Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties
In arid agriculture, the effective allocation of scarce water resources and the assessment of irrigation shortage risks are critical water management practices. However, these practices are faced with inherent and unignorable uncertainties affecting multiple variables. This study aims to model the t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/1/45 |
| _version_ | 1797344820484636672 |
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| author | Fan Zhang Peixi Tang Tingting Zhou Jiakai Liu Feilong Li Baoying Shan |
| author_facet | Fan Zhang Peixi Tang Tingting Zhou Jiakai Liu Feilong Li Baoying Shan |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | In arid agriculture, the effective allocation of scarce water resources and the assessment of irrigation shortage risks are critical water management practices. However, these practices are faced with inherent and unignorable uncertainties affecting multiple variables. This study aims to model the typical uncertainties in these practices and understand how they impact the allocation of scarce water resources. We advocate for a nuanced consideration of variable characteristics and data availability, variation, and distribution when choosing uncertainty representation methods. We proposed a comprehensive framework that integrates the cloud model to delineate scenarios marked by subjective vagueness, such as “high” or “low” prices. Simultaneously, the stochastic method was used for modeling meteorological and hydrological variables, notably precipitation and crop evapotranspiration. Additionally, to navigate subjectivity and imprecise judgment in standards classification, this framework contains a cloud-model-based assessment method tailored for evaluating irrigation shortage risks. The proposed framework was applied to a real-world agricultural water management problem in Liangzhou County, northwest China. The results underscored the efficacy of the cloud model in representing subjective vagueness, both in the optimization process and the subsequent assessment. Notably, our findings revealed that price predominantly influences net benefits, and that precipitation and crop evapotranspiration emerge as decisive factors in determining optimal irrigation schemes. Moreover, the identification of high water storage risks for maize in the Yongchang and Jinyang districts serves as a reminder for local water managers of the need to prioritize these areas. By adeptly modeling multiple uncertainties, our framework equips water managers with tools to discern sensitive variables. We suggest that enhanced precipitation and evapotranspiration forecasts could be a promising way to narrow the uncertainties. |
| first_indexed | 2024-03-08T11:08:27Z |
| format | Article |
| id | doaj.art-660a4dbc61d3457380d92d0d545d4cb2 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-08T11:08:27Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-660a4dbc61d3457380d92d0d545d4cb22024-01-26T14:21:56ZengMDPI AGAgronomy2073-43952023-12-011414510.3390/agronomy14010045Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid UncertaintiesFan Zhang0Peixi Tang1Tingting Zhou2Jiakai Liu3Feilong Li4Baoying Shan5Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaSchool of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, ChinaGuangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, ChinaResearch Unit Knowledge-Based System, Ghent University, 9000 Ghent, BelgiumIn arid agriculture, the effective allocation of scarce water resources and the assessment of irrigation shortage risks are critical water management practices. However, these practices are faced with inherent and unignorable uncertainties affecting multiple variables. This study aims to model the typical uncertainties in these practices and understand how they impact the allocation of scarce water resources. We advocate for a nuanced consideration of variable characteristics and data availability, variation, and distribution when choosing uncertainty representation methods. We proposed a comprehensive framework that integrates the cloud model to delineate scenarios marked by subjective vagueness, such as “high” or “low” prices. Simultaneously, the stochastic method was used for modeling meteorological and hydrological variables, notably precipitation and crop evapotranspiration. Additionally, to navigate subjectivity and imprecise judgment in standards classification, this framework contains a cloud-model-based assessment method tailored for evaluating irrigation shortage risks. The proposed framework was applied to a real-world agricultural water management problem in Liangzhou County, northwest China. The results underscored the efficacy of the cloud model in representing subjective vagueness, both in the optimization process and the subsequent assessment. Notably, our findings revealed that price predominantly influences net benefits, and that precipitation and crop evapotranspiration emerge as decisive factors in determining optimal irrigation schemes. Moreover, the identification of high water storage risks for maize in the Yongchang and Jinyang districts serves as a reminder for local water managers of the need to prioritize these areas. By adeptly modeling multiple uncertainties, our framework equips water managers with tools to discern sensitive variables. We suggest that enhanced precipitation and evapotranspiration forecasts could be a promising way to narrow the uncertainties.https://www.mdpi.com/2073-4395/14/1/45cloud modeluncertaintyagricultural water optimizationwater storage risk assessmentprecision agriculture |
| spellingShingle | Fan Zhang Peixi Tang Tingting Zhou Jiakai Liu Feilong Li Baoying Shan Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties Agronomy cloud model uncertainty agricultural water optimization water storage risk assessment precision agriculture |
| title | Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties |
| title_full | Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties |
| title_fullStr | Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties |
| title_full_unstemmed | Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties |
| title_short | Cloud-Based Framework for Precision Agriculture: Optimizing Scarce Water Resources in Arid Environments amid Uncertainties |
| title_sort | cloud based framework for precision agriculture optimizing scarce water resources in arid environments amid uncertainties |
| topic | cloud model uncertainty agricultural water optimization water storage risk assessment precision agriculture |
| url | https://www.mdpi.com/2073-4395/14/1/45 |
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