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The world's population is in continuous increase in recent decades. This increase poses significant challenges to the agricultural and farming sector. With more mouths to feed, old farming techniques can't meet the demand. It has become increasingly crucial to adopt advanced management techniques and cutting-edge technologies to boost agricultural productivity, reduce harvesting waste and meet the growing demand for food. This necessitates a paradigm shift in managing farms and agriculture, moving from traditional methods to more innovative and efficient approaches. Two approaches that have recently gained considerable attention are Agile management and Artificial Intelligence (AI). Farmers and agricultural managers can streamline their operations, increase efficiency, and improve their decision-making capabilities by adopting Agile and AI. This paper aims to explore the unique benefits and challenges of implementing Agile management and AI technologies in the agricultural sector and provide insights into their potential to revolutionise the industry. A theoretical implementation model was created with tips and guides for implementation. [ABSTRACT FROM AUTHOR]

Aim Regional-scale assessments are frequently conceived to guide the strategic application of conservation actions. Although changes to priority areas from initial assessments are inevitable, the transition from regional-scale assessment to implementing local actions is poorly understood. An outstanding question concerns the frequency with which regionally assessed priorities should be updated as actions are implemented. We address this gap by simulating the incremental implementation of local actions guided by regional conservation assessments, exploring how update frequency can influence aspects of translating regional assessments to local actions. Location Fiji coral reefs. Methods Our simulations were designed within the framework of systematic conservation planning, with implemented actions simulated based on conservation value in achieving objectives and feature rarity. Other decision rule-sets were put in place to simulate on-the-ground negotiations that are often necessary when transitioning from regional-scale conservation assessments to local actions. We use our simulations to evaluate how the frequency of updating regional priorities influences (1) total time taken to achieve objectives represented by numbers of planning units investigated, (2) total extent of final reserve systems and (3) spatial overlap between initial regional priorities and final implemented reserves. Results Changes in the frequencies of updating did not influence the time taken to achieve conservation objectives, nor the total extent of final reserve systems. However, there was a significant difference in the number of times planning units were re-investigated for implementing actions within in scenarios that involved more frequent updates. Spatial overlap between initial regional priorities and final implemented reserves increased with decreases in update frequency. Main conclusions We find two potential benefits to updating priorities more frequently: (1) faster achievement of objectives for high-priority features and (2) greater potential to capitalize on areas previously investigated. Our findings provide insights into trade-offs to consider regarding the frequency of updating regional assessments, which varies depending on the planning context. [ABSTRACT FROM AUTHOR]

Stadial (stage-based) theory clarifies the relation between the evolving conception of mature socialism, on the one hand, and historical experiences of central planning and “market socialism,” on the other. The core of mature socialism is a system of multilevel democratic iterative coordination (MDIC), involving mutually supportive and mutually defining roles for a central authority and enterprises. This conception clarifies the relations between the socialist core and various precursor forms in existing transitional societies. [ABSTRACT FROM PUBLISHER]