The optimum use of existing water resources as well as the efforts to achieve new water resources have been considered as two major solutions to the relative resolution of water scarcity. Through utilization of the information and meteorological data, it is possible to identify areas with potentials for water harvesting from air humidity. It also allows for collecting and converting them into fresh water using simple physical laws. Due to lack of atmospheric precipitations or inappropriate distribution of precipitations in Chabahar, located in the south of Sistan and Baluchistan province, Iran, water is a limiting factor for agricultural activities and even for the entire life. In this study, water was harvested from air humidity using a screen collector with dimensions of 1×1 m. The magnitude of water harvesting was monitored daily for a period of 365 days. The results revealed that approximately 20% of the water available in the air could be extracted in this area. Then, monthly meteorological data from Chabahar synoptic station between 1990 and 2011 was used to predict the harvestable water for the upcoming year using an artificial neural network. After determining the effective input variables in predicting the amount of harvestable water, the modeling was performed using Multi-Layer Perceptron Network (MLP) and General Feed Forward Network. The results indicated that the MLP network had a higher ability to predict the amount of harvestable water when compared to the GFF network (at the R2 test stage it was 0.86 versus 0.44). The most suitable structure to predict harvestable water from the fog in Chabahar was the MLP Artificial Neural Network with the array of 12-1-25 and the Hyperbolic Tangent Stimulus Function with the Lewenburg Marquette Training Law. Also, the values of the RMSE and MAE error rates were 2.19 and 1.81, respectively. Therefore, it is possible to predict the amount of harvestable water in the next 12 months which can be used in water resources management and productivity.
{"title":"Projection of harvestable water from air humidity using artificial neural network (Case study: Chabahar Port)","authors":"Chakavak Khajeh Amiri Khaledi","doi":"10.13128/IJAM-286","DOIUrl":"https://doi.org/10.13128/IJAM-286","url":null,"abstract":"The optimum use of existing water resources as well as the efforts to achieve new water resources have been considered as two major solutions to the relative resolution of water scarcity. Through utilization of the information and meteorological data, it is possible to identify areas with potentials for water harvesting from air humidity. It also allows for collecting and converting them into fresh water using simple physical laws. Due to lack of atmospheric precipitations or inappropriate distribution of precipitations in Chabahar, located in the south of Sistan and Baluchistan province, Iran, water is a limiting factor for agricultural activities and even for the entire life. In this study, water was harvested from air humidity using a screen collector with dimensions of 1×1 m. The magnitude of water harvesting was monitored daily for a period of 365 days. The results revealed that approximately 20% of the water available in the air could be extracted in this area. Then, monthly meteorological data from Chabahar synoptic station between 1990 and 2011 was used to predict the harvestable water for the upcoming year using an artificial neural network. After determining the effective input variables in predicting the amount of harvestable water, the modeling was performed using Multi-Layer Perceptron Network (MLP) and General Feed Forward Network. The results indicated that the MLP network had a higher ability to predict the amount of harvestable water when compared to the GFF network (at the R2 test stage it was 0.86 versus 0.44). The most suitable structure to predict harvestable water from the fog in Chabahar was the MLP Artificial Neural Network with the array of 12-1-25 and the Hyperbolic Tangent Stimulus Function with the Lewenburg Marquette Training Law. Also, the values of the RMSE and MAE error rates were 2.19 and 1.81, respectively. Therefore, it is possible to predict the amount of harvestable water in the next 12 months which can be used in water resources management and productivity.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"28 1","pages":"3-11"},"PeriodicalIF":1.2,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85930470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The effect of three irrigation levels (100%, 75% and 50% of crop water requirement), five weed control treatments (pyroxsulam, mesosulfuron-methyl, isoproturon+diflufenican, hand weeding and unweeded check control treatment), five cobalt concentrations (0, 5, 10, 15 and 20 ppm) and their interaction on wheat productivity, weed growth and water use efficiency, were examined in two field experiments in sandy soil at the Agricultural Experimental Station of the National Research Centre, Egypt. The results indicated that pyroxsulam recorded the greatest weed control efficiency. Application of 100% of crop water requirement showed the largest values of flag-leaf area, chlorophyll content, plant height, spikes number/m2, grains number/spike, 1,000 grain weight, straw and grain yield of wheat plants, compared with all other irrigation treatments. Isoproturon+diflufenican followed by pyroxsulam and mesosulfuron-methyl treatments gave the largest grain yield. Application of cobalt resulted in recovery from the negative effects of insufficient water on wheat yield in low fertility soils and using cobalt at a rate of 15 ppm resulted in increased wheat grain yield. The maximum grain yield with largest protein and carbohydrates percentages in grains was obtained by application of 100% of crop water requirement with pyroxsulam and using 15 ppm cobalt, followed by 75% of crop water requirement combined with isoproturon+diflufenican treatment, with insignificant difference between both two interaction treatments.
