equipment such as precision agriculture devices can be managed through an FMIS. This pathway of evolution has led to the inclusion of a rich set of functionalities and opened up the possibility to improve the cost control of farms. In this chapter, we present the state-of-the-art on these topics depicting the new functionalities included in evolved FMIS and how they can connect the farm to the external context and stakeholders. Then, we delve into the costing functionality of FMIS to understand how precision agriculture can improve the allocation of costs to final products. Finally, we conclude our discussion on the process of adoption of FMIS in European farms.