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Journal articles on the topic 'Irrigation DSS'
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1
Alessandro, Massaro, Meuli Giacomo, Savino Nicola, and Galiano Angelo. "A Precision Agriculture DSS Based on Sensor Threshold Management for Irrigation Field." Signal & Image Processing: An International Journal (SIPIJ) 9, no. 6 (2019): 39–58. https://doi.org/10.5281/zenodo.3461592.
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In this paper is analyzed a case study of an industry project concerning irrigation decision support system (DSS) suitable for precision agriculture applications. In particular, a first prototype irrigation module has been developed by testing different components. The prototypal system concerns the irrigation management by reading field and weather values and, by enabling electrovalves through cloud control. A web panel will monitor in real time all sensors data, besides the DSS will activate or disactivate the irrigation pipelines. The irrigation decision is performed by comparing the measurements with pre-set threshold limits of sensor values and by analyzing predicted weather data. The paper describes in details the network design and implementation by discussing the sequence diagram describing the DSS data flow. Finally is proposed the DSS algorithm by discussing the DSS logic and its first implementation. The proposed DSS behaves as an engine processing simultaneously multiple parameters. The goal of the paper is to prove how potentially a microcontroller can perform a DSS which can be customized for different cultivations.
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Alessandro, Massaro. "A Precision Agriculture DSS Based on Sensor Threshold Management for Irrigation Field." Signal & Image Processing: An International Journal (SIPIJ) 9, no. 6 (2019): 39–58. https://doi.org/10.5281/zenodo.2575441.
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In this paper is analyzed a case study of an industry project concerning irrigation decision support system (DSS) suitable for precision agriculture applications. In particular, a first prototype irrigation module has been developed by testing different components. The prototypal system concerns the irrigation management by reading field and weather values and, by enabling electrovalves through cloud control. A web panel will monitor in real time all sensors data, besides the DSS will activate or disactivate the irrigation pipelines. The irrigation decision is performed by comparing the measurements with pre-set threshold limits of sensor values and by analyzing predicted weather data. The paper describes in details the network design and implementation by discussing the sequence diagram describing the DSS data flow. Finally is proposed the DSS algorithm by discussing the DSS logic and its first implementation. The proposed DSS behaves as an engine processing simultaneously multiple parameters. The goal of the paper is to prove how potentially a microcontroller can perform a DSS which can be customized for different cultivations.
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Zucaro, Raffaella, Silvia Baralla, Andrea Arzeni, et al. "Integrating Irrigation Decision Support Systems for Efficient Water Use: A Case Study on Mediterranean Agriculture." Land 14, no. 1 (2024): 5. https://doi.org/10.3390/land14010005.
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Irrigation plays a pivotal role in Southern Europe, and its importance is expected to further increase due to rising climatic variability. Concurrently, the European Water Framework Directive (WFD) mandates the safeguarding of water bodies and the implementation of incentive pricing strategies to promote efficient water management. Within this context, Irrigation Scheduling Decision Support Systems (IS-DSS) could contribute to the achievement of these objectives, although there are still obstacles to their adoption due to uncertainties regarding their potential benefits. This paper aims to derive a pricing model that reflects actual water use through the adoption of an IS-DSS. The innovation of this study lies in showing that adopting an IS-DSS to reduce irrigation volumes can potentially lower concession fees in collective irrigation systems. Thus, it contributes to the fulfilment of the WFD’s objectives regarding incentive water pricing. Notably, the tool is evaluated using the case study of a farm located in the Mediterranean region. The results demonstrate the dual benefits of IS-DSS adoption: on the one hand, it helps preserve water resources with a 24% reduction in irrigation volumes; on the other, it decreases irrigation costs by 20% at the farm level and by 9.4% at the irrigation district level. Therefore, the presented study provides insights into the potential of IS-DSS to enhance water pricing policies to promote efficient water management in Southern European agriculture in alignment with the WFD requirements.
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Garofalo, Simone Pietro, Diego Sebastiano Intrigliolo, Salvatore Camposeo, et al. "Agronomic Responses of Grapevines to an Irrigation Scheduling Approach Based on Continuous Monitoring of Soil Water Content." Agronomy 13, no. 11 (2023): 2821. http://dx.doi.org/10.3390/agronomy13112821.
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The efficient management of irrigation water can affect crop profitability quite significantly. The application of precision irrigation based on soil monitoring can help manage water resources. In viticulture, the irrigation technique is thought to strongly influence grape ripening and the final grape composition. In this study, an irrigation decision support system was compared to a surface drip irrigation system in a commercial vineyard located in Andrea (Southern Italy) planted with Vitis vinifera cv. Montepulciano. We aimed to investigate the ability of the DSS to save water while maintaining an acceptable yield and quality of the grapes. To allow for the comparison, eco-physiological as well as yield parameters were measured during the irrigation periods in both irrigation systems over two years (2019 and 2020). The results indicate that the vines grown using the DSS treatment were less stressed compared to the plants grown using farm irrigation in both years. The yield attributes showed slight or no significant differences between the treatments. The quality results showed no significant differences between the treatments in both years. Our results indicate that with savings of 10% and 17% of the irrigation water in the first and second year, respectively, the DSS was able to maintain good yield and quality levels as compared to the farm irrigation system. These two-year results provide a promising implementation of its use in precision irrigation.
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Evett, Steven R., Susan A. O’Shaughnessy, Manuel A. Andrade, et al. "Theory and Development of a VRI Decision Support System: The USDA-ARS ISSCADA Approach." Transactions of the ASABE 63, no. 5 (2020): 1507–19. http://dx.doi.org/10.13031/trans.13922.
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HighlightsMulti-faceted research efforts converged to an automated irrigation decision support system (DSS).Low-cost, solar-powered, wireless plant abiotic and biotic stress sensors were developed to aid the DSS.Low-cost, accurate TDR soil water sensors and a wireless node and gateway system were developed for the DSS.Sensor systems and research-based algorithms were integrated into an automated irrigation DSS and control system.Abstract. Variable-rate irrigation (VRI) is now possible with every new center pivot irrigation system sold, either using sector (speed) control or both sector and zone (radial along the pipeline) control. However, decision support systems able to generate a prescription for spatially varying irrigation based on crop water need have lagged far behind VRI equipment. Irrigation based on crop water need has been shown to increase both crop water productivity and nutrient use efficiency, meaning that an effective VRI decision support system (DSS) could improve profitability while conserving resources. In this article, we report separately on a VRI DSS using sensor-based plant and soil water feedback as implemented in four U.S. states. This article describes the genesis and development of the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system, of the integral plant and soil sensors, and of its wireless sensor network subsystems, as well as the role of multi-location research efforts and cooperative research and development agreements in the development of the needed plant and soil sensors and the ISSCADA and wireless sensor network systems. Keywords: Crop water productivity, Decision support system, Product development, Sensors, Variable-rate irrigation, VRI.
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Brook, Anna, Keren Salinas, Eugenia Monaco, and Antonello Bonfante. "LCIS DSS—An Irrigation Supporting System for Efficient Water Use in Precision Agriculture." Proceedings 30, no. 1 (2019): 21. http://dx.doi.org/10.3390/proceedings2019030021.
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The sustainable management of water resources is one of the most important topics to face future climate change and food security. Many countries facing a serious water crisis, due to both natural and artificial causes. The efficient use of water in agriculture is one of the most significant agricultural challenges that modern technologies. These last are considered powerful management instruments able to help farmers achieve the best efficiency in irrigation water use and to increase their incomes by obtaining the highest possible crop yield. In this context, within the project “An advanced low cost system for farm irrigation support—LCIS” (a joint Italian Israeli R&D project), a fully transferable Decision Support Systems (DSS) for irrigation support, based on three different methodologies representative of the state of the art in irrigation management tools (W-Tens, in situ soil sensor; IRRISAT®, remote sensing; W-Mod, simulation modelling of water balance in the soil-plant and atmosphere system), has been developed. These three LCIS-DSS tools have been evaluated, in terms of their ability to support the farmer in irrigation management, in a real applicative case study in Italy and Israel. The main challenge of a new DSS for irrigation is attributed to the uncertain factors during the growing season such as weather uncertainty, and crop monitoring platform. For encounter this challenge, we developed during two years the LCIS, a web-based real-time DSS for irrigation scheduling using low-cost imaging spectroscopy for state estimation of the agriculture system and probabilistic short- and medium-term climate forecasts. While the majority of the existing DSS models are incorporated directly into the optimization framework, we propose to integrate continuous feedback from the field (e.g., soil moisture, crop water-stress, plant stage, LAI, and biomass) estimated based on remote sensing information. These field data will be collected by the point-based spectrometer and hyperspectral imaging system. Then a low-cost camera will be designed for specific spectral/spatial parameters (bound to the required feedbacks). The main objectives were: developing real-time Decision Support System (DSS) for optimal irrigation scheduling at farm scale for crop yield improvement, reducing irrigation cost, and water saving; developing a low-cost imaging spectroscopy framework to support the irrigation scheduling DSS above and facilitates its use in countries/places where expensive imaging spectroscopy is not available; examining the developed framework in real-life application, the framework will be calibrated evaluated using high resolution devices and tested using a low-cost system in Israel and Italy farms.
