Optimization of crop irrigation strategy under a stochastic weather regime: A simulation study

Ahmed, Jamil; Bavel, C. H. M. van; Hiler, E. A.

doi:10.1029/wr012i006p01241

Cited by 22 publications

(4 citation statements)

References 14 publications

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“…It was related to time t following seeding through a supplies conditions, and the effect of reduced water on transpiration was incorporated through reduced soil water status. But apart from affecting temporal variation in soil water status, timing and amount of water additions also effect the pattern of leaf area development[Ahmed et al, 1976;Dugas et al, 1983] and hence the transpiration load T• of the plant. Thus K t should be assessed from leaf area development for specific wetting histories for par-titioning ET a into T• (that equals T m under plentiful water supplies): T•(or Tm) = E T mK ,…”

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confidence: 99%

Synthesis of a simplified water use simulation model for predicting wheat yields

Arora¹,

Prihar²,

Gajri³

1987

Water Resources Research

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A simplified water use simulation model has been developed that predicts components of field water balance for wheat using information on atmospheric evaporative demand, extractable water and draining characteristics of soil profile, leaf area development, root growth characteristics, and soil wetting events. The model accounts for soil water flow with redistributing water below field capacity and estimates layerwise water use. Predicted evaportranspiration and soil water content in different soil layers matched well with the measured values for a wide range of water management situations and the root-meansquare error varied between 10 and 15%. Transpiration-yield relations, estimated through a multiplicative approach, showed that booting-heading phase of wheat development was most sensitive to water stress. Validation of this model against independent data was reasonably well, thereby suggesting that the model dependably predicts grain yield. Small data required for the operation of the model increase its capability as management tool for efficient on-farm water use.

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confidence: 99%

Synthesis of a simplified water use simulation model for predicting wheat yields

Arora¹,

Prihar²,

Gajri³

1987

Water Resources Research

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“…Therefore, attempts have been taken to consider rainfall uncertainty in both long‐term and short‐term irrigation optimization models. Stochastic dynamic programming (SDP) models [e.g., Dudley and Burt , ] and the detailed soil moisture dynamics simulation models [e.g., Ahmed et al ., ; Ines et al ., ] have been developed to precisely survey SC farm problems. However, these models are not applicable in MC problems due to being computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Coupled stochastic soil moisture simulation‐optimization model of deficit irrigation

Alizadeh

Mousavi

2013

Water Resources Research

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[1] This study presents an explicit stochastic optimization-simulation model of short-term deficit irrigation management for large-scale irrigation districts. The model which is a nonlinear nonconvex program with an economic objective function is built on an agrohydrological simulation component. The simulation component integrates (1) an explicit stochastic model of soil moisture dynamics of the crop-root zone considering interaction of stochastic rainfall and irrigation with shallow water table effects, (2) a conceptual root zone salt balance model, and 3) the FAO crop yield model. Particle Swarm Optimization algorithm, linked to the simulation component, solves the resulting nonconvex program with a significantly better computational performance compared to a Monte Carlobased implicit stochastic optimization model. The model has been tested first by applying it in single-crop irrigation problems through which the effects of the severity of water deficit on the objective function (net benefit), root-zone water balance, and irrigation water needs have been assessed. Then, the model has been applied in Dasht-e-Abbas and Ein-khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. While the maximum net benefit has been obtained for a stress-avoidance (SA) irrigation policy, the highest water profitability has been resulted when only about 60% of the water used in the SA policy is applied. The DAID with respectively 33% of total cultivated area and 37% of total applied water has produced only 14% of the total net benefit due to lowvalued crops and adverse soil and shallow water table conditions.

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“…Water, considered a limited resource, is still an elementary requirement for life on Earth; the upsurge in population growth and climatic changes over several decades has affected water utilization, and water is now in high demand [1,2]. The resources available for agriculture are being depleted at an alarming rate; hence, traditional methods have to be replaced with new methods for the effective utilization of available resources.…”

Section: Introductionmentioning

confidence: 99%

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

Veerachamy

Ramalakshmi

Kavin

et al. 2022

Water

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The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications.

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Optimization of crop irrigation strategy under a stochastic weather regime: A simulation study

Cited by 22 publications

References 14 publications

Synthesis of a simplified water use simulation model for predicting wheat yields

Synthesis of a simplified water use simulation model for predicting wheat yields

Coupled stochastic soil moisture simulation‐optimization model of deficit irrigation

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

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