Simulation of irrigation reuse system nitrate losses and potential corn yield reductions

Adelman, Donald D.

doi:10.1016/s1462-9011(00)00082-4

Cited by 4 publications

(3 citation statements)

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“…Drainage water and dissolved nutrients have been globally utilized for crops and greenhouses. In some intensive farming areas, farmers have begun to test their groundwater for nitrate concentrations and therefore change their nutrient budgets accordingly [10]. In another case, reapplication of N-rich runoff waters provided more than the annual nutrient requirements for that land [11].…”

Section: Introductionmentioning

confidence: 99%

Water Sustainability through Drainage Reuse in Agriculture – A Case for Collaborative Wireless Sensor Networks

Zia¹

2023

Irrigation and Drainage - Recent Advances

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With increasing prevalence of wireless sensor networks (WSNs) and internet of things (IoT) in agriculture and hydrology, there exists an opportunity for providing a technologically viable solution for the conservation of already scarce freshwater resources. In this chapter, a novel framework is proposed for enabling a proactive management of agricultural drainage and nutrient losses at farm scale where complex models are replaced by in situ sensing, communication, and low complexity predictive models suited to an autonomous operation. This is achieved through the development of the proposed Water Quality Management using Collaborative Monitoring (WQMCM) framework that combines local farm-scale WSNs through an information sharing mechanism. In this chapter, we present the design of a framework for facilitating real-time utilization or disposal of agricultural drainage among farms using collaboration among prevalent farm networks. The basic system architecture comprises modules for environmental learning, prediction of the impact of neighboring events in terms of drainage and nutrients losses, and a local decision support mechanism. The overall functionality of the framework is explored in terms of stages of learning, training, and testing. A network learning model is required to identify flow links of a network with neighboring networks.

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Section: Introductionmentioning

confidence: 99%

Water Sustainability through Drainage Reuse in Agriculture – A Case for Collaborative Wireless Sensor Networks

Zia¹

2023

Irrigation and Drainage - Recent Advances

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“…Despite tremendous technological advancements and the great promise of the benefits of drainage water reuse, implementation of intelligent and autonomous management mechanisms has not kept pace with the deteriorating water situation [20]. While drainage reuse has been advocated and adopted in farming [21]- [23], various resource constraints and farmer's concerns regarding real time availability of information on volumes, timings, and quality of discharges that will be delivered to the farms [24], [25], currently restricts wide adoption of this mechanism in agriculture.…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Machine Learning Nitrate Loss Predictive Model–Towards Proactive Farm Management in a Networked Catchment

et al. 2019

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With the advent of wireless sensor networks, the ability to predict nutrient-rich discharges, using on-node prediction models, offers huge potential for enabling real-time water reuse and management within an agriculturally dominated catchment. Existing discharge models use multiple parameters and large historical data which are difficult to extract and this, coupled with constraints on network nodes (battery life, computing power, and sensor availability), makes it necessary to develop low-dimensional models. This paper investigates a data-driven model for predicting daily total oxidized nitrate fluxes and reduces the number of model parameters used to 5-a reduction of at least 50%. Trained on only a 12-month training dataset derived from the published measured data, results for the model generated using an M5 decision tree, giving an R 2 of 0.92 and a relative root-mean-square error of 26%. The 80% of the residuals for test data falls within +/−0.05 Kgůha −1 ůday −1 error range, which is minimal, offering an improvement over results obtained by the contemporary research.

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“…This will also lead to improved water quality as it will allow nutrients to be kept in the place where they can be useful where previously they would have been discharged with no control into the local environment, eventually ending up in the streams and rivers. While drainage reuse has been advocated and adopted in farming (Adelman, 2000, Willardson et al, 1997, Harper, 2012, various resource constraints and farmer's concerns regarding real time availability of information on volumes, timings, and quality of discharges that will be delivered to the farms (Carr et al, 2011, Oster andGrattan, 2002), currently restricts wide adoption of this mechanism in agriculture.…”

Section: Introductionmentioning

confidence: 99%

Predicting discharge using a low complexity machine learning model

Zia¹,

Harris²,

Merrett³

et al. 2015

Computers and Electronics in Agriculture

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This paper reports on the validation of a simplified discharge prediction model that is suitable for implementation on a resourced constrained system such as a wireless sensor network, which will allow their operation to become more proactive rather than reactive. The data-driven model, utilising an M5 decision tree modelling technique, is validated using a 12-month training data set derived from published measured data. Daily runoff and drainage is predicted, and the results are compared with existing data-driven models developed in this domain. Results for the model give an R 2 of 0.82 and Root Relative Mean Square Error (RRMSE) of 35.9%. 80% of the residuals for the predicted test values fall within a +2 mm discharge depth/day error range. The main significance is that the proposed model gives comparable results with fewer samples and simpler parameters when compared to previous published research, which offers the potential for implementation in resource constrained monitoring and control systems.

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Simulation of irrigation reuse system nitrate losses and potential corn yield reductions

Cited by 4 publications

References 0 publications

Water Sustainability through Drainage Reuse in Agriculture – A Case for Collaborative Wireless Sensor Networks

Water Sustainability through Drainage Reuse in Agriculture – A Case for Collaborative Wireless Sensor Networks

A Low-Complexity Machine Learning Nitrate Loss Predictive Model–Towards Proactive Farm Management in a Networked Catchment

Predicting discharge using a low complexity machine learning model

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