Technology will be key to China’s food self-reliance

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Genetically Modified Crops ◽  
State Ownership ◽  
Market Forces ◽  
Xi Jinping ◽  
Content Type ◽  
Commercial Cultivation ◽  
Self Reliance ◽  
Self Sufficiency ◽  
Moderate Scale

Significance Their view reflects a renewed preoccupation with grain self-sufficiency. As President Xi Jinping put it in July, “the more risks and challenges we face, the more we should fill our bowls with Chinese grain”. Impacts More ‘moderate-scale’ farms, including some run by companies, will replace traditional household plots. State ownership of all farmland will continue, but market forces may be given a greater role. Commercial cultivation of genetically modified crops may finally be on the horizon. Application of artificial intelligence to agriculture will increase, albeit from a very low base.

China will push for greater self-reliance in food

2022 ◽  
Keyword(s):  
Food Security ◽  
Natural Disasters ◽  
Food Waste ◽  
Animal Feed ◽  
Labour Force ◽  
Arable Land ◽  
Xi Jinping ◽  
Content Type ◽  
Environmental Threats ◽  
Self Reliance

Significance President Xi Jinping last year called for "a sense of crisis about food security”. Behind such statements lies an awareness of environmental threats and natural disasters, a shrinking and ageing farm labour force, shortages of water and arable land, and food waste on an enormous scale. Impacts China cannot avoid dependence on imports of animal feed as its population's demand for meat rises further. Beijing will make greater efforts to diversify foreign sources of feed imports. China is immutably locked into overseas dependence for soybeans, and potentially maize and barley, too.

Go West initiative may increase China’s self-reliance

2020 ◽  
Keyword(s):  
Economic Growth ◽  
Western China ◽  
Foreign Firms ◽  
High Tech ◽  
Foreign Investors ◽  
Content Type ◽  
The Core ◽  
Self Reliance ◽  
Ethnic Tensions ◽  
Self Sufficiency

Significance It is also designed to enable greater reliance on domestic resources and markets in an increasingly hostile international geopolitical environment. Infrastructure is the core of the strategy. Impacts The most visible impact will be the construction of infrastructure, especially in transport, energy and the high-tech sector. Foreign investors are likely to play little if any role in Go West. Some foreign firms may benefit if costs fall and incomes rise in Western China; others will lose out if China’s self-sufficiency increases. Economic growth will not reduce ethnic tensions in Xinjiang and Tibet if ethnic Han benefit disproportionately.

Genetic modification will make slow headway in China

2017 ◽  
Keyword(s):  
Gm Crops ◽  
Foreign Firms ◽  
Transgenic Technology ◽  
Xi Jinping ◽  
Content Type ◽  
Biotechnology Research ◽  
Commercial Cultivation ◽  
The Government ◽  
Biotechnology Sector

Subject China's biotechnology sector. Significance President Xi Jinping has called for China to "occupy the commanding heights of transgenic technology” and not yield to “big foreign firms”. China is now the world’s top public spender on biotechnology research and largest importer of genetically modified (GM) crops. However, GM technology faces fierce opposition, most recently manifested in the Heilongjiang provincial government’s December 2016 ban on GM crops and a domestic campaign against state-owned ChemChina’s planned purchase of Swiss pesticide and seed producer Syngenta. Impacts Inconsistent policy and diverging interests among policymakers hold back China's biotechnology sector, despite a strong wish to promote it. Private firms and the government seek foreign cooperation and overseas development, but foreign firms will be wary of technology capture. Imports of new GM products will increase only gradually, to protect the local industry from competition and mollify safety concerns. Commercial cultivation of pest-resistant GM corn is a long-term goal but is unlikely to happen within three or four years.