{"title":"Wheat productivity and water use efficiency responses to irrigation, cobalt and weed management","authors":"I. El-Metwally, N. Gad","doi":"10.13128/IJAM-287","DOIUrl":"https://doi.org/10.13128/IJAM-287","url":null,"abstract":"The effect of three irrigation levels (100%, 75% and 50% of crop water requirement), five weed control treatments (pyroxsulam, mesosulfuron-methyl, isoproturon+diflufenican, hand weeding and unweeded check control treatment), five cobalt concentrations (0, 5, 10, 15 and 20 ppm) and their interaction on wheat productivity, weed growth and water use efficiency, were examined in two field experiments in sandy soil at the Agricultural Experimental Station of the National Research Centre, Egypt. The results indicated that pyroxsulam recorded the greatest weed control efficiency. Application of 100% of crop water requirement showed the largest values of flag-leaf area, chlorophyll content, plant height, spikes number/m2, grains number/spike, 1,000 grain weight, straw and grain yield of wheat plants, compared with all other irrigation treatments. Isoproturon+diflufenican followed by pyroxsulam and mesosulfuron-methyl treatments gave the largest grain yield. Application of cobalt resulted in recovery from the negative effects of insufficient water on wheat yield in low fertility soils and using cobalt at a rate of 15 ppm resulted in increased wheat grain yield. The maximum grain yield with largest protein and carbohydrates percentages in grains was obtained by application of 100% of crop water requirement with pyroxsulam and using 15 ppm cobalt, followed by 75% of crop water requirement combined with isoproturon+diflufenican treatment, with insignificant difference between both two interaction treatments.","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"13 1","pages":"13-24"},"PeriodicalIF":1.2,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89878813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.19199/2018.2.2038-5625.005
V. Kandić, D. Dodig, M. Zorić, A. Nikolić, G. Šurlan-Momirović, Z. Kaitovic, G. Aleksić, Nenad Durić
{"title":"Principal morphological and agronomic characteristics of some durum wheat varieties in central Italy influenced by meteorological anomalies","authors":"V. Kandić, D. Dodig, M. Zorić, A. Nikolić, G. Šurlan-Momirović, Z. Kaitovic, G. Aleksić, Nenad Durić","doi":"10.19199/2018.2.2038-5625.005","DOIUrl":"https://doi.org/10.19199/2018.2.2038-5625.005","url":null,"abstract":"","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"32 1","pages":"31-38"},"PeriodicalIF":1.2,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83391096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.19199/2017.2.2038-5625.025
A. Bianchi, D. Masseroni, M. Thalheimer, L. O. Medici, A. Facchi
{"title":"Field irrigation management through soil water potential measurements : a review","authors":"A. Bianchi, D. Masseroni, M. Thalheimer, L. O. Medici, A. Facchi","doi":"10.19199/2017.2.2038-5625.025","DOIUrl":"https://doi.org/10.19199/2017.2.2038-5625.025","url":null,"abstract":"","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"11 1","pages":"25-38"},"PeriodicalIF":1.2,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79511940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01DOI: 10.19199/2017.2.2038-5625.013
Stefania Bolla, M. Branca, C. Cassardo, S. Ferrarese, R. Notarpietro
{"title":"Testing of WRF parameterizations with X-band radar data in a convective rainfall event.","authors":"Stefania Bolla, M. Branca, C. Cassardo, S. Ferrarese, R. Notarpietro","doi":"10.19199/2017.2.2038-5625.013","DOIUrl":"https://doi.org/10.19199/2017.2.2038-5625.013","url":null,"abstract":"","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"112 1","pages":"13-24"},"PeriodicalIF":1.2,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80663371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.19199/2016.1.2038-5625.047
H. Ghazouani, D. Autovino, G. Rallo, B. Douh, G. Provenzano
semi-arid climate and the frequent periods of drought, the country needs to face, more and more often, with severe water shortage and the application of precision irrigation represents a desirable management strategy (Cammalleri et al., 2013). Despite irrigated agriculture covers only 7% of the cropped area, it is responsible for about 33% of agricultural Tunisian products (Zairi et al., 2003). Projections for the future indicate the need to reinforce the role of irrigation in food security to up 50% of the total agricultural production of the country (FAO, 2005). Using HYDRUS-2D model to assess the optimal drip lateral depth for Eggplant crop in a sandy loam soil of central Tunisia
{"title":"Using HYDRUS-2D model to assess the optimal drip lateral depth for eggplant crop in a sandy loam soil of central Tunisia","authors":"H. Ghazouani, D. Autovino, G. Rallo, B. Douh, G. Provenzano","doi":"10.19199/2016.1.2038-5625.047","DOIUrl":"https://doi.org/10.19199/2016.1.2038-5625.047","url":null,"abstract":"semi-arid climate and the frequent periods of drought, the country needs to face, more and more often, with severe water shortage and the application of precision irrigation represents a desirable management strategy (Cammalleri et al., 2013). Despite irrigated agriculture covers only 7% of the cropped area, it is responsible for about 33% of agricultural Tunisian products (Zairi et al., 2003). Projections for the future indicate the need to reinforce the role of irrigation in food security to up 50% of the total agricultural production of the country (FAO, 2005). Using HYDRUS-2D model to assess the optimal drip lateral depth for Eggplant crop in a sandy loam soil of central Tunisia","PeriodicalId":54371,"journal":{"name":"Italian Journal of Agrometeorology-Rivista Italiana Di Agrometeorologia","volume":"21 1","pages":"47-58"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81145032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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