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Darouich, Hanaa, Lucian Simionesei, Ana R. Oliveira, Ramiro Neves, and Tiago B. Ramos. "Assessing the Impact of IrrigaSys Decision Support System on Farmers’ Irrigation Practices in Southern Portugal: A Post Evaluation Study." Agronomy 14, no. 1 (2023): 66. http://dx.doi.org/10.3390/agronomy14010066.
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The IrrigaSys decision support system (DSS) has supported farmers’ decision-making regarding irrigation scheduling in the Sorraia Valley irrigation district in Southern Portugal over a span of six years (2017–2022). This study aims to conduct a postevaluation of farmers’ adherence to the DSS, employing a multicriteria analysis (MCA) approach with data from the 2019 (driest year) and 2020 (average year) growing seasons. Two distinct scenarios were taken into consideration: the first focused on water conservation, and the second centered on farmers’ economic returns. The outcomes of the first scenario revealed that farmers exhibited a reasonable level of expertise, particularly during the driest season. They achieved water-saving indicators comparable to those obtained when adhering to optimized irrigation schedules generated weekly by the DSS. In the wetter season, discrepancies emerged between farmers’ and model indicators, primarily attributed to challenges in integrating reliable information from precipitation forecasts into the decision-making process. In the second scenario, both farmers’ and model results exhibited close economic indicators throughout both seasons. While IrrigaSys requires further developments, these results show that the DSS has effectively contributed to supporting irrigation water management in the study region.
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Almiñana, M., L. F. Escudero, M. Landete, J. F. Monge, A. Rabasa, and J. Sánchez-Soriano. "WISCHE: A DSS for water irrigation scheduling." Omega 38, no. 6 (2010): 492–500. http://dx.doi.org/10.1016/j.omega.2009.12.006.
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Cahn, Michael, Lee Johnson, and Sharon Benzen. "Evapotranspiration-Based Irrigation Management Effects on Yield and Water Productivity of Summer Cauliflower on the California Central Coast." Horticulturae 11, no. 3 (2025): 322. https://doi.org/10.3390/horticulturae11030322.
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Improvements in irrigation water productivity constitute an ongoing effort globally. In California, growers are under regulatory pressure to stabilize groundwater levels and reduce nitrate leaching, partially, by further improvements in irrigation optimization. Evapotranspiration (ET)-based methods can inform crop water requirements and boost irrigation efficiency, but in practice, they can be challenging for farmers to implement, especially in vegetable systems. Irrigation field trials were conducted near Salinas CA in 2018 and 2019 to evaluate the crop coefficient model employed by the CropManage ET-based irrigation decision support system (DSS) for summer cauliflower (Brassica oleracea var. botrytis cv. Symphony) and investigate potential water savings through improved irrigation scheduling. Overhead sprinklers were used for crop establishment, and surface drip was used subsequently. A randomized complete block design was used to administer treatments near 50, 75, 100, and 150% of crop evapotranspiration (ET) during the drip period with an added treatment at 125% in 2019. Water requirement for the 100% treatment was determined by the CropManage DSS model based on crop coefficients derived from fractional canopy cover. Deliveries to remaining treatments were scaled proportionally. The total yield and irrigation productivity were maximized by the 100% treatment both years with total applied water ranging from 275 to 300 mm. At present, the reported water application for summer cauliflower averages 465 mm in the region. Hence, implementing ET-based irrigation scheduling, administered through the CropManage DSS, could reduce water use in summer cauliflower by an average of 30% relative to current practices and serve to enhance groundwater management while maintaining crop returns.
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Adamu , Mohammed, Abioye Mayowa , and Isaac Jonah. "Decision Support System and Fuzzy Logic Controller for Capillary Irrigation System." American Journal of Computing and Engineering 6, no. 1 (2023): 14–28. http://dx.doi.org/10.47672/ajce.1422.
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Purpose: Different irrigation systems exist and they all possess various degrees of benefits in enhancing food sufficiency. In this study however, the enhancement of capillary irrigation system through an integrated fuzzy logic controller with Decision Support System (DSS) to ensure improvement in water saving for irrigation thereby improving crop yield towards food security was examined and achieved.
 Methodology: An integrated fuzzy logic controller with Decision Support System (DSS) for capillary irrigation system was adopted for the enhancement of water saving for irrigation. By using this method, the challenges of irrigation management which is prevalent with capillary irrigation system is minimised using the fuzzy logic controller. An Internet of things (IoT) based weather station for computation of potential evapotranspiration (PET), for measuring rainfall and a VH400 moisture content sensor for monitoring the volumetric water content of soil, were some facilities used to control the water level depth (WLD) autonomously through a fuzzy controller simulated in MATLAB and implemented on Arduino Mega.
 Findings: The soil moisture content (SMC) depicts that fuzzy controlled water level depth (WLD) is able to compensate reduction of water in crop medium that took place due to plant water uptake which changes daily. The result proves that dynamics of water supply depth has substantial effects on the water absorption flow rate, wetting pattern, soil water content and cumulative infiltration which are proportional to the water supply depth decrement. 
 Unique Contribution to Practice: An integrated fuzzy logic controller with Decision Support System (DSS) is a new technique proposed for managing capillary irrigation system as it offers enhanced water saving capacity (irrigation volume) based on crop demand.
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Papathanasiou, Jason, Thomas Bournaris, Georgios Tsaples, Panagiota Digkoglou, and Basil D. Manos. "Applications of DSSs in Irrigation and Production Planning in Agriculture." International Journal of Decision Support System Technology 13, no. 3 (2021): 18–35. http://dx.doi.org/10.4018/ijdsst.2021070102.
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Agricultural management has become an increasingly complex endeavor. As a result, there is the need to generate knowledge from information that can guide practice, and in that aspect, decision support systems (DSSs) can contribute to achieving this goal. A DSS can be defined as a computer-based information system that is used to support complex decision-making. The aim of this paper is to present such a DSS focused on the agricultural sector. Its purpose is to be used for the planning of agricultural production and better utilization of a region's available resources. The development of the DSS relies on the classic theory of such tools utilizing MCDM models, databases, and a user interfaces. The proposed DSS was applied in the prefecture of Larissa in Central Greece. The DSS is adaptable to different contexts, and applications of these capabilities are presented at the end of the paper, applied in different regions under additional objectives and/or constraints.
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Fouial, Abdelouahid, and Juan Antonio Rodríguez Díaz. "DESIDS: An Integrated Decision Support System for the Planning, Analysis, Management and Rehabilitation of Pressurised Irrigation Distribution Systems." Modelling 2, no. 2 (2021): 308–26. http://dx.doi.org/10.3390/modelling2020016.
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Pressurized irrigation distribution systems (PIDSs) play a vital role in irrigation intensification, especially in the Mediterranean region. The design, operation and management of these systems can be complex, as they involve several intertwined processes which need to be considered simultaneously. For this reason, numerous decision support systems (DSSs) have been developed and are available to deal with these processes, but as independent components. To this end, a comprehensive DSS called DESIDS has been developed and tested. This DSS has been developed to bear in mind the needs of irrigation district managers for an integrated tool that can assist them in taking strategic decisions for managing and developing reliable, adequate and sustainable water distribution plans which provide the best services to farmers. Hence, four modules were integrated in DESIDS: (i) irrigation demand and scheduling module; (ii) hydraulic analysis module; (iii) operation and management module; and (iv) design and rehabilitation module. DESIDS was tested on different case studies, proving itself a valuable tool for irrigation district managers, as it provides a wide range of decision options for the proper operation and management of PIDSs. The developed DSS can be used as a platform for future integrations and expansions, and to include other processes needed for better decision-making support.
