Gender and innovation processes in rice-based systems

This GRiSP report is based on the perspectives of women and men from three rice-growing villages in the Nueva Ecija province of the Philippines

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

КОНЦЕПТУАЛЬНЫЕ ПРИНЦИПЫ ИНТЕЛЛЕКТУАЛЬНЫХ СЕЛЬСКОХОЗЯЙСТВЕННЫХ МАШИН НА ПРИМЕРЕ ЗЕРНОУБОРОЧНОГО КОМБАЙНА

Monitoring of different ways to increase the productivity of agricultural machinery under development of intelligent agricultural  production shown that traditional directions of increasing the  concentration of energy, sizes of working elements and running  speed almost reached their resources capacity. Besides even power  of the existing machines is not fully used because of the influence of  the human factor. An operator cannot quickly react to constantly  changing agricultural background parameters while the machine is in motion. The authors offered to automate the management of the  majority of all technological operations using devices that the machinery is supplied with. Research revealed the real opportunity to establish an automated management and regulatory  system. If an operator is excluded then it is possible to increase efficiency of a machine from 10 to 50 percent depending on its type. Some recommendations on the structure of the priority of  automatic devices were given and the main vision principles of  intelligent combine harvesters were formulated. They are based on  three underlying factors which include electric drives for working  tools involving transmission; automatic equipment systems; computerization of controlling machine and its operation modes.Мониторинг различных путей повышения производительности сельхозмашин в рамках развития интеллектуального сельскохозяйственного производства показал, что  традиционные направления увеличения энергонасыщенности, габаритов рабочих органов,  скорости движения агрегатов почти исчерпали свои ресурсные возможности. К тому же  установили, что даже уже созданная техника не полностью реализует свои потенциальные  возможности из-за влияния человеческого фактора. Оператор не может оперативно  реагировать на постоянно меняющиеся параметры агрофона во время движения агрегата.  Предложили автоматизировать управление большинством технологических операций с  помощью устройств, которыми снабжается полевой агрегат. Провели исследования, которые показали реальную возможность создания такой автоматизированной системы управления и регулирования. Установили, что если исключить оператора при выполнении основных  технологических операций, то можно повысить производительность агрегата с 10 до 50  процентов в зависимости от его типа. Дали рекомендации по структуре приоритетных автоматических устройств и сформулировали основные концептуальные принципы интеллектуальных комбайнов. Показали, что они основаны на трех основополагающих  факторах: электроприводе рабочих органов, включая трансмиссию; систем автоматических  устройств; компьютеризации управления машиной и режимами ее работы

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Sustainable agriculture – Poland and Portugal

Papers from communications presented at the Workshop “Agriculture Sustainability, Poland-Portugal”, held in the University of Évora, Portugal, December, 2013

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art

Control Architecture For Multi-Robot System

A multiple robot control architecture including a plurality of robotic agricultural machines including a first and second robotic agricultural machine. Each robotic agricultural machine including at least one controller configured to implement a plurality of finite state machines within an individual robot control architecture (IRCA) and a global information module (GIM) communicatively coupled to the IRCA. The GIMs of the first and second robotic agricultural machines being configured to cooperate to cause said first robotic agricultural machine and said second agricultural machine to perform at least one agricultural task

Geographies of Competitive Advantage: An Examination of the US Farm Machinery Industry

Many explanations of competitive advantage view place as a secondary factor. Organizational studies models tend to be considered aspatially, yet most are inherently geographic. It is important to consider the impact that geography has on the success or failure of an individual firm or a sector. This dissertation examines how location impacts the US farm machinery industry through an empirical analysis of Porter’s Theory of Competitive Advantage. Contributing to this empirical test are other bodies of literature including models for headquarters and research and development siting, product life cycle theory, industry life cycle theory, and green technologies as a driver of competitive advantage. The US farm machinery industry is composed of three firms: Deere and Company, Case New Holland, and the Allis-Gleaner Corporation. Theory-elaborating case study methodology, informed by archival data, publically available documents, trade show reconnaissance, and plant tours, coupled with map and content analysis allows for a deeper understanding of how geography impacts competitive advantage in the sector. Comparing findings from these geographic case studies to Porter’s results led to a new understanding of competitive advantage for mature manufacturing in a globalized economy. Previous analysis found Porter’s single diamond, which focuses on local conditions for competitive advantage, most appropriate for explaining mature industries in advanced market economies. This study found, however, that as mature industries increasingly pursue a global focus, a double diamond model, which takes into account both local and global conditions for competitive advantage, is more appropriate, even in an advanced economy. This research also found that, much like second-tier cities are desirable for headquarters and research and development siting, second-tier countries (that can provide high-skill labor at lower prices) are increasingly attractive for manufacturing operations. The need for modifications to product life cycle theory that take into account the impact of these countries as well as the effects of nationalism on manufacturing decisions in mature economies were also uncovered by this dissertation. This research demonstrates the continued importance of place to understanding competitive advantage, not only in the US farm machinery industry, but generally for mature manufacturing as a whole

