A Risk-Informed Decision-Making Framework for Climate Change Adaptation through Robust Land Use and Irrigation Planning

Ermolieva, T.; Havlík, Petr; Frank, Stefan; Kahil, Taher; Balkovič, Juraj; Skalský, Rastislav; Ermoliev, Y.; Knopov, P. S.; Borodina, Olena; Горбачук, Василь

doi:10.3390/su14031430

Cited by 10 publications

(3 citation statements)

References 32 publications

(92 reference statements)

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“…Among the different types of innovation in agriculture, a particularly important role today is played by those related to irrigation practices (Asadi et al, 2020). Today, in fact, water scarcity and droughts are a major problem (Saeed et al, 2021), probably exacerbated by climate change, which represents one of the greatest environmental, social and economic threats to the entire planet (Ungureanu et al, 2020;Ermolieva et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Timpanaro

Pecorino

Chinnici

et al. 2023

Front. Sustain. Food Syst.

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IntroductionThe combination of knowledge, personal skills and company resources influences, all things being equal, such as the availability of new technologies, market conditions and other factors external to the company, farmers in their innovation choices. This study is an attempt to understand which psychological constructs influence the decision-making process of farmers specialized in typical Mediterranean crops with regard to innovation. Previous studies on the adoption of agricultural innovations have often considered socio economic characteristics and ignored the underlying motivational factors that influence the behavioral intention of farmers.MethodsThis study adopted three socio-psychological constructs, Attitude (ATT), Subjective Norm (SN), and Perceived Behavioral Control (PBC), derived from the Theory of Planned Behavior (TPB), and proposed three new constructs, Perceived Innovations Characteristics (PIC), Benefits (B), and Transferability (T), thus using an Extended Model of the Theory of Planned Behavior.ResultsThe outcome of the multiple regression revealed that farmers' intention (I) to adopt sustainable irrigation innovations is positively influenced by attitude (ATT), subjective norm (SN), and perceived innovation characteristics (PIC). This last construct had mediating effects on the indirect relationships between PBC, benefits (B), transferability (T), and intention (I).DiscussionThe results provide numerous insights, useful both for outlining the demand for innovation and for calibrating future policies aimed at the primary sector, especially on the sustainable management of irrigation resources. In particular, the analyses carried out highlight the importance of factors external to the company as key levers in shaping the demand for innovations.

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Section: Introductionmentioning

confidence: 99%

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Timpanaro

Pecorino

Chinnici

et al. 2023

Front. Sustain. Food Syst.

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“…This process has been proved to favor the implementation of effective policies and management actions building flexibility, robustness and resilience to the system. (McPhail et al, 2018;Ermolieva et al, 2022;Narita et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Analyzing the impact of agricultural water-demand management on water availability in the Urubu River basin – Tocantins, Brazil

Volken¹,

Minoti²,

Alves³

et al. 2022

Rev. ambiente água

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The Urubu River is part of the Formoso River Basin located in Tocantins State in northern Brazil. It is an important agricultural region where irrigation has an important role in rice and soybean crops, cultivated during the rainy and the dry seasons, respectively. The high levels of irrigation associated with below-average precipitation in 2016 and in the following years resulted in a water crisis in the Urubu Basin, with serious consequences to the environment and the economy of the region. This work evaluated the impact of reducing irrigation on environmental flows in the Urubu River Basin using hydrological modeling in WEAP. Irrigation water demand scenarios were simulated and analyzed from July 2018 through June 2019. Results indicated the need to reduce 35% of all water withdrawls in order to avoid the interruption of flow in the Urubu River Basin. This percentage was even greater when only some of the farmers cooperated. The paper emphasized that it is important that all farmers be involved and cooperate to reduce their water withdrawal by any means, including improving their irrigation system efficiency. The water regulator may also motivate water withdrawal reduction by modifying water permits and applying water withdrawal restrictions during the dry season. Keywords: hydrological modeling, scenario analysis, water balance.

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“…Among the methodological aims of the project is the development of "Models' Linkage" algorithms which are in the core of the IDSS as they enable distributed models' linkage and data integration into one system on a platform [5][6][7][8]. The linkage algorithms solve the problem of linking distributed models, e.g., sectorial and/or regional, into an inter-sectorial inter-regional integrated models.…”

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confidence: 99%

Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQGbased procedures for Distributed Models’ Linkage

Ermolieva¹,

Ermoliev²,

Zagorodny³

et al. 2022

Stuc.intelekt

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In this paper we discuss the on-going joint work contributing to the IIASA (International Institute for Applied Systems Analysis, Laxenburg, Austria) and National Academy of Science of Ukraine projects on “Modeling and management of dynamic stochastic interdependent systems for food-water-energy-health security nexus” (see [1-2] and references therein). The project develops methodological and modeling tools aiming to create Intelligent multimodel Decision Support System (IDSS) and Platform (IDSP), which can integrate national Food, Water, Energy, Social models with the models operating at the global scale (e.g., IIASA GLOBIOM and MESSAGE), in some cases ‘downscaling’ the results of the latter to a national level. Data harmonization procedures rely on new type non-smooth stochastic optimization and stochastic quasigradient (SQG) [3-4] methods for robust of-line and on-line decisions involving large-scale machine learning and Artificial Intelligence (AI) problems in particular, Deep Learning (DL) including deep neural learning or deep artificial neural network (ANN). Among the methodological aims of the project is the development of “Models’ Linkage” algorithms which are in the core of the IDSS as they enable distributed models’ linkage and data integration into one system on a platform [5-8]. The linkage algorithms solve the problem of linking distributed models, e.g., sectorial and/or regional, into an inter-sectorial inter-regional integrated models. The linkage problem can be viewed as a general endogenous reinforced learning problem of how software agents (models) take decisions in order to maximize the “cumulative reward". Based on novel ideas of systems’ linkage under asymmetric information and other uncertainties, nested strategic-operational and local-global models are being developed and used in combination with, in general, non-Bayesian probabilistic downscaling procedures. In this paper we illustrate the importance of the iterative “learning” solution algorithms based on stochastic quasigradient (SQG) procedures for robust of-line and on-line decisions involving large-scale Machine Learning, Big Data analysis, Distributed Models Linkage, and robust decision-making problems. Advanced robust statistical analysis and machine learning models of, in general, nonstationary stochastic optimization allow to account for potential distributional shifts, heavy tails, and nonstationarities in data streams that can mislead traditional statistical and machine learning models, in particular, deep neural learning or deep artificial neural network (ANN). Proposed models and methods rely on probabilistic and non-probabilistic (explicitly given or simulated) distributions combining measures of chances, experts’ beliefs and similarity measures (for example, compressed form of the kernel estimators). For highly nonconvex models such as the deep ANN network, the SQGs allow to avoid local solutions. In cases of nonstationary data, the SQGs allow for sequential revisions and adaptation of parameters to the changing environment, possibly, based on of-line adaptive simulations. The non-smooth STO approaches and SQG-based iterative solution procedures are illustrated with examples of robust estimation, models’ linkage, machine learning, adaptive Monte Carlo optimization for cat risks (floods, earthquakes, etc.) modeling and management.

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A Risk-Informed Decision-Making Framework for Climate Change Adaptation through Robust Land Use and Irrigation Planning

Cited by 10 publications

References 32 publications

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Analyzing the impact of agricultural water-demand management on water availability in the Urubu River basin – Tocantins, Brazil

Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQGbased procedures for Distributed Models’ Linkage

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