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Shi, Xiang, Wenting Han, Ting Zhao, and Jiandong Tang. "Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing." Sensors 19, no. 13 (2019): 2880. http://dx.doi.org/10.3390/s19132880.
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Rational utilization of water resources is one of the major methods of water conservation. There are significant differences in the irrigation needs of different agricultural fields because of their spatial variability. Therefore, a decision support system for variable rate irrigation (DSS-VRI) by center pivot was developed. This system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI). The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. The inputs to the fuzzy inference system were constituted with ETc, CWSI and precipitation. To provide guidance for users, the duty-cycle control map was outputted using ambiguity resolution. The control command contained in the map adjusted the duty cycle of the solenoid valve, and then changed the irrigation amount. A water stress experiment was designed to verify the rationality of the DSS-VRI. The results showed that the more severe water stress is, the more irrigation is obtained, consistent with the expected results. Meanwhile, a user-friendly software interface was developed to implement the DSS-VRI function.
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Lilburne, Linda, Jim Watt, and Keith Vincent. "A prototype DSS to evaluate irrigation management plans." Computers and Electronics in Agriculture 21, no. 3 (1998): 195–205. http://dx.doi.org/10.1016/s0168-1699(98)00035-0.
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Gallardo, Marisa, M. Teresa Peña-Fleitas, Francisco M. Padilla, Juan Cedeño, and Rodney B. Thompson. "Prescriptive-Corrective Irrigation and Macronutrient Management in Greenhouse Soil-Grown Tomato Using the VegSyst-DSS v2 Decision Support Tool." Horticulturae 9, no. 10 (2023): 1128. http://dx.doi.org/10.3390/horticulturae9101128.
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This work relates to greenhouse vegetable production in soil in Almeria, Spain. The prescriptive–corrective management (PCM) of irrigation and fertilization (N, P, K, Ca, and Mg) was evaluated. PCM combined recommendations (prescriptive management) for irrigation and nutrients made with the VegSyst-DSS v2, a decision support system, with monitoring (corrective management) using tensiometers (for irrigation) and petiole sap analysis (for nutrients). PCM was compared with conventional farmer management (CONV). The VegSyst-DSS v2 recommends applied nutrient concentrations considering simulated crop uptake, available soil nutrient supply, and evapotranspiration (ETc). This study was conducted with soil-grown tomato in a plastic greenhouse. Nutrients were applied in nutrient solution via drip fertigation. Compared to CONV management, PCM reduced irrigation by 25%, N, K, and Mg application by 40%, Ca by 58%, and P by 85%. There were no significant differences between treatments in fruit production and quality, despite appreciable reductions in irrigation and nutrient application. An economic analysis indicated that in this 7-month tomato crop, PCM compared to CONV management was associated with a financial saving of 1611 € ha−1. These results showed that by using prescriptive–corrective fertigation management, based on the VegSyst-DSS v2, considerable savings can be achieved in water and nutrient (N, P, K, Ca, and Mg) inputs to greenhouse tomato without compromising production. This can reduce farmer costs and the environmental impact associated with these greenhouse production systems.
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Chandrashekhara, K.T*1 Dr. Thungamani.M2 Apoorva Mathur3 Harshitha Basavaraju4 Faizan Ansari5 &. Saket Mahendra6. "PRECISION IRRIGATION USING INTEGRATED INTELLIGENT SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 562–66. https://doi.org/10.5281/zenodo.817952.
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Agriculture, the backbone of Indian economy, contributes to the overall economic growth of the country and determines the standard of life for more than 50% of the Indian population. Irrigation consumes more than 80% of the total water use in the country thereby requiring systems to build decision support systems (DSS) to overcome the problem of water wastage and deficit. In general, this paper presents an Intelligent System as an alternative and efficient way to solve the farming resources optimization and decision making. Precision agriculture systems based on the Internet of Things (IOT) technology is explained in detail especially on the hardware and network architecture and software process control of the precision irrigation system. The system collect, analyse and monitors data from the sensors in a feedback loop which activates the control devices based on pre-calculated threshold value.
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Kumar, Jitendra, Neelam Patel, Pramod Kumar Sahoo, et al. "Development of Sensor and Decision Support System (DSS)-based Automated Irrigation System for Enhancing Water Productivity of Tomato Crop." Journal of Soil Salinity and Water Quality 16, no. 2 (2024): 307–16. http://dx.doi.org/10.56093/jsswq.v16i2.156426.
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In India, given the rising trend of population growth, climate change and the need to increase agricultural production with efficient utilization of water, It is crucial to execute precise water management strategies in the farmland. In this study, efforts were made towards development of an automated system for irrigation scheduling on real-time basis considering the soil moisture conditions and crop parameters, and evaluation of performance under different irrigation methods in tomato crop. The soil moisture sensor was integrated with decision support system (DSS) and microcontroller using internet and global system for mobile communication (GSM) module for automated irrigation. The developed sensor was compared with Frequency Domain Reflectometer (FDR), tensiometer, and watermark sensor and was calibrated using the gravimetric method. The crop and irrigation water productivity of tomato crop ranged from 5.2–12.6 kg m-3 for control and automated systems, and 7.7–18.7 kg m-3 for the latter under different methods of irrigation. By using an automated drip irrigation system instead of a manually operated check basin irrigation system, cultivators of tomatoes were able to save 39.61% of the water. In terms of economics analysis, highest benefit cost ratio were obtained under manually operated drip irrigation (2.61) followed by automated drip irrigation system (2.50) in tomato crop.
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Fotia, Konstantina, Andi Mehmeti, Ioannis Tsirogiannis, et al. "LCA-Based Environmental Performance of Olive Cultivation in Northwestern Greece: From Rainfed to Irrigated through Conventional and Smart Crop Management Practices." Water 13, no. 14 (2021): 1954. http://dx.doi.org/10.3390/w13141954.
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Olive cultivation is expanding rapidly in the northwestern part of Greece, under both rainfed and irrigated practices. Irrigation can result in larger yields and economic returns, but trade-offs in the water–energy–pollution nexus remain a controversial and challenging issue. This study presents an environmental Life Cycle Assessment (LCA) of Greek olive orchard systems in the plain of Arta (Epirus), comparing rainfed (baseline), Decision Support System (DSS)-based (smart) irrigation practices and farmer experience-based (conventional) irrigation practices. The contributions in this paper are, first, to provide a first quantitative indication of the environmental performance of Greek olive growing systems under different management strategies, and second, to detail the advantages that can be achieved using smart irrigation in olive cultivation in the Greek and Mediterranean contexts. Eighteen midpoints (e.g., climate change, water scarcity, acidification, freshwater eutrophication, etc.), two endpoints (damages on human health and ecosystem quality), and a single score (overall environmental impact) were quantified using the IMPACT World+ life cycle impact assessment method. The LCA model was set up using the OpenLCA software v1.10.3. The functional units were 1 ton of product (mass-based) and 1 ha of cultivated area (area-based) on a cradle-to-farm gate perspective. Irrigated systems had the lowest impacts per mass unit due to higher yields, but showed the highest impacts per cultivated area. The DSS-based irrigation management could reduce water and energy use by 42.1% compared to conventional practices. This is translated into a reduction of 5.3% per 1 ton and 10.4% per 1 ha of the total environmental impact. A sensitivity analysis of impact assessment models demonstrated that the benefits could be up to 18% for 1 ton of product or 22.6% for 1 ha of cultivated land. These results outline that DSS-based irrigation is a promising option to support less resource-intensive and sustainable intensification of irrigated agriculture systems in the plain of Arta.
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Jeong, Ju Hui, and Wook Oh. "Drought and Darkness during Long-Term Simulated Shipping Delay Post-Shipping Flowering of Phalaenopsis Sogo Yukidian ‘V3’." Horticulturae 7, no. 11 (2021): 483. http://dx.doi.org/10.3390/horticulturae7110483.
