Sustainable Water Resources Using Corner Pivot Lateral, A Novel Sprinkler Irrigation System Layout for Small Scale Farms

Rad, Saeed; Gan, Lei; Chen, Xiaobing; You, Shaohong; Huang, Liangliang; Su, Shihua; Taha, Mohd Raihan

doi:10.3390/app8122601

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Generally, the system benefits would decrease with rising λ levels and change with different scenarios. For instance, when λ = 0.9, the system cost would be [3.55, 4.00] × 10 12 RMB¥ under S4 and [3.23, 3.61] × 10 12 RMB¥ under S3. This is because different scenarios would correspond to the water allocation proportion of different departments, thus leading to different benefits of the overall system.…”

Section: Results Analysismentioning

confidence: 99%

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Miao

Zuo

et al. 2019

Processes

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Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the variation of scenarios (i.e., from S2 to S3) can result in a change of 9% over the planning horizon for water allocation in the industrial sector. Findings can help decision-makers resolve conflicts among economic objective, water resource demand, and sewage discharge, as well as COD emissions.

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Section: Results Analysismentioning

confidence: 99%

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Miao

Zuo

et al. 2019

Processes

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“…In connection with the role played by Center Pivot Irrigation systems (CPI), numerous studies are being carried out to improve the technical parameters of these systems: improving the uniformity of irrigation, modeling sprinklers, and lateral corner joints [9][10][11]. In addition, the Precision Agriculture methodology appears for such systems as well.…”

Section: A Precision Agriculture Digitalization and Artificial Intell...mentioning

confidence: 99%

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

et al. 2022

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The article proposes a methodology for optimizing the process of irrigation of crops using a phytoindication system based on computer vision methods. We have proposed an algorithm and developed a system for obtaining a map of irrigation for maize in low latency mode. The system can be installed on a center pivot irrigation and consists of 8 IP cameras connected to a DVR connected to a laptop. The algorithm consists of three stages. Image preprocessing stage -applying an integrated excess green and excess red difference (ExGR) index. The classification stage is the application of the method that we choose depending on the operating conditions of the system. At the final stage, a neural network trained using the Resilient Propagation method is used, which determines the rate of watering of plants in the current sector of the location of the sprinkler. The selected methods of pretreatment and classification made it possible to achieve an accuracy of plant identification up to 93%, growth stages -up to 92% (with unconsolidated maize sowing and good lighting). System performance up to 100 plants in one second, which exceeds the performance of similar systems. The neural network showed an accuracy of 92% on the training set and 87% on the test set. Dynamic analysis of spatial and temporal variability leads to an increase in productivity and efficiency of water use. In addition, given the ubiquitous distribution of agribusiness management systems, this approach is quite simple to implement in the farm's conditions.

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“…This, of course, is of great importance when maize is grown for the food industry and animal feed production, where high grain quality is expected. Previous studies emphasized the importance of the interaction between growing systems (irrigation and fertilization), weather conditions, and soil properties for soil water and nutrient availability, as well as crop yield potential [37][38][39][40][41]. An interesting observation was made by Qi et al [20].…”

Section: Introductionmentioning

confidence: 99%

Extreme Weather Events Affect Agronomic Practices and Their Environmental Impact in Maize Cultivation

et al. 2021

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Sustainable and profitable crop production has become a challenge due to frequent weather extremes, where unstable crop yields are often followed by the negative impacts of agronomic practices on the environment, i.e., nitrate leaching in irrigated and nitrogen (N)-fertilized crop production. To study this issue, a three-year field study was conducted during quite different growing seasons in terms of weather conditions, i.e., extremely wet, extremely dry, and average years. Over three consecutive years, the irrigation and N fertilizers rates were tested for their effect on grain yield and composition, i.e., protein, starch, and oil content of the maize hybrids; soil N level (%); and nitrate leaching. The results showed that the impact of the tested factors and their significance was year- or weather-condition-dependent. The grain yield result stood out during the extremely wet year, where the irrigation rate reduced the grain yield by 7.6% due to the stress caused by the excessive amount of water. In the remainder of the study, the irrigation rate expectedly increased the grain yield by 13.9% (a2) and 20.8% (a3) in the extremely dry year and 22.7% (a2) and 39.5% (a3) during the average year. Regardless of the weather conditions, the N fertilizer rate increased the grain yield and protein content. The soil N level showed a typical pattern, where the maximum levels were at the beginning of the study period and were higher as the N fertilizer rate was increased. Significant variations in the soil N level were found between weather conditions (r = −0.719) and N fertilizer rate (r = 0.401). Nitrate leaching losses were expectedly found for irrigation and N fertilizer treatments with the highest rates (a3b3 = 79.8 mg NO3− L).

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Sustainable Water Resources Using Corner Pivot Lateral, A Novel Sprinkler Irrigation System Layout for Small Scale Farms

Cited by 4 publications

References 26 publications

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

A Scenario-Based Optimization Model for Planning Sustainable Water-Resources Process Management under Uncertainty

Artificial Neural Networks and Computer Vision’s-Based Phytoindication Systems for Variable Rate Irrigation Improving

Extreme Weather Events Affect Agronomic Practices and Their Environmental Impact in Maize Cultivation

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