Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

Sánchez, Juan Manuel; Rodríguez, Juan ́Pablo; Espitia, Helbert Eduardo

doi:10.3390/pr8111374

Cited by 25 publications

(20 citation statements)

References 104 publications

(118 reference statements)

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“…In his view, "technology is not just a tool or a means to achieve the goal. [4] On the contrary, it is a political actor, and the means and the goal are inseparable". In this regard, the legal regulation system that regulates human behavior through the long-term exploration of the causal relationship between behavior and consequences may fall into a dilemma: it is undoubtedly unconvincing to blame the designer for the infringement caused by artificial intelligence, while if AI is to blame, how can we determine the liability of a machine?…”

Section: Challenge In the Governance Methods Of Public Policymentioning

confidence: 99%

Development and Reform of Public Policy in the Era of Artificial Intelligence

Wang¹

2022

BCPBM

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After the development over half a century, artificial intelligence (AI) has been increasingly applied in the field of Internet and big data technology, and has been growing in leaps and bounds. With the advent of the era of AI, the rise of AI is not only a major development in the field of technology or industry, but also a fundamental and comprehensive reform in the economic, social and political fields. The resulting governance challenges require the reconstruction and innovation of the public policy framework. This paper studies the development and reform of public policy in the era of AI and puts forward relevant suggestions, providing a broader development platform for public policy in the era of AI.

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Section: Challenge In the Governance Methods Of Public Policymentioning

confidence: 99%

Development and Reform of Public Policy in the Era of Artificial Intelligence

Wang¹

2022

BCPBM

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“…Recently, modelling in mathematics has been proposed to promote the modernization of agriculture for increasing both sustainability agriculture and food security greatly. Machine learning and statistics learning are used to analyze the agriculture data for smart decision-making [65]. The sustainability agriculture and food security are more reliable, capable, and help to boost productivity [66].…”

Section: Table 5 the 30 Highest Of Variable Importancementioning

confidence: 99%

Hybrid model in machine learning–robust regression applied for sustainability agriculture and food security

Mukhtar

Ali

Ismail

et al. 2022

IJECE

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<span lang="EN-US">A dataset containing 1924 observations used in this study to evaluate the effect of 435 different independent variables on one dependent variable. Big data has some issues such as irrelevant variables and outliers. Therefore, this study focused on analysing and comparing the impact of three different variable selection based on machine learning techniques, including random forest (RF), support vector machines (SVM), and Boosting. Further, the M robust regression was applied to address the outliers using M–bi square, M–Hampel, and M–Huber. Random forest and M-Hampel results revealed the significant comparing from the other methods such as mean absolute error (MAE) 175.33995, mean square error (MSE) 31.8608, mean average percentage error (MAPE) 9.16091, sum of square error (SSE) 89270.45, R–square 0.829511, and R–square adjusted 0.82670. Also, these techniques indicated that the 8 selection criteria were lower than the other techniques including Akaike information criterion (AIC) 47.25915, generalized cross validation (GCV) 47.27169, hannan-quinn (HQ) 47.60351, RICE (47.2845), SCHWARZ 51.7099, sigma square (SGMASQ) 46.50605, SHIBATA 47.23489, and final prediction error (FPE) 47.25929. Therefore, the study recommended that the best random forest and M-Hampel models are helpful to show the minimum issues and efficient validation for analysing and comparing big data.</span>

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“…The collected data suggests two main patterns. First, considering the growing attention to the link between digital communication applications and farming from very diverse disciplines, the impact of digital communication applications on "career progress" is highly established and guided by several literature reviews (Jimenez et al 2021;Araújo et al 2021;Sánchez et al 2020;Klerkx et al 2019). However, relative to "career progress", there are few studies on the "career initiation" and "career change" effect of digital communication on the farming profession.…”

Section: Table 1 Subject Headings and Abstract Used In The Review Studymentioning

confidence: 99%

How digital communications contribute to shaping the career paths of youth: a review study focused on farming as a career option

Unay‐Gailhard

Brennen

2022

Agric Hum Values

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Can the power of digital communications create opportunities for overcoming generational renewal problems on farms? This interdisciplinary review explores the reported impacts of digital communication on career initiation into farming from a global perspective via the lens of career theories. Seventy-three papers were synthesized into two domains: (1) the impact of digital communication interactions on farming career initiation, and (2) the dynamics of digital communication initiatives that create opportunities to inspire youth into farming. The finding shows that the mainstream literature primarily aims to support the continuity of farming careers but pay little attention to the potential of digital communication to attract youth into farming. This review argues that career communications for farming receives insufficient attention, and could be better integrated into agricultural communications strategies by using the potential of digital communications. Study concludes that while economic and geographic factors, as well as societal and cultural norms, lead to negative perceptions on farming careers, there are three pathways that may contribute to breaking down these negative perceptions. Firstly, taking the changing nature of career motivations, such as the trend towards sustainable farming linked to self-fulfillment, among today’s youth into consideration is essential. Secondly, highlighting technological advances in digital agriculture practices, like geographical flexibility or innovation capacity of farming, for example, is important to increase awareness about new opportunities in the profession. Lastly, communication campaigns with targeted groups (e.g., young females) play a role to change the negative perceptions of the rural way of life and the farming profession.

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Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

Cited by 25 publications

References 104 publications

Development and Reform of Public Policy in the Era of Artificial Intelligence

Development and Reform of Public Policy in the Era of Artificial Intelligence

Hybrid model in machine learning–robust regression applied for sustainability agriculture and food security

How digital communications contribute to shaping the career paths of youth: a review study focused on farming as a career option

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