A holistic simulation model of solid-set sprinkler irrigation systems for precision irrigation

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-09-09 DOI:10.1007/s11119-024-10171-8
M. Morcillo, J. F. Ortega, R. Ballesteros, A. del Castillo, M. A. Moreno
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Abstract

In the context of limited resources and a growing demand for food due to an increase in the worldwide population, irrigation plays a vital role, and the efficient use of water is a major objective. In pressurized irrigation systems, water management is linked to high energy requirements, which is especially relevant in sprinkler irrigation. Therefore, decision support models are important for optimizing the design and management of irrigation systems. In this study, a holistic model for solid set irrigation systems (SORA 2024) was developed. This new model integrates hydraulic models at the subunit and plot levels to evaluate the distribution of pressure (EPANET, Rossman in The EPANET programmer’s toolkit for analysis of water distribution systems, Tempe, Arizona, 1999), the discharge and water distribution for each emitter (SIRIAS, Carrion et al. in , Irrig Sci 20(2):73–84, 2001) and the distribution of water applied by all the emitters of the subunit (SORA, Carrión et al. in Irrig Sci 20(2): 73–84, 2001). The integrated model also includes crop simulation (AQUACROP, Steduto et al. in Agron J 101(3), 426–437, 2009). to assess the effect of water distribution on crop production. The objective of this holistic model is to assist in decision-making processes for designing, sizing, upgrading, and managing solid set irrigation systems at the sprinkler level. The new integrated model (SORA 2024) was applied to a 2.84 ha commercial plot with 2 irrigation sectors that grow onion crops (Allium cepa L.). It was used to analyse each irrigation event from a real irrigation season, considering the conditions (pressure, irrigation time/periods, environmental conditions, and so on). The analysis is based on the sprinkler–nozzle combination, working pressure and wind direction and intensity during each irrigation event. The model also accounts for the cumulative effect/impact of all irrigation events on the plot. The model was validated through field trials using the “crop as a sensor” approach (Sarig et al. in , Agron 11(3):2021). To demonstrate the effectiveness of the model, the choice of nozzles in each sprinkler of the subunit was optimized. This is a quick and cost-effective way for farmers to improve their irrigation systems. By using this method, farmers can achieve better uniformity of water application and a slight increase in crop yield while maintaining the same irrigation schedule and amount of water used. Furthermore, the model enables farmers to work at the emitter level while integrating the results for the entire plot. This allows for precise irrigation of variable dosages by using different sprinkler–nozzle combinations in the same subunit. Farmers can do this based on the prior zoning of the plot, which is determined by its productive potential. This justifies the use of different irrigation dosages in each zone.

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用于精确灌溉的固态喷灌系统整体模拟模型
在资源有限和全球人口增长导致粮食需求不断增加的背景下,灌溉发挥着至关重要的作用,而高效用水则是一个主要目标。在有压灌溉系统中,水管理与高能耗要求相关,这一点在喷灌中尤为重要。因此,决策支持模型对于优化灌溉系统的设计和管理非常重要。在这项研究中,开发了一个用于固体灌溉系统的整体模型(SORA 2024)。这一新模型整合了子单元和地块层面的水力模型,以评估压力分布(EPANET,Rossman,载于《EPANET 程序员分析输水系统的工具包》,亚利桑那州坦佩,1999 年)、每个发射器的排量和水量分布(SIRIAS,Carrion et al.Irrig Sci 20(2):73-84, 2001)和子单元所有喷头的水量分布(SORA,Carrión 等人,Irrig Sci 20(2):73-84, 2001).综合模型还包括作物模拟(AQUACROP,Steduto 等人,载于 Agron J 101(3),426-437,2009 年),以评估配水对作物产量的影响。该综合模型的目的是协助在喷灌层面设计、确定规模、升级和管理固体灌溉系统的决策过程。新的综合模型(SORA 2024)应用于一块 2.84 公顷的商业地块,该地块有 2 个灌溉区,种植洋葱作物(Allium cepa L.)。该模型用于分析实际灌溉季节的每个灌溉事件,并考虑各种条件(压力、灌溉时间/时段、环境条件等)。分析基于每次灌溉过程中的喷头组合、工作压力、风向和风力强度。该模型还考虑了所有灌溉事件对地块的累积效应/影响。该模型采用 "作物作为传感器 "的方法通过田间试验进行了验证(Sarig 等人,Agron 11(3):2021)。为了证明该模型的有效性,对子单元每个喷头的喷嘴选择进行了优化。这是农民改进灌溉系统的一种快速、经济有效的方法。使用这种方法,农民可以在保持灌溉时间和用水量不变的情况下,提高施水均匀度,并略微增加作物产量。此外,该模型还能让农民在整合整个地块结果的同时,在喷头一级开展工作。这样就可以通过在同一子单元中使用不同的喷头组合来实现不同剂量的精确灌溉。农民可以根据地块的生产潜力进行事先分区。这样就可以在每个区域使用不同的灌溉剂量。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
期刊最新文献
Evaluation of the PROMET model for yield estimation and N fertilization in on-farm research Combining 2D image and point cloud deep learning to predict wheat above ground biomass A holistic simulation model of solid-set sprinkler irrigation systems for precision irrigation Integrating NDVI and agronomic data to optimize the variable-rate nitrogen fertilization MangoDetNet: a novel label-efficient weakly supervised fruit detection framework
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