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We investigated the relationship between simulated shipping (SS) without watering or light and post-shipping growth and flowering of Phalaenopsis Sogo Yukidian ‘V3’. Two experimental environments were created: a low-temperature chamber for simulated shipping and a growth chamber for simulated finishing at the destination. Plants from both the control and treatment groups were moved from the low-temperature chamber to the growth chamber after the end of the simulated shipping. Control plants received continuous light and regular irrigation; plants in the treatment group were placed in the low-temperature chamber under light (LSS) or dark (DSS) conditions for 10, 20, 30, 40, or 50 days, without irrigation. Once DSS duration exceeded 40 days, the leaf-yellowing rate increased rapidly. Chlorophyll content decreased from day 10 to 30 of DSS and slightly increased in LSS and DSS over 40 days. The photochemical reflectance index decreased with the SS duration. The maximum quantum yield PSII photochemistry (Fv/Fm) values sharply decreased after the end of SS; after 40 days, neither LSS nor DSS plants recovered to the normal range. In the same SS duration, the number of days to spiking was delayed in the DSS. In addition, the number of days to spiking was delayed, owing to the longer SS duration. LSS for 50 days induced early flowering, as in the control group, but lowered flower quality. The results demonstrate that drought stress from long-term shipping (>40 days) delayed flowering. In particular, DSS delayed flowering more than LSS due to the decrease in chlorophyll content and the reduction in carbohydrates through respiration.
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Fatmawati, Triana, Yan Watequlis Syaifudin, Alfiandi Rahmadani, Maulana Rosandy, and Htoo Htoo Sandi Kyaw. "Optimizing Irrigation Infrastructure Management with Web-Based Technologies and OpenStreetMap Integration." International Journal of Frontier Technology and Engineering 3, no. 1 (2024): 54–68. https://doi.org/10.33795/ijfte.v3i1.6280.
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Irrigation is essential for increasing agricultural productivity in Indonesia, directly impacting national food security and farmer welfare by ensuring the sustainability of irrigation systems. Despite its importance, significant challenges persist, including damage to 46% of the irrigation infrastructure across 7.1 million hectares, with responsibilities divided among central, provincial, and city governments. These challenges are compounded by issues such as inadequate information access, limitations in real-time monitoring, and poor stakeholder coordination. To address these issues, integrating technological tools such as Geographic Information Systems (GIS) and platforms such as OpenStreetMap (OSM) is essential. These technologies offer real-time information, facilitate land change tracking, and enable spatial mapping, which supports proactive management and planning while improving decision making through data-driven insights. The adoption of Rapid Application Development (RAD) and Decision Support Systems (DSS) further enhances the efficiency of irrigation system management. The RAD methodology ensures swift development cycles with user interaction, while DSS provides structured decision-making capabilities. This approach supports robust system design, user interface development, and geographic data visualization. In practice, efficient data collection and stakeholder participation enable the system to effectively report and track irrigation conditions, promoting public participation, and fostering long-term infrastructure sustainability. The developed online monitoring system demonstrates its effectiveness by providing interactive map features and transparent reporting platforms, verified through successful implementation of testing scenarios. This system not only improves infrastructure management in areas like Batu City, but also sets a precedent for scalable participatory irrigation solutions, creating a foundation for future enhancements.
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Ramos, Tiago B., Lucian Simionesei, Ana R. Oliveira, Ramiro Neves, and Hanaa Darouich. "Exploring the Use of Vegetation Indices for Validating Crop Transpiration Fluxes Computed with the MOHID-Land Model. Application to Vineyard." Agronomy 11, no. 6 (2021): 1228. http://dx.doi.org/10.3390/agronomy11061228.
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The success of an irrigation decision support system (DSS) much depends on the reliability of the information provided to farmers. Remote sensing data can expectably help validate that information at the field scale. In this study, the MOHID-Land model, the core engine of the IrrigaSys DSS, was used to simulate the soil water balance in an irrigated vineyard located in southern Portugal during three growing seasons. Modeled actual basal crop coefficients and transpiration rates were then compared with the corresponding estimates derived from the normalized difference vegetation index (NDVI) computed from Sentinel-2 imagery. On one hand, the hydrological model was able to successfully estimate the soil water balance during the monitored seasons, exposing the need for improved irrigation schedules to minimize percolation losses. On the other hand, remote sensing products found correspondence with model outputs despite the conceptual differences between both approaches. With the necessary precautions, those products can be used to complement the information provided to farmers for irrigation of vine crop, further contributing to the regular validation of model estimates in the absence of field datasets.
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Zia, Huma, Ahsan Rehman, Nick R. Harris, Sundus Fatima, and Muhammad Khurram. "An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm." Sensors 21, no. 12 (2021): 4175. http://dx.doi.org/10.3390/s21124175.
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Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.
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Mannini, P., R. Genovesi, and T. Letterio. "IRRINET: Large Scale DSS Application for On-farm Irrigation Scheduling." Procedia Environmental Sciences 19 (2013): 823–29. http://dx.doi.org/10.1016/j.proenv.2013.06.091.
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Jacucci, G., C. Uhrik, G. Bertanzon, et al. "Integrating an Expert System Component into the Hydra Irrigation DSS." IFAC Proceedings Volumes 28, no. 4 (1995): 91–96. http://dx.doi.org/10.1016/s1474-6670(17)45546-7.
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P. W. Jayasuriya, Hemanatha, Ahmed Al-Busaidi, and Mushtaque Ahmed. "Development of a decision support system for precision management of conjunctive use of treated wastewater for irrigation in Oman." Journal of Agricultural and Marine Sciences [JAMS] 22, no. 1 (2018): 58. http://dx.doi.org/10.24200/jams.vol22iss1pp58-62.
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This research aimed at finding alternative options for conjunctive use of treated wastewater (TW) with groundwater (GW) minimizing the irrigation water from aquifers in the Al-Batinah region with the assistance of a Decision Support System (DSS). Oman is facing a three-facet problem of lowering of GW table, wastewater over-production and excess TW. Approved guidelines for use of TW with tertiary treatments are of two classes: class-A (for vegetables consumed raw), class-B (after cooking). The developed DSS is comprised of four management subsystems: (1) data management in Excel, (2) model and knowledge management by macro programming in Excel, (3) with linear programming (LP) optimization models including transportation algorithms, and (4) user interface with Excel or Visual Basic (VB). The results are based on two extreme scenarios: zero TW excess, and zero GW used for irrigation. The DSS could predict water balance for number of crop rotations, and based on adjustable cost variables farmer profit margins could be created. Crop selections and rotation could be done using LP optimizations while transportation algorithm could organize best locations and capacities for treatment plants and the wastewater collection and transportation to farming areas via treatment plants. The developed DSS will be very useful as a water management, optimization and planning tool.
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Torres-Sanchez, Roque, Honorio Navarro-Hellin, Antonio Guillamon-Frutos, Rubén San-Segundo, Maria Carmen Ruiz-Abellón, and Rafael Domingo-Miguel. "A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques." Water 12, no. 2 (2020): 548. http://dx.doi.org/10.3390/w12020548.
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Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.
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Jaiswal, R. K., N. C. Ghosh, Poonam Guru, and Devakant. "MIKE BASIN Based Decision Support Tool for Water Sharing and Irrigation Management in Rangawan Command of India." Advances in Agriculture 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/924948.
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In this study, MIKE BASIN has been used as a decision support tool for irrigation management and water sharing of Rangawan reservoir, an interstate project of Madhya Pradesh and Uttar Pradesh in India. The water sharing and optimum irrigation releases have been analyzed by developing two separate models in decision support tool; the first model computes irrigation demand and offers inputs to the second model, which calculates water supplies and deficits as per the water sharing agreements between the two states. The models have been used to generate twelve different scenarios for evaluation of irrigation demands, water supply, and demand deficit/excess for actual cropping pattern in command of Madhya Pradesh part. Simulated results showed, in average/wet rainfall year with conveyance efficiency of 60% and application efficiency of 70%, the irrigation demand of 11.83 Mm3has been found satisfying without any deficit. By improving efficiencies, conjunctive use, and managing irrigation supplies as recommended from scenarios of DSS application, more areas in the command can be brought under irrigation. The developed models can be used for real time reservoir operation and irrigation planning under variable climatic conditions, conveyance and application efficiencies, consumptive use of surface and groundwater, and probable runoff and cropping pattern.