Spot farming – an alternative for future plant production

Das Ziel der nachhaltigen Intensivierung der Landwirtschaft ist die Steigerung der weltweiten Nahrungsmittelproduktion bei gleichzeitiger Reduzierung des Inputs sowie der Vermeidung von negativen Umwelteinflüssen. Wachsende Kritik an den derzeitigen Produktionssystemen sowie der demografische Wandel, der mit einem zunehmenden Arbeitskräftemangel in den ländlichen Räumen einhergeht, stellen weltweit eine zunehmende Herausforderung für die Landwirtschaft dar. Im Rahmen dieses Problemfeldes bieten die Digitalisierung und auto­nome Maschinensysteme neue Möglichkeiten um die Landwirtschaft an diese Herausforderungen anzupassen. Bisher ist nicht bekannt, welche Veränderungen zur Errei­chung einer nachhaltigen Intensivierung im Gesamt­system Pflanzenproduktion notwendig sind und wie die Landwirtschaft der Zukunft unter Einbeziehung neuer technologischer Möglichkeiten aussehen könnte.Im Rahmen dieser Arbeit wurde ein Konzept für zukünf­tige Pflanzenbausysteme unter Berücksichtigung der Kulturpflanzenansprüche und des Landschaftskontextes entwickelt. Hierbei werden die Agrarflächen nach unterschiedlichen teilflächenspezifischen Eigenschaften bewertet und darauf aufbauend in unterschiedlichen Spots reorganisiert. Das daraus abgeleitete Konzept des Spot-Farmings basiert auf einer vollständigen Bewirtschaftung mit autonomen Robotiksystemen auf Einzelpflanzenebene. Durch höhere Präzision bei Aussaat, Düngungs- und Pflanzenschutzmaßnahmen können Ressour­cen gespart und Erträge gesteigert werden. Kleine Robotersysteme können zudem einen Beitrag zum Boden­schutz leisten. Das Spot-Farming-Konzept berücksichtigt darüber hinaus die natürlichen Landschafts­eigenschaften, um gesellschaftlich erwünschte Neben­aspekte, wie vielfältigere Kulturlandschaften, mehr Biodiversität und Struktur in der Landschaft, zu berücksichtigen.Die Bewertung des Konzepts nach pflanzenbaulichen, technischen und ökonomischen Aspekten zeigt, dass die Kombination von modernen Technologien und einer Reorga­nisierung der Kulturlandschaften zum Ziel der nachhaltigen Intensivierung beitragen kann.Sustainable intensification is described as the desirable goal for agricultural production to increase agricultural productivity while using less input and without adverse environmental impacts. Increasing criticism on current agricultural production systems as well as demographic changes related with labour shortages in rural areas pose major challenges to agriculture all over the world. In this context, digitalization and autonomous machinery provide new opportunities to adapt agriculture to future demands. However, it is unknown what changes are necessary for a sustainable intensification of cropping systems and how future agriculture could look like under consideration of new technologies.Here we developed a concept for future cropping systems with focus on the requirements of crops and landscapes. In this concept, the agricultural area is classified into individual spots according to their site-specific characteristics. The resulting spot farming approach is completely managed by an autonomous robot system on the level of individual plants. High precision sowing, fertilization and pesticide application could reduce agronomic input and could increase yields. In addition, small robots contribute to soil protection. Furthermore, the spot farming approach considers landscape properties and has the potential for a higher biodiversity and more structural ele­ments as well as an increased social acceptance.The evaluation of the concept according to agronomical, technical and economic aspects showed that the combination of modern technologies and a reorganisation of agricultural landscapes could contribute to the goal of sustainable intensification