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

Land ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 30 ◽  
Author(s):  
Felix Akrofi-Atitianti ◽  
Chinwe Ifejika Speranza ◽  
Louis Bockel ◽  
Richard Asare
Keyword(s):  
Production System ◽  
Cocoa Production ◽  
Smart Agriculture ◽  
Determinants Of Practice

Coffee and Cocoa Production in Agroforestry—A Climate-Smart Agriculture Model

2015 ◽  
pp. 209-224 ◽  
Author(s):  
Philippe Vaast ◽  
Jean-Michel Harmand ◽  
Bruno Rapidel ◽  
Patrick Jagoret ◽  
Olivier Deheuvels
Keyword(s):  
Cocoa Production ◽  
Smart Agriculture

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

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2847 ◽  
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.

Climate-Smart agriculture and potato production in Kenya: review of the determinants of practice

2021 ◽  
pp. 1-16
Author(s):  
Andrew Waaswa ◽  
Agnes Oywaya Nkurumwa ◽  
Anthony Mwangi Kibe ◽  
Joel Ngeno Kipkemoi
Keyword(s):  
Potato Production ◽  
Smart Agriculture ◽  
Determinants Of Practice

scholarly journals WHY SHOULD CLIMATE SMART AGRICULTURE BE PROMOTED IN THE INDONESIAN COFFEE PRODUCTION SYSTEM?

2021 ◽  
Vol 16 (7) ◽  
pp. 347-363
Author(s):  
YELI SARVINA ◽  
◽  
TANIA JUNE ◽  
SURJONO HADI SUTJAHJO ◽  
RITA NURMALINA ◽  
...  
Keyword(s):  
Production System ◽  
Coffee Production ◽  
Smart Agriculture
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