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Andrade, Manuel A., Susan A. O’Shaughnessy, and Steven R. Evett. "ARSPivot, A Sensor-Based Decision Support Software for Variable-Rate Irrigation Center Pivot Systems: Part A. Development." Transactions of the ASABE 63, no. 5 (2020): 1521–33. http://dx.doi.org/10.13031/trans.13907.
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HighlightsThe ARSPivot software seamlessly integrates site-specific irrigation scheduling methods with weather, plant, and soil water sensing systems in the operation of variable-rate irrigation (VRI) center pivot systems.ARSPivot embodies an Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system that incorporates site-specific irrigation scheduling methods and automates the collection and processing of data obtained from sensing systems supporting them.ARSPivot incorporates a friendly graphical user interface (GUI) that assists in the process of setting up a computerized representation of a coupled ISSCADA VRI center pivot system and simplifies the review of irrigation prescriptions automatically generated based on sensor feedback.ARSPivot’s GUI includes a geographic information system (GIS) that relates sensed data and imported GIS data to specific field control zones.Abstract. The commercial availability of variable-rate irrigation (VRI) systems gives farmers access to unprecedented control of the irrigation water applied to their fields. To take full advantage of these systems, their operations must integrate site-specific irrigation scheduling methods that in turn should be supported by a network of sensing systems. An Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system patented by scientists with the USDA-Agricultural Research Service (ARS) at Bushland, Texas, incorporates site-specific irrigation scheduling methods informed by weather, plant, and soil water sensing systems. This article introduces a software package, ARSPivot, developed to integrate the ISSCADA system into the operation of VRI center pivot systems. ARSPivot assists the operation and integration of a complex network of sensing systems, irrigation scheduling methods, and irrigation machinery to achieve this end. ARSPivot consists of two independent programs interacting through a client-server architecture. The client program is focused on automatically collecting and processing georeferenced data from sensing systems and communicating with a center pivot control panel, while the server program is focused on communicating with users through a friendly graphical user interface (GUI) involving a geographic information system (GIS). The GUI allows users to visualize and modify site-specific prescription maps automatically generated based on sensor-based irrigation scheduling methods, and to control and monitor the application of irrigation amounts specified in these recommended prescription maps using center pivots equipped for VRI zone control or VRI speed control. This article discusses the principles and design considerations followed in the development of ARSPivot and presents tools implemented in the software for the virtual design and physical operation of a coupled ISSCADA VRI center pivot system. This article also illustrates how the ISSCADA system and ARSPivot constitute a comprehensive sensor-based decision support system (DSS) for VRI management that is accessible to users without in-depth knowledge of sensing systems or irrigation scheduling methods. Keywords: Center pivot irrigation, Decision support system, Precision agriculture, Sensors, Site-specific irrigation scheduling, Software, Variable rate irrigation.n
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Tolomio, Massimo, and Raffaele Casa. "Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements." Remote Sensing 12, no. 23 (2020): 3945. http://dx.doi.org/10.3390/rs12233945.
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Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effects on crop growth and development. Crop models can be categorized into (i) water-driven and (ii) radiation-driven, depending on the main variable governing crop growth. Water stress is calculated starting from (i) soil water content or (ii) transpiration deficit. The stress affects relevant features of plant growth and development in a similar way in most models: leaf expansion is the most sensitive process and is usually not considered when planning irrigation, even though prolonged water stress during canopy development can consistently reduce light interception by leaves; stomatal closure reduces transpiration, directly affecting dry matter accumulation and therefore being of paramount importance for irrigation scheduling; senescence rate can also be increased by severe water stress. The mechanistic concepts of crop models can be used to improve existing simpler methods currently integrated in irrigation management DSS, provide continuous simulations of crop and water dynamics over time and set predictions of future plant–water interactions. Crop models can also be used as a platform for integrating information from various sources (e.g., with data assimilation) into process-based simulations.
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Ahmad, Uzair, Arturo Alvino, and Stefano Marino. "Solar Fertigation: A Sustainable and Smart IoT-Based Irrigation and Fertilization System for Efficient Water and Nutrient Management." Agronomy 12, no. 5 (2022): 1012. http://dx.doi.org/10.3390/agronomy12051012.
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The agricultural sector is one of the major users of water resources. Water is an important asset that needs to be preserved using the latest available technologies. Modern technologies and digital tools can transform the agricultural domain from being manual and static to intelligent and dynamic leading to higher production with lesser human supervision. This study describe the agronomic models that should be integrated with the intelligent system which schedule the irrigation and fertilization according to the plant needs, and monitors and maintains the desired soil moisture content via automatic watering. Solar fertigation is a fertigation support system based on photovoltaic solar power energy and an IoT system for precision irrigation purposes. The system monitors the temperature, radiation, humidity, soil moisture, and other physical parameters. An agronomic DSS platform based on the integration of soil, weather, and plant data and sensors was described. Furthermore, a three-year study on seven ETo models, such as three temperature-, three radiation-, and a combination-based models were tested to evaluate the sustainable ETo estimation and irrigation scheduling in a Mediterranean environment. Results showed that solar fertigation and Hargreaves–Samani (H-S) equation represented a nearby correlation to the standard FAO P–M and does offer a small increase in accuracy of ETo estimates. Furthermore, the hybrid agronomic DSS is suitable for smart fertigation scheduling.
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Arshad, Jehangir, Musharraf Aziz, Asma A. Al-Huqail, et al. "Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield." Sustainability 14, no. 2 (2022): 827. http://dx.doi.org/10.3390/su14020827.
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A majority of the population of developing countries is associated with agriculture directly or indirectly. The liaison of engineering technology and Sustainable Development Goals (SDGs) can build a bridge for farmers to enhance their skills regarding advancements through future generation agriculture trends. The next-generation trends include better soil preparation, intelligent irrigation systems, advanced methods of crop nutrient inspection, smart fertilizers applications, and multi-cropping practices. This work proposes a smart Decision Support System (DSS) that acquires the input parameters based on real-time monitoring to optimize the yield that realizes sustainability by improving per hectare production and lessening water seepage wastage in agribusiness. The proposed model comprises three basic units including an intelligent sensor module, smart irrigation system and controlled fertilizer module. The system has integrated sensors, cloud employing decision support layers, and networking based DSS to recommend cautions for optimum sustainable yield. The intelligent sensors module contains a temperature and humidity sensor, NPK sensor, soil moisture sensor, soil conductivity sensor, and pH sensor to transmit the statistics to the cloud over the internet via Long Range (LoRa) using Serial Peripheral Interface (SPI) communication protocol. Moreover, an android application has been developed for real-time data monitoring according to GPS location and node information (accessed remotely). Furthermore, the DSS contemplates the accessible information from sensors, past patterns, monitoring climate trends and creating cautions required for sustainable fertilizer consumption. The presented results and comparison validate the novelty of the design as it embraces smart irrigation with smart control and smart decision-making based on accurate real-time field data. It is better than existing systems as it transmits the data over the LoRa that is an open-source communication with long-range transmission ability up to several kilometres. The sensor nodes helped in advancing the yield of crops, which resulted in achieving inclusive and sustainable economic goals.
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NAIN, A. S., and K. K. SINGH. "Conceptualization of a framework of decision support system for agriculture in hilly region." MAUSAM 67, no. 1 (2021): 195–204. http://dx.doi.org/10.54302/mausam.v67i1.1178.
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Decision support system (DSS) in agriculture helps farming community to take appropriate decision as per the situation to maximize economic return by enhancing productivity and reducing the cost of inputs. The prime most purpose of DSS is to enhance the input use efficiency by applying the input when it is needed most. The requirement of DSS in the hilly states is being felt more as environmental conditions vary greatly in tempo-spatial domain. Climate change associated with increasing probability of extreme weather conditions has further deepened the need of DSS. There have been many attempts in the past to use / develop DSS in the hilly regions. The serious efforts in this direction were initiated by fine tuning the Decision Support System for Agrotechnology Transfer (DSSAT) in Indian conditions. DSSAT helps to take appropriate decisions on selection of cultivar, sowing time, irrigation, fertilization and harvesting of crops. Of late geospatial technology alone and in combination with crop simulation model has also been used to develop DSS. Present paper underlines the efforts of researchers / academicians to develop DSS in hilly states with their usability and limitations. Paper also conceptualizes a framework of DSS for hilly regions by integrating a forewarning system and agriculture expert system.
