Contribution of Irrigation Ponds to the Sustainability of Agriculture. A Review of Worldwide Research

López-Felices, Belén; Aznar-Sánchez, José A.; Velasco-Muñoz, Juan F.; Piquer‐Rodriguez, María

doi:10.3390/su12135425

Cited by 27 publications

(26 citation statements)

References 90 publications

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“…Furthermore, all input combinations employed in this research showed good performance. R 2 , COE, MSE and NRMSE for the training period were found 0.99, 1.00, 21245.92 and 820420.17m 3…”

Section: Rainfall Forecastingmentioning

confidence: 87%

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Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

Waqas¹,

Saifullah²,

Hashim³

et al. 2021

Weather Forecasting

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The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981–2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning.

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“…Furthermore, all input combinations employed in this research showed good performance. R 2 , COE, MSE and NRMSE for the training period were found 0.99, 1.00, 21245.92 and 820420.17m 3…”

Section: Rainfall Forecastingmentioning

confidence: 87%

“…Water resources are crucial for agriculture, and most of the population livelihood depends on agriculture. Water storage is necessary, and the urban population's rapid growth [3,4]. The efficient and precise modeling of the rainfall-runoff process is crucial in planning water resources management [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

Waqas¹,

Saifullah²,

Hashim³

et al. 2021

Weather Forecasting

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“…The plant-soil relationship has been studied by different authors [44], revealing the relationship between certain species and some edaphic parameters: although soil fertility is often established using edaphic analysis techniques [45,46], there are no studies on the influence of nutrients on the abundance of species. For these reasons, vegetation covers, breakages, soil erosion, and water resources are studied to evaluate the environmental impact, such as soil loss and increased evapotranspiration [47][48][49]. In all cases, the authors made a diagnosis without deep knowledge about the plant-soil relationship, which moreover did not result, as in other work where for example in a contaminated soil with heavy metal, a close relationship was observed [43].…”

Section: Discussionmentioning

confidence: 99%

Indicative Value of the Dominant Plant Species for a Rapid Evaluation of the Nutritional Value of Soils

et al. 2020

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A study was conducted on 14 grassland communities located in the south of the Iberian Peninsula and their edaphology, which is identified as specific plant associations. The edaphic study of each association allows a rapid evaluation of the nutrient content in the soil without the need for laboratory edaphic analysis. For each phytosociological relevé and soil, samplings were carried out. The field data were subjected to various statistical analysis—canonical correspondence analysis (CCA), Bayesian networks, and decision trees—to establish nutrient content. When the abundance value of the species is 9 in the Van der Maarel scale, there is an increase in the values of several soil parameters. In the case of Hordeum leporinum, when the Van der Maarel index is 9, the Kc (exchangeable potassium in cmol/kg) undergoes the greatest variation, to a value of up to 0.729 cmol/kg. The application of the decision tree to this species reveals that the soil attributes with the greatest influence in the classification are conductivity, %_si (silt texture), pH, and pF 15 atm (pressure at 15 atmospheres (water retention capacity) in %). Indeed, this interlaced edaphic and phytosociological study provides us with a high-value tool to obtain quick information on the content of nutrients in the soil.

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“…Today, a wide range of software tools are available for processing, analysing, aggregating and visualising bibliographic information [20,21]. All of this has enabled bibliometric analysis to become a methodology frequently used in disciplines as diverse as economics, agronomics, biology, engineering, medicine or psychology [22,23].…”

Section: Methodsmentioning

confidence: 99%

Three Decades of Behavioural Economics in Agriculture. An Overview of Global Research

et al. 2021

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Over the last three decades, behavioural economics has been gaining ground in the research on a wide range of agriculture-related themes. This is due to the diversity of the agents involved in the production systems and the agro-food value chains in which opposing interests must be reconciled. The main objective of this study is to examine the dynamics of the research on the application of behavioural economics in agriculture across the world. To do this, a bibliometric analysis has been carried out through a literature review of the period between 1991 and 2020. The results of the study show that the use of behavioural economics has increased in the research on agriculture, particularly over the last five years. The application of behavioural economics in agriculture has focused on analysing consumers, producers, management, marketing, development, environmental issues, climate change, food and health.

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Contribution of Irrigation Ponds to the Sustainability of Agriculture. A Review of Worldwide Research

Cited by 27 publications

References 90 publications

Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

Indicative Value of the Dominant Plant Species for a Rapid Evaluation of the Nutritional Value of Soils

Three Decades of Behavioural Economics in Agriculture. An Overview of Global Research

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