Species-specific irrigation scheduling with a spatially explicit biophysical model: A comparison to substrate moisture sensing with insight into simplified physiological parameterization

Barnard, David M.; Bauerle, William L.

doi:10.1016/j.agrformet.2015.08.244

Cited by 4 publications

(3 citation statements)

References 59 publications

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“…This is usually in form of an off/off control strategy in which irrigation is applied after the accumulation of a set point cumulative transpiration amount (Davis and Dukes, 2010). These computer-controlled irrigation systems make use of mechanistic or empirical models to estimate crop transpiration based on environmental and physiological factors (Barnard and Bauerle, 2015).…”

Section: Introductionmentioning

confidence: 99%

Dynamic modelling of lettuce transpiration for water status monitoring

Adeyemi

Peets

Domun

et al. 2018

Computers and Electronics in Agriculture

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Real-time information on the plant water status is an important prerequisite for the precision irrigation management of crops. The plant transpiration has been shown to provide a good indication of its water status. In this paper, a novel plant water status monitoring framework based on the transpiration dynamics of greenhouse grown lettuce plants is presented. Experimental results indicated that lettuce plants experiencing adequate water supply transpired at a higher rate compared to plants experiencing a shortage in water supply. A data-driven model for predicting the transpiration dynamics of the plants was developed using a system identification approach. Results indicated that a second order discrete-time transfer function model with incoming radiation, vapour pressure deficit, and leaf area index as inputs sufficiently explained the dynamics with an average coefficient of determination of 2 = 0.93 ± 0.04. The parameters of the model were updated online and then applied in predicting the transpiration dynamics of the plants in real-time. The model predicted dynamics closely matched the measured values when the plants were in a predefined water status state. The reverse was the case when there was a significant change in the water status state. The information contained in the model residuals (measured transpirationmodel predicted transpiration) was then exploited as a means of inferring the plant water status. This framework provides a simple and intuitive means of monitoring the plant water status in real-time while achieving a sensitivity similar to that of stomatal conductance measurements. It can be applied in regulating the water deficit of greenhouse grown crops, with specific advantages over other available techniques.

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Section: Introductionmentioning

confidence: 99%

Dynamic modelling of lettuce transpiration for water status monitoring

Adeyemi

Peets

Domun

et al. 2018

Computers and Electronics in Agriculture

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“…They are able to incorporate the physiological and morphological representation of the plant into the decision support tool. Barnard and Bauerle [116] described an irrigation scheduling system based on the spatially explicit biophysical model, MAESTRA (Multi-Array Evaporation Stand Tree Radiation A), which couples the within-canopy photosynthesis and stomatal conductance. Data on leaf temperature, canopy aerodynamics and environmental variables are used as inputs into the model to predict the plant transpiration.…”

Section: Mechanistic Modelsmentioning

confidence: 99%

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

et al. 2017

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Globally, the irrigation of crops is the largest consumptive user of fresh water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This paper discusses how incorporating adaptive decision support systems into precision irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil-plant-atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

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“…In [13], the automated irrigation scheduling techniques was developed and implemented using the real-time prediction from MAESTRA (Multi-Array Evaporation Stand Tree Radiation A) model. The irrigation efficiency, tree growth and the amount of water supplied were compared with the model-based irrigation to the sensing-based irrigation.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based optimal irrigation scheduling of agro-hydrological systems

Sahoo¹,

Agyeman²,

Debnath³

et al. 2021

Preprint

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The typical agricultural irrigation scheduler provides information on how much to irrigate and when to irrigate. The accurate and effective scheduler decision for a large agricultural field is still an open research problem. In this work, we address the high dimensionality of the agricultural field and propose a systematic approach to provide optimum irrigation amount and irrigation time for three-dimensional agro-hydrological systems. The water dynamics of the agro-hydrological system are represented using a cylindrical three-dimensional Richards Equation. We introduce a structure-preserving model reduction technique to decrease the dimension of the system model. Using the reduced model, the optimization-based closed-loop scheduler is designed in model predictive control (MPC) environment. The closed-loop approach can handle weather disturbances and provide improved yield and water conservation. The primary objective of the proposed scheduler is to ensure maximum yield, minimum water consumption and maximize the time between the two irrigation events, which results in less electricity usage. The proposed approach is applied to three different scenarios to show the effectiveness and superiority of the proposed framework.

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Species-specific irrigation scheduling with a spatially explicit biophysical model: A comparison to substrate moisture sensing with insight into simplified physiological parameterization

Cited by 4 publications

References 59 publications

Dynamic modelling of lettuce transpiration for water status monitoring

Dynamic modelling of lettuce transpiration for water status monitoring

Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation

Knowledge-based optimal irrigation scheduling of agro-hydrological systems

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