Support of irrigation water use and eco-friendly decision process in agricultural production planning

Bournaris, Thomas; Papathanasiou, Jason; Manos, Basil; Kazakis, Nerantzis; Voudouris, K.

doi:10.1007/s12351-015-0178-9

Cited by 23 publications

(24 citation statements)

References 17 publications

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“…The theory that climate influences development and development influences climate has gained currency in recent years. Increased emissions from the energy, industrial, and agriculture sectors, as well as extensive deforestation and rapid changes in land use and land management practises, have all contributed to significant changes in the earth's gaseous composition in recent decades [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

Revathy

Aurchana²,

Arieth

et al. 2022

Journal of Nanomaterials

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The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The method makes use of climate data from Thanjavur, India’s soils. To choose the optimal crop for a particular set of input and climate conditions, the system leverages real-time input of location-specific soil attributes. The model is been tested with various machine learning techniques such as NB, KNN, SVM, and decision tree. Among all these methods, the SVM gives the good results. The accuracy of the model was determined using LSTM, SVM, and Tabu search optimization. TSO with SVM has an 89% accuracy value.

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Section: Introductionmentioning

confidence: 99%

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

Revathy

Aurchana²,

Arieth

et al. 2022

Journal of Nanomaterials

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“…The high variability of g s , even for similar crop water-status conditions, has already been described in field-grown maize plants [38,39] and is probably caused by the large number of factors that influence this physiological variable, such as air temperature, vapor pressure deficit, phenological stage, the degree of sun exposure of leaves or the leaf position within the vertical gradient of the plant [39]. In any case, the high sensitivity of the CWSI index to maize water stress makes it an excellent irrigation-decision tool for environmentally vulnerable or drought-prone areas [40]. The variable ∆T has also been proposed on some occasions as a feasible crop water status indicator [41].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Assessment of Reference Baselines for the Determination of the Crop Water Stress Index in Maize under Mediterranean Conditions

Prior¹,

Apolo-Apolo²,

Castro-Valdecantos³

et al. 2021

Water

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Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress.

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“…Although the SuSAM index was developed to be compatible with a coastal hydrogeological environment, parameters of agricultural activities and nitrate pollution could be included in the method with the aim of reducing nitrate pollution [53]. An expanded DSS-MAR system could include agricultural planning [54] and groundwater vulnerability maps, which are useful tools for groundwater management [55]. The application of MAR strengthens the concept of integrated water resource management and could also help solve the problem of treated wastewater misuse [56], as well as improve urban water quality [57].…”

Section: Mar Suitability Map and Validation Of The Indexmentioning

confidence: 99%

Delineation of Suitable Zones for the Application of Managed Aquifer Recharge (MAR) in Coastal Aquifers Using Quantitative Parameters and the Analytical Hierarchy Process

Kazakis

2018

Water

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Abstract:Coastal aquifer salinization is usually related to groundwater overexploitation and water table decline. Managed Aquifer Recharge (MAR) can be applied as a measure to reverse and prevent this phenomenon. A detailed literature review was performed to identify the various methods and parameters commonly used to determine suitable sites of MAR application. Based on the review results, a new multi-criteria index (SuSAM) that is compatible to coastal aquifers was developed to delineate suitable zones for MAR application. New parameters were introduced into the index, such as distance from the shore and hydraulic resistance of the vadose zone, while factor weights were determined using the Analytical Hierarchy Process (AHP) and single sensitivity analysis. The applicability of the new index was examined in the coastal aquifer of the Anthemountas basin located in northern Greece. The most suitable areas for MAR application cover 28% of the aquifer's surface area, while 16% of the area was characterized as non-suitable for MAR application. The new method constitutes the first step of the managed aquifer recharge concept for the delineation of MAR-suitable zones in coastal aquifers.

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Support of irrigation water use and eco-friendly decision process in agricultural production planning

Cited by 23 publications

References 17 publications

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

Long-Term Assessment of Reference Baselines for the Determination of the Crop Water Stress Index in Maize under Mediterranean Conditions

Delineation of Suitable Zones for the Application of Managed Aquifer Recharge (MAR) in Coastal Aquifers Using Quantitative Parameters and the Analytical Hierarchy Process

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