Welcome to the marriage of human capability and artificial intelligence

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre
Keyword(s):  
Artificial Intelligence ◽  
Team Building ◽  
Workplace Change ◽  
Content Type ◽  
The Future ◽  
Human Capability ◽  
Mind Set ◽  
Future Work ◽  
Crucial Part ◽  
Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

New Taiwan opposition leader could ease China tension

2021 ◽  
Keyword(s):  
Xi Jinping ◽  
Charismatic Leader ◽  
Content Type ◽  
Cautious Approach ◽  
Ruling Party

Significance He is Beijing's preferred candidate and appears to have a cordial relationship with President Xi Jinping. He takes the helm at a moment when relations with China are the tensest they have been in more than a decade. Impacts Chu will stick to the '1992 Consensus' that there is only one China. Chu will take a more cautious approach to cross-Strait cooperation than Taiwan's last Kuomintang president, Ma Ying-jeou (2008-16). The task of making the Kuomintang a ruling party again will probably require a more charismatic leader than Chu.

Artificial intelligence research in agriculture: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amit Sood ◽  
Rajendra Kumar Sharma ◽  
Amit Kumar Bhardwaj
Keyword(s):  
Artificial Intelligence ◽  
Peer Review ◽  
Diffusion Of Innovation ◽  
Comprehensive Review ◽  
Content Type ◽  
Design Elements ◽  
Extant Literature ◽  
Use Of Technology ◽  
Challenges And Opportunities ◽  
Market Factors

PurposeThe purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.Design/methodology/approachThe authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.FindingsFour factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.Research limitations/implicationsThe proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.Practical implicationsPractitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.Social implicationsThis paper brings new developed methods and practices in agriculture for betterment of society.Originality/valueThis paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

AI technologies and their impact on supply chain resilience during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Modgil ◽  
Shivam Gupta ◽  
Rébecca Stekelorum ◽  
Issam Laguir
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Information Processing ◽  
Supply Chains ◽  
Failure Modes ◽  
Stress Test ◽  
Weak Links ◽  
Content Type ◽  
Supply Chain Resilience

PurposeCOVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approachWe adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.FindingsAn AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implicationsAs the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implicationsSupply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/valueThe present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

How to survive in the age of artificial intelligence? Exploring the intelligent transformations of SMEs in central China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinqiang Wang ◽  
Yaobin Lu ◽  
Si Fan ◽  
Peng Hu ◽  
Bin Wang
Keyword(s):  
Artificial Intelligence ◽  
Small And Medium Enterprises ◽  
Policy Support ◽  
Central China ◽  
Limited Resources ◽  
Text Data ◽  
Content Type ◽  
External Pressures ◽  
Medium Enterprises

PurposeThe purpose of the research is to explore how small and medium enterprises (SMEs) in central China achieve intelligent transformation through the use of artificial intelligence (AI). Because of unequal resource allocation, constraints on the intelligent transformation of SMEs in central China are different from those in economically and technologically well-developed coastal provinces. Hence, the authors focus on SMEs in central China to identify drivers of and barriers to intelligent transformation.Design/methodology/approachThe interview data were collected from 66 SMEs across 20 industries in central China. To verify the validity of the data collection method, the authors used two methods to control for retrospective bias: multi-level informants and enterprises' AI project application materials (Wei and Clegg, 2020). The final data were validated without conflicts. Next, the authors cautiously followed a two-step approach recommended by Venkatesh et al. (2010) and used NVivo 11.0 to analyze the collected text data.FindingsSMEs in central China are enthusiastic about intelligent transformation while facing both internal and external pressures. SMEs need to pay attention to both internal (enterprise development needs, implementation cost, human resources and top management involvement) and external factors (external market pressure, convenience of AI technology and policy support) and their different impacts on intelligent transformation. However, constrained by limited resources, SMEs in central China have been forced to take a step-by-step intelligent transformation strategy based on their actual needs with the technological flexibility method in the short term.Originality/valueConsidering the large number of SMEs and their importance in promoting China's economic development and job creation (SME Bureau of MIIT, 2020), more research on SMEs with limited resources is needed. In the study, the authors confirmed that enterprises should handle “social responsibility” carefully because over-emphasizing it will hinder intelligent transformation. However, firms should pay attention to the role of executives in promoting intelligent transformation and make full use of policy support to access more resources.

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

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