Multi-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain

Galán‐Martín, Ángel; Vaskan, Pavel; Antón, Assumpció; Esteller, Laureano Jiménez; Guillén‐Gosálbez, Gonzalo

doi:10.1016/j.jclepro.2016.06.099

Cited by 88 publications

(33 citation statements)

References 47 publications

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“…The combination of surrogate models and multi-objective optimization provides promising opportunities to solve tradeoff problems [24], e.g., maximizing water treatment abilities while minimizing construction cost at the same time. A set of alternatives (comprising the Pareto optimal solutions and the Pareto front) were generated by multi-objective optimization, which means no solution could be further improved according to both objectives [25,26]. The Pareto-optimal solutions yielded by genetic algorithms (GAs) need a huge number of iterations.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Liu

Yu³

et al. 2019

Water

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The tradeoff between engineering costs and water treatment of the artificial lake system has a significant effect on engineering decision-making. However, decision-makers have little access to scientific tools to balance engineering costs against corresponding water treatment. In this study, a framework integrating numerical modeling, surrogate models and multi-objective optimization is proposed. This framework was applied to a practical case in Chengdu, China. A water quality model (MIKE21) was developed, providing training datasets for surrogate modeling. The Artificial Neural Network (ANN) and Support Vector Machine (SVM) were utilized for training surrogate models. Both surrogate models were validated with the coefficient of determinations (R2) greater than 0.98. SVM performed more stably with limited training data sizes while ANN demonstrated higher accuracies with more training samples. The multi-objective optimization model was developed using the genetic algorithm, with targets of reducing both engineering costs and target aquatic pollutant concentrations. An optimal target concentration after treatment was identified, characterized by the ammonia concentration (1.3 mg/L) in the artificial lake. Furthermore, scenarios with varying water quality in the upstream river were evaluated. Given the assumption of deteriorated upstream water quality in the future, the optimal proportion of pre-treatment in the total costs is increasing.

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Section: Introductionmentioning

confidence: 99%

Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Liu

Yu³

et al. 2019

Water

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“…Ongoing climate change has increased the frequency and severity of droughts, flooding, and urban heat islands (IPCC 2014). In recent decades, this has resulted in increased damage and casualties from weather-related disasters, decreased agricultural production, degraded or destroyed ecosystems, and other effects (Polasky et al 2008, Klijn et al 2012, Lehmann et al 2013, Galán-Martín et al 2017, Scarano 2017. Various studies have attempted to identify which areas will be most exposed to climate change impacts and at what intensities (Chavas et al 2009, Kim et al 2014, Thorne et al 2017a, but further discussion of climate change adaptions from the perspective of landuse is needed .…”

Section: Introductionmentioning

confidence: 99%

“…For landuse planning, the integration of various adaptation strategies critical because related climate change impacts may spatially overlap. Furthermore, trade-offs between adaptation strategies can occur due to competing objectives and other conditions (Kennedy et al 2016, Galán-Martín et al 2017. For example, enhancing climate adaptation in one sector may weaken resilience in other sectors (Thorne et al 2017b).…”

Section: Introductionmentioning

confidence: 99%

Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk

Yoon

Thorne

Park

et al. 2019

Environ. Res. Lett.

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As climate change is ongoing, many studies have recently focused on adaptation to climate change from a spatial perspective. However little is known about how changing the spatial composition of landuse could improve climate change resilience. Consideration of climate change impacts when spatially allocating landuse could be a useful and fundamental long term adaptation strategy, particularly for regional planning. Here, we identify climate adaptation scenarios based on existing extents of three landuse classes using multi-objective genetic algorithms for a 9982 km 2 region with 3.5 million inhabitants in South Korea. We selected five objectives for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damage and existing landuse conversion, maximization of rice yield, protection of high species richness areas, and economic value. We generated 17 Pareto landuse scenarios by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial landuse composition for all adaptation objectives, suggesting that some alteration of current landuse patterns could increase overall climate resilience. Given the flexible structure of the optimization model, we expect that regional stakeholders could efficiently generate other scenarios by adjusting model parameters (weighting combinations) or replacing input data (impact maps), and selecting a scenario depending on preference or a number of problem-related factors.

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“…Water scarcity has been a main constraint to social-economic development, underscoring the importance of efficient water resource management. Irrigated agriculture, as the largest water consumer, is being subjected to reductions in water allocation because of the expected increase in water demand for non-agricultural sectors, such as industrial, domestic, ecological, and environmental use [1]. Future irrigated agriculture will face challenges to meet the growing demand of food, attributed to continuing population growth and rapid socio-economic development.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle

Yan

2018

Water

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Agricultural water scarcity is a global problem and this reinforces the need for optimal allocation of irrigation water resources. However, decision makers are challenged by the complexity of fluctuating stream condition and irrigation quota as well as the dynamic changes of the field water cycle process, which make optimal allocation more complex. A two-stage chance-constrained programming model with random parameters in the left- and right-hand sides of constraints considering field water cycle process has been developed for agricultural irrigation water allocation. The model is capable of generating reasonable irrigation allocation strategies considering water transformation among crop evapotranspiration, precipitation, irrigation, soil water content, and deep percolation. Moreover, it can deal with randomness in both the right-hand side and the left-hand side of constraints to generate schemes under different flow levels and constraint-violation risk levels, which are informative for decision makers. The Yingke irrigation district in the middle reaches of the Heihe River basin, northwest China, was used to test the developed model. Tradeoffs among different crops in different time periods under different flow levels, and dynamic changes of soil moisture and deep percolation were analyzed. Scenarios with different violating probabilities were conducted to gain insight into the sensitivity of irrigation water allocation strategies on water supply and irrigation quota. The performed analysis indicated that the proposed model can efficiently optimize agricultural irrigation water for an irrigation district with water scarcity in a stochastic environment.

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Multi-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain

Cited by 88 publications

References 47 publications

Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk

A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle

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