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Massaro, Alessandro, Giacomo Meuli, Nicola Savino, and Angelo Galiano. "A PRECISION AGRICULTURE DSS BASED ON SENSOR THRESHOLD MANAGEMENT FOR IRRIGATION FIELD." Signal & Image Processing : An International Journal 9, no. 6 (2018): 39–58. http://dx.doi.org/10.5121/sipij.2018.9604.
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Fouial, Abdelouahid, Nicola Lamaddalena, and Juan Antonio Rodríguez Díaz. "Generating Hydrants’ Configurations for Efficient Analysis and Management of Pressurized Irrigation Distribution Systems." Water 12, no. 1 (2020): 204. http://dx.doi.org/10.3390/w12010204.
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Water scarcity is a mounting problem in arid and semi-arid regions such as the Mediterranean. Therefore, smarter and more effective water management is required, especially in irrigated agriculture. One of the most challenging uncertainties in the operation of on-demand collective Pressurized Irrigation Distribution Systems (PIDSs) is to know, a priori, the number and the position of hydrants in simultaneous operation. To this end, a model was developed to generate close to reality operating hydrants configurations, with 15, 30 or 60 min time steps, by estimating the irrigation scheduling for the entire irrigation season, using climatic, crop and soil data. The model is incorporated in an integrated DSS called Decision Support for Irrigation Distribution Systems (DESIDS) and links two of its modules, namely, the irrigation demand and scheduling module and the hydraulic analysis module. The latter is used to perform two types of analyses for the performance assessment and decision-making processes. The model was used in a real case study in Italy to generate hydrants’ operation taking into consideration irrigation scheduling. The results show that during the peak period, hydrants simultaneity topped 62%. The latter created pressure deficit in some hydrants, thus reducing the volume of water supplied for irrigation by up to 87 m3 in a single hydrant during the peak demand day. The developed model proved to be an important tool for irrigation managers, as it provides vital information with great flexibility and the ability to assess and predict the operation of PIDSs at any period during the irrigation season.
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Tsirogiannis, Ioannis L., Nikolaos Malamos, and Penelope Baltzoi. "Application of a Generic Participatory Decision Support System for Irrigation Management for the Case of a Wine Grapevine at Epirus, Northwest Greece." Horticulturae 9, no. 2 (2023): 267. http://dx.doi.org/10.3390/horticulturae9020267.
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In southern Europe, irrigation is the major water user and thus, development of operational tools that support decisions aiming to improve irrigation management, is of great importance. In this study, a web-based participatory decision support system for irrigation management (DSS), based on the principles of UN FAO’s paper 56, without requirement for any special monitoring hardware to be installed in each field, is evaluated for the case of a commercial wine grapevine (Vitis vinifera ‘Vertzami’) located at Epirus (northwest Greece), for two successive years (2021 and 2022). The soil moisture time series that were generated by the DSS’s model were compared to those measured by soil moisture sensors. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) ranged between 2.98–3.22% and 3.63–4.06%, respectively, under various irrigation practices and goals. Irrigation resulted very high yields and Crop Water Productivity (WPC) was 20–44% improved when following the DSS’s recommendations. The results also confirm potential pitfalls of sensor-based soil moisture monitoring and rainfall estimations using mathematical models. Finally, the value of water meters as practical sensors, which could support efficient irrigation management, is underlined. In every case, mindful application of decision support systems that require minimum or no hardware to be installed in each field, could extensively support growers and agronomic consultants to test, document and disseminate good practices and calculate environmental indices.
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Visconti, F., M. de la Fuente, I. Buesa, et al. "Decision support system for selecting the rootstock, irrigation regime and nitrogen fertilization in winemaking vineyards: WANUGRAPE4.0." BIO Web of Conferences 68 (2023): 01032. http://dx.doi.org/10.1051/bioconf/20236801032.
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We aim to develop and transfer to the wine sector a decision support system (DSS) in the frame of WANEGRAPE4.0 project that, integrated into a geographic information system, helps wine growers in i) selecting the most suitable rootstock given some agroecological conditions and oenological objectives; and ii) managing irrigation and nitrogen fertilization in the most suitable way for the selected rootstock and agroecological conditions. The following goals have been achieved. First, the modular structure and information flow of the DSS has been defined. Second, the main algorithms of the water balance module (DSS core part) have been formulated and the module coded in a spreadsheet. Third, this water balance module has been tested with data from field experiments in several regions of Spain. Fourth, the relationships between grapevine water status and production and harvest quality variables have been established, revealing an always-significant effects of the decrease in water stress on vegetative development, yield, and grape composition. Fifth, the nitrogen fertilizer effects on vine performance has been assessed. Sixth, the effects rootstocks have on 5 parameters of vine production and grape quality for winemaking have been established too by doing another meta-analysis of rootstock trials. Seventh, a rootstock selection module has been defined. The WANUGRAPE4.0 project goes on with the integration of all its modules, their coding in a World Wide Web language and their publication on an Internet portal.
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Musazura, William, and Alfred O. Odindo. "Suitability of the Decentralised Wastewater Treatment Effluent for Agricultural Use: Decision Support System Approach." Water 13, no. 18 (2021): 2454. http://dx.doi.org/10.3390/w13182454.
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The decentralised wastewater treatment system (DEWATS) is an onsite sanitation technology that can be used in areas away from municipal sewerage networks. The discharge of effluent emanating from DEWATS into water bodies may cause pollution. Agricultural use of the effluent may improve crop yields and quality thereby contributing to food security in low-income communities. There are drawbacks to the agricultural use of treated wastewater. Therefore, the study assessed the crop, environmental and health risks when irrigating with anaerobic filter (AF) effluent using the Decision Support System (DSS) of the South African Water Quality Guideline model, in four South African agroecological regions, three soil types, two irrigation systems and three different crops. The model was parameterised using AF effluent characterisation data and simulated for 45 years. The model predicted that there are no negative impacts for using AF effluent on soil quality parameters (root zone salinity, soil permeability and oxidisable carbon loading), leaf scorching and irrigation equipment. The problems were reported for nutrient loading (N and P) in maize and microbial contamination in cabbage and lettuce. It was recommended that the effluent should be diluted when used for maize production and advanced treatment should be explored to allow unrestricted agricultural use.
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Acutis, Marco, Alessia Perego, Ettore Bernardoni, and Michele Rinaldi. "AQUATER Software as a DSS for Irrigation Management in Semi-Arid Mediterranean Areas." Italian Journal of Agronomy 5, no. 2 (2010): 205. http://dx.doi.org/10.4081/ija.2010.205.
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Patel, Jignesh, and Chetan Bhatt. "A CommonKADS Model Framework for Web Based Agricultural Decision Support System." International Journal on Food System Dynamics 5, no. 4 (2014): 196–203. https://doi.org/10.18461/ijfsd.v5i4.545.
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Increased demand of farm products and depletion of natural resources compel the agriculture community to increase the use of Information and Communication Technology (ICT) in various farming processes. Agricultural Decision Support Systems (DSS) proved useful in this regard. The majority of available Agricultural DSSs are either crop or task specific. Less emphasis has been placed on the development of comprehensive DSS, which are non-specific regarding crops or farming processes. The crop or task specific DSSs are mainly developed with rule based or knowledge transfer based approaches. The DSSs based on these methodologies lack the ability for scaling up and generalization. The Knowledge engineering modeling approach is more suitable for the development of large and generalized DSS. Unfortunately the model based knowledge engineering approach is not much exploited for the development of Agricultural DSS. CommonKADS is one of the popular modeling frameworks used for the development of Knowledge Based System (KBS). The paper presents the organization, agent, task, communication, knowledge and design models based on the CommonKADS approach for the development of scalable Agricultural DSS. A specific web based DSS application is used for demonstrating the multi agent CommonKADS modeling approach. The system offers decision support for irrigation scheduling and weather based disease forecasting for the popular crops of India. The proposed framework along with the required expert knowledge, provides a platform on which the larger DSS can be built for any crop at a given location.
