Robust Decision Making for a Climate-Resilient Development of the Agricultural Sector in Nigeria

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

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Faculty Opinions recommendation of Robust Decision-Making Under Severe Uncertainty For Conservation Management.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1031061.365424 ◽

2006 ◽

Author(s):

Hugh Possingham

Keyword(s):

Decision Making ◽

Conservation Management ◽

Robust Decision Making ◽

Severe Uncertainty

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Sustainability Assessment and Agricultural Supply Chains Evidence-Based Multidimensional Analyses as Tools for Strategic Decision-Making—The Case of the Pineapple Supply Chain in Benin

Sustainability ◽

10.3390/su13042060 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2060

Author(s):

Doriane Desclee ◽

David Sohinto ◽

Freddy Padonou

Keyword(s):

Decision Making ◽

Sustainable Development ◽

Developing Countries ◽

Supply Chains ◽

Agricultural Sector ◽

Sustainability Assessment ◽

Assessment Tools ◽

Strategic Decision ◽

Evidence Based ◽

Agricultural Supply

Contributing to Sustainable Development Goals and Agenda 2030 is a shared objective of all institutions and people. The challenges differ according to the characteristics of every context. In developing countries, strongly dependent on the agricultural sector, agricultural supply chains are recognized as crucial for economic growth and enablers for livelihood improvement. Moreover, sustainable development issues are correlated and can meet in agricultural supply chains. For several decades, parallel to decision-makers, the research community has elaborated sustainability assessment tools. Such tools evolved to fit with actuality, but it is challenging to find decision-making support tools for sustainable development adequate in agricultural supply chains and developing countries contexts. There is a necessity to define evidence-based tools and exhaustive analytical frameworks according to sustainability multidimensionality and strategical tradeoffs necessity. The VCA4D method aims to go beyond the limits of previous methods. It proposes a combination of multidisciplinary analytical tools applied empirically to analyze agricultural supply chains in their context. It provides evidence-based analytical results allowing to identify enablers for strategic sustainable and inclusive interventions. However, to even better meet contextual exhaustiveness’s expectations and indicators’ robustness to lead to relevant interventions, we should insist on a stricter framing of contextual data collection processes.

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