Climate-smart agriculture and forestry: maintaining plant productivity in a changing world while minimizing production system effects on climate

DANIELLE A. WAY; STEVE P. LONG

doi:10.1111/pce.12592

Climate-smart agriculture and forestry: maintaining plant productivity in a changing world while minimizing production system effects on climate

Plant Cell & Environment ◽

10.1111/pce.12592 ◽

2015 ◽

Vol 38 (9) ◽

pp. 1683-1685 ◽

Cited By ~ 4

Author(s):

DANIELLE A. WAY ◽

STEVE P. LONG

Keyword(s):

Production System ◽

Plant Productivity ◽

Smart Agriculture ◽

Changing World

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Assessing Climate Smart Agriculture and Its Determinants of Practice in Ghana: A Case of the Cocoa Production System

Land ◽

10.3390/land7010030 ◽

2018 ◽

Vol 7 (1) ◽

pp. 30 ◽

Cited By ~ 14

Author(s):

Felix Akrofi-Atitianti ◽

Chinwe Ifejika Speranza ◽

Louis Bockel ◽

Richard Asare

Keyword(s):

Production System ◽

Cocoa Production ◽

Smart Agriculture ◽

Determinants Of Practice

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Requirements for Modeling the Human Operator in Socio-Technical Production Systems

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.613.453 ◽

2014 ◽

Vol 613 ◽

pp. 453-460 ◽

Cited By ~ 2

Author(s):

Marco Faber ◽

Marcel Ph. Mayer ◽

Christopher M. Schlick

Keyword(s):

Production System ◽

Creative Thinking ◽

World Economy ◽

Production Systems ◽

Technical System ◽

Human Operator ◽

High Wage ◽

Technical Production ◽

Changing World

The changing world economy makes high demands on today's production systems. In order to stay competitive, companies, especially in high-wage countries, have to adjust their production for enabling customer individual wishes. The human operator provides meaningful skills including sensorimotorical skills and the capability of creative thinking from which the production system can significantly benefit. For establishing effective human-machine cooperation, both the employee and the technical system need to have an understanding of each other so that they can estimate the counterpart. In this paper, the requirements for introducing the human operator in technical models of production systems are described. Furthermore, first solutions are presented to implement effective human-machine cooperation.

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Reflections and Methodological Proposals to Treat the Concept of “Information Precision” in Smart Agriculture Practices

Sensors ◽

10.3390/s20102847 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2847 ◽

Cited By ~ 2

Author(s):

Fabrizio Mazzetto ◽

Raimondo Gallo ◽

Pasqualina Sacco

Keyword(s):

Decision Making ◽

Information Integration ◽

Production System ◽

Hierarchical Level ◽

Technological Basis ◽

Related Data ◽

Information Precision ◽

Smart Agriculture ◽

Concept Of Information ◽

Physical And Chemical

Smart Agriculture (SA) is an evolution of Precision Farming (PF). It has technological basis very close to the paradigms of Industry 4.0 (Ind-4.0), so that it is also often referred to as Agriculture 4.0. After the proposal of a brief historical examination that provides a conceptual frame to the above terms, the common aspects of SA and Ind-4.0 are analyzed. These are primarily to be found in the cognitive approaches of Knowledge Management 4.0 (KM4.0, the actual theoretical basis of Ind-4.0), which underlines the need to use Integrated Information Systems (IIS) to manage all the activity areas of any production system. Based upon an infological approach, “raw data” becomes “information” only when useful to (or actually used in) a decision-making process. Thus, an IIS must be always designed according to such a view, and KM4.0 conditions the way of collecting and processing data on farms, together with the “information precision” by which the production system is managed. Such precision needs, on their turn, depend on the hierarchical level and the “Macrodomain of Prevailing Interest” (MPI) related to each decision, where the latter identifies a predominant viewpoint through which a system can be analyzed according to a prevailing purpose. Four main MPIs are here proposed: (1) physical and chemical, (2) biological and ecological, (3) productive and hierarchical, and (4) economic and social. In each MPI, the quality of the knowledge depends on the cognitive level and the maturity of the methodological approaches there achieved. The reliability of information tends to decrease from the first to the fourth MPI; lower the reliability, larger the tolerance margins that a measurement systems must ensure. Some practical examples are then discussed, taking into account some IIS-monitoring solutions of increasing complexity in relation to information integration needs and related data fusion approaches. The analysis concludes with the proposal of new operational indications for the verification and certification of the reliability of the information on the entire decision-making chain.

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