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Sanchez Vizcaino, J., J. I. Corcoles, M. A. Moreno, and J. M. Tarjuelo. "Groundwater Extraction with Minimum Cost: Application to Sprinkler Irrigation Systems for Corn Crop in Spain." Agrociencia 19, no. 3 (2015): 46–58. http://dx.doi.org/10.31285/agro.19.271.
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The aim of this study was to develop a DSS tool named DC-WAT, which linked with the already developed PRESUD tool, aims to optimize, in a holistic manner, the process of water extraction from an aquifer and its application in plot with a pressurized irrigation systems, obtaining the minimum total water application cost (CT) (operation (Cop) + investment (Ca)) per unit irrigated area improving water and energy management. This tool permits identifying the cost for transporting water from the source to the irrigation subunit inlet (Cws) and analyzing the irrigation system as a whole, from the water source to the emitter. An application to permanent sprinkler irrigation systems using groundwater of two types of aquifer (confined and unconfined aquifers) for corn crop in Spain is analyzed, evaluating the effects on CT of parameters such as the static water table in the aquifer (SWT), irrigated area (S), sprinklers and laterals spacing and average application rate (ARa). Results showed that Cws increased lineally with SWT and decreased exponentially with S. The timing of crops water requirements, the efficiency of the irrigation system, and the size of the irrigation subunit, among other factors, determine the optimal pumping flow rate and the cost of energy. For the aquifers studied, the Cws was mainly conditioned by the borehole investment cost, being the confined aquifer 30-60% more expensive than the unconfined for the studied cases. The Ce is the most important cost of CT (65-70 %in the studied cases). DC-WAT is a useful tool to optimize the design and sizing of water pumping facilities in irrigation systems, which considers the aquifer performance in a holistic manner.
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du Plessis, Heinrich M., John G. Annandale, and Nico Benadé. "A Decision Support System That Considers Risk and Site Specificity in the Assessment of Irrigation Water Quality (IrrigWQ)." Applied Sciences 13, no. 23 (2023): 12625. http://dx.doi.org/10.3390/app132312625.
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Irrigators are increasingly challenged to maintain or even increase production using less water, sometimes of poorer quality, and often from unconventional sources. This paper describes the main features of a newly developed software-based Decision Support System (DSS), with which the fitness for use (FFU) of water for irrigation (IrrigWQ) can be assessed. The assessment considers site-specific factors, several non-traditional water constituents, and the risk of negative effects. The water balance components of a cropping system and the redistribution of solutes within a soil profile are assessed with a simplified soil water balance and chemistry model. User-friendly, colour-coded output highlights the expected effects of water constituents on soil quality, crop yield and quality, and irrigation infrastructure. Because IrrigWQ uses mainly internationally accepted cause–effect relationships to assess the effect of water quality constituents, it is expected to find universal acceptance and application among users. IrrigWQ also caters for calculating so-called Water Quality Requirements (WQRs). WQRs indicate the threshold levels of water quality constituents for irrigation at specified levels of acceptability or risk. WQRs assist water resource managers in setting site-specific maximum threshold levels of water quality constituents that can be tolerated in a water source before impacting negatively on successful irrigation.
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Bellvert, Joaquim, Ana Pelechá, Magí Pamies-Sans, Jordi Virgili, Mireia Torres, and Jaume Casadesús. "Assimilation of Sentinel-2 Biophysical Variables into a Digital Twin for the Automated Irrigation Scheduling of a Vineyard." Water 15, no. 14 (2023): 2506. http://dx.doi.org/10.3390/w15142506.
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Decision support systems (DSS) are needed to carry out precision irrigation. Key issues in this regard include how to deal with spatial variability and the adoption of deficit irrigation strategies at the field scale. A software application originally designed for water balance-based automated irrigation scheduling locally fine-tuned through the use of sensors has been further developed with the emerging paradigm of both digital twins and the Internet of Things (IoT). The aim of this research is to demonstrate the feasibility of automatically scheduling the irrigation of a commercial vineyard when adopting regulated deficit irrigation (RDI) strategies and assimilating in near real time the fraction of absorbed photosynthetically active radiation (fAPAR) obtained from Sentinel-2 imagery. In addition, simulations of crop evapotranspiration obtained by the digital twin were compared with remote sensing estimates using surface energy balance models and Copernicus-based inputs. Results showed that regression between instantaneous fAPAR and in situ measurements of the fraction of intercepted photosynthetically active radiation (fIPAR) had a coefficient of determination (R2) ranging from 0.61 to 0.91, and a root mean square deviation (RMSD) of 0.10. The conversion of fAPAR to a daily time step was dependent on row orientation. A site-specific automated irrigation scheduling was successfully adopted and an adaptive response allowed spontaneous adjustments in order to stress vines to a certain level at specific growing stages. Simulations of the soil water balance components performed well. The regression between digital twin simulations and remote sensing-estimated actual (two-source energy balance Priestley–Taylor modeling approach, TSEB-PTS2+S3) and potential (Penman–Monteith approach) evapotranspiration showed RMSD values of 0.98 mm/day and 1.14 mm/day, respectively.
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Chauhan, Mayank, Radhika Negi, Ravi, et al. "Empowering Small-Scale Vegetable Farmers with Drone-based Decision Support Systems for Sustainable Production." International Journal of Environment and Climate Change 15, no. 1 (2025): 203–16. https://doi.org/10.9734/ijecc/2025/v15i14685.
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In India, small-scale vegetable farmers face significant challenges in achieving sustainable and profitable production due to limited access to modern technologies and decision support tools. This study explores the potential of drone-based remote sensing and decision support systems (DSS) to empower small-scale farmers and promote sustainable vegetable production practices. The research involved deploying multispectral sensor-equipped drones over 50 smallholder vegetable farms in the state of Maharashtra, India to collect high-resolution crop health and growth data across multiple growing seasons. The aerial data was processed and integrated into a cloud-based DSS platform that provided participating farmers with actionable insights and recommendations to optimize irrigation, fertilization, pest/disease control, and harvest scheduling. The DSS also incorporated weather forecasts, market price information, and expert agronomic knowledge to help farmers make informed decisions. However, challenges remain in building digital literacy, trust, and infrastructure to enable wider adoption among smallholder farmers. Future work should focus on participatory design of DSSs, integration with existing agricultural extension services, and inclusive business models for delivering precision agriculture technologies to small-scale farmers in developing countries.
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Jewpanya, Parida, Josephine D. German, Pinit Nuangpirom, Meilinda Fitriani Nur Maghfiroh, and Anak Agung Ngurah Perwira Redi. "A Decision Support System for Irrigation Management in Thailand: Case Study of Tak City Agricultural Production." Applied Sciences 12, no. 20 (2022): 10508. http://dx.doi.org/10.3390/app122010508.
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Water is essential for agriculture in many world regions and for achieving sustainability in production systems. Maximizing net returns with the available resources is significant, but doing so is a complex problem, owing to the many factors that affect this process. In this study, a decision support system (DSS) incorporating a crop planning model is developed for identifying optimal cropping plans and irrigation management. The model estimates crop yield, production, water requirement, and net income. In this system, the Simulated Annealing algorithm (SA) is used as an optimization tool inside the DSS developed, and the result is as robust as the exact solution with higher computational efficiency. From the model applied, it is found that the current crop pattern and water distribution plan used in the study area should be improved. The computational analysis also found that of the five plans proposed, three plans could produce the highest generated income. On contrary, the current strategy used by Tak’s province farmer has the lowest generated income. This result shows that if a better-designed and more efficient crop planning method was, should be used instead.
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Urrestarazu, L. Pérez, J. A. Rodríguez Díaz, E. Camacho Poyato, R. López Luque, and F. M. Borrego Jaraba. "Development of an integrated computational tool to improve performance of irrigation districts." Journal of Hydroinformatics 14, no. 3 (2012): 716–30. http://dx.doi.org/10.2166/hydro.2012.074.
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Nowadays irrigation district managers require several tools to assess irrigation networks' performance such as hydraulic models, geographic information systems (GIS) or decision support systems (DSS) which are available but as independent elements. Thus, simplifying the use of these tools by means of applications that integrate all these components would be helpful for irrigation district managers. In this paper, a computer tool combining a GIS, a hydraulic model and performance indicators (PIs) has been developed creating a database to deal with most information required in an irritation district. MapObjects Java Edition was used for the GIS integration and EPANET calculation module for the hydraulic modeling. This tool enables the study of the network performance, taking into account real measures (data from the remote control system) and simulated measures (obtained when running the hydraulic model) which are stored in a database and used to calculate different indicators that can be represented in the GIS. The PIs calculated with this tool give important information regarding the network response to different conditions, malfunction problems and failures in supply. Therefore, this tool is also useful to study the effects of improvements and the quality of service provided to farmers.
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Asmelita, Lily M. Limantara, M. Bisri, Widandi Soetopo, and Indra Farni. "Rice Self-Sufficiency and Optimization of Irrigation by Using System Dynamic." Civil Engineering Journal 10, no. 2 (2024): 489–501. http://dx.doi.org/10.28991/cej-2024-010-02-010.
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This research intends to optimize the results of irrigation canals with the conversion of function to fisheries without reducing rice self-sufficiency regionally. However, irrigation is an infrastructure asset that needs to be used optimally. It is due to the water; water sources and irrigation infrastructure can provide more benefits to rice fields, which are to function as fisheries in the study location (West Sumatra Province). The aim of this research is to propose the optimal combinations of irrigated land planted with rice and those in the form of fisheries. The methodology uses System Dynamics due to the official BPS data. There are many tools that are used in this system dynamics approach, such as causal diagrams, archetype systems, diagrams of stock and flow, and the behavior of over-time graphs. The DSS generator for simulating the program in this study uses Stella, which is a new paradigm in the water resources system approach. The result shows that the potential increase in income that could be obtained by converting the rice fields to tilapia fisheries is about 126 million Rupiah per year per hectare. West Sumatra Province, as a national rice granary, has many districts that are more self-sufficient in rice, so it can be considered to utilize irrigation to become the irrigation for fisheries. The potential of rice fields that can be converted into fisheries while maintaining self-sufficiency in rice at the district/city level of West Sumatra Province is more than 61 thousand hectares, and it generates an increase in income of about 7.7 trillion per year. Doi: 10.28991/CEJ-2024-010-02-010 Full Text: PDF
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Luppi, Marta, Pierre-Olivier Malaterre, Adriano Battilani, Vittorio Di Federico, and Attilio Toscano. "A Multi-disciplinary Modelling Approach for Discharge Reconstruction in Irrigation Canals: The Canale Emiliano Romagnolo (Northern Italy) Case Study." Water 10, no. 8 (2018): 1017. http://dx.doi.org/10.3390/w10081017.
Full textAbstract:
Agriculture is the biggest consumer of water in the world, and therefore, in order to mitigate the effects of climate change, and consequently water scarcity, it is important to reduce irrigation water losses and to improve the poor collection of hydraulic status data. Therefore, efficiency has to be increased, and the regulation and control flow should be implemented. Hydraulic modelling represents a strategic tool for the reconstruction of the missing hydraulic data. This paper proposes a methodology for the unmeasured offtake and flowing discharge estimation along the open-canal Canale Emiliano Romagnolo (CER), which is one of the major irrigation infrastructures in Northern Italy. The “multi-disciplinary approach” that was adopted refers to agronomic and hydraulic aspects. The tools that were used are the IRRINET management Decisional Support System (DSS) and the SIC2 (Simulation and Integration of Control for Canals) hydraulic software. Firstly, the methodology was developed and tested on a Pilot Segment (PS), characterized by a simple geometry and a quite significant historical hydraulic data availability. Then, it was applied on an Extended Segment (ES) of a more complex geometry and hydraulic functioning. Moreover, the available hydraulic data are scarce. The combination of these aspects represents a crucial issue in the irrigation networks in general.
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Čereković, Nataša, Mirela Kajkut Zeljković, and Vanja Daničić. "Understanding the response of fruit crops to drought stress and irrigation needs under climate change conditi." AgroReS 14 (May 23, 2025): 55–66. https://doi.org/10.63356/agrores.2025.007.
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Climate change has significantly altered weather patterns, increasing the frequency and intensity of drought events and posing serious challenges to agricultural production, particularly fruits. Water scarcity and increased evapotranspiration demands, posing critical challenges to global agriculture and threatening the sustainability of fruit production. Understanding the response of fruit crops to drought stress and their specific irrigation needs is essential for developing resilient and sustainable cultivation systems. This work aims to consolidate existing research and provide a comprehensive analysis of strategies to mitigate the impacts of water scarcity on fruit crops. The paper focuses on the following key areas: (1) evaluating the growth and performance of fruit crops across diverse environments and cultivation methods; (2) assessing the water needs of fruit crops, including evapotranspiration rates, crop coefficients, and strategies for efficient water use; (3) identifying and recommending the most effective irrigation methods; (4) exploring advanced tools for real-time monitoring of plant water status; and (5) comparing and evaluating existing models for quantifying plant water requirements under drought conditions, with an emphasis on their potential integration into decision support systems (DSS). By addressing these critical aspects, it aims to provide actionable insights and foster the adoption of innovative irrigation and water management strategies to support sustainable fruit crop production in the context of climate change.
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Eastwood, C. R., B. T. Dela Rue, and D. I. Gray. "Using a ‘network of practice’ approach to match grazing decision-support system design with farmer practice." Animal Production Science 57, no. 7 (2017): 1536. http://dx.doi.org/10.1071/an16465.
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The use of pasture measurement tools and decision-support systems (DSS) for grazing management remains limited on New Zealand dairy farms. However, effective use of such tools provides opportunities to optimise pasture grown and pasture harvested. The present study used a mixed-method qualitative research approach to investigate pasture data and technology use for grazing decision making, through interviews and workshops with farmers, rural professionals, commercial software developers and a panel of farming-system specialists. Results suggest that different drivers for use of pasture data and DSS exist between farm owner-operators and corporate farming operations. Larger multi-farm businesses are collecting pasture data for use at a governance level as well as for operational decision making. Understanding the seasonal influences on decision making, and incorporating major regional differences such as pasture growth rates and impact of irrigation use, provides guidance on how to better match DSS to farmer practice. Study participants identified a need for greater integration of software tools to connect in-paddock data capture with real-time feedback. Also, data integration is needed to enable the transfer of information across different platforms for corporate farming operations. Rural professionals used commercial grazing DSS products, but also constructed their own spreadsheets to enable functionality and reporting not available in the DSS products. The research highlighted a need for farmer-orientated tools that are flexible to incorporate differences in user goals, decision making, mobility and desired outputs. Key attributes identified were seasonality, simplicity, ability to trial before purchase, flexibility in application, scalability to match farm systems, and integration with other tools. Future research and design of DSS tools requires a focus on co-creation with farmers, to merge scientific and practical knowledge.
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Martínez-Valderrama, Jaime, Javier Ibáñez, and Francisco J. Alcalá. "AQUACOAST: A Simulation Tool to Explore Coastal Groundwater and Irrigation Farming Interactions." Scientific Programming 2020 (May 14, 2020): 1–20. http://dx.doi.org/10.1155/2020/9092829.
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In the framework of coastal groundwater-dependent irrigation agriculture, modelling becomes indispensable to know how this renewable resource responds to complex (usually not conceptualized nor monitored) biophysical, social, and economic interactions. Friendly user interfaces are essential to involve nonmodeling experts in exploiting and improving models. Decision support systems (DSS) are software systems that integrate models, databases, or other decision aids and package them in a way that decision makers can use. This paper addresses these two issues: firstly with the implementation of a System Dynamics (SD) model in Vensim software that considers the integration of hydrological, agronomic, and economic drivers and secondly with the design of a Venapp, push-button interfaces that allow users access to a Vensim model without going through the Vensim modelling environment. The prototype designed, the AQUACOAST tool, gives an idea of the possibilities of this type of models to identify and analyze the impact of apparently unrelated factors such as the prices of cultivated products, subsidies or exploitation costs on the advance of saltwater intrusion, and the great threat to coastal groundwater-dependent irrigation agriculture systems.