https://riviste.fupress.net/index.php/IJAm/issue/feed Italian Journal of Agrometereology 2019-06-11T18:44:59+00:00 Simone simone.orlandini@unifi.it Open Journal Systems <p>The <em>Italian Journal of Agrometeorology</em> (Rivista Italiana di Agrometeorologia) publishes English or Italian-written original papers about agrometeorology, that is the science which studies the interactions between meteorological, hydrological factors and the agro-forest ecosystem and with agriculture, including all the related themes: herbaceous and arboreal species ecophysiology, crop phenology, phytopathology, entomology, soil physics and hydrology, micrometeorology, crop modelling, remote-sensing, landscape planning, geo-graphical in-formation system and spatialization techniques, instru-mentation for physical and biological measurements, data validation techni-ques, agroclimatology, diffusion of information and support services for farmers.</p> https://riviste.fupress.net/index.php/IJAm/article/view/286 Projection of harvestable water from air humidity using artificial neural network (Case study: Chabahar Port) 2019-06-06T14:48:25+00:00 Chakavak Khajeh Amiri Khaledi C.Khajehamiri@cmu.ac.ir <p class="p1"><span class="s1">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 R<sup>2</sup> 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.</span></p> 2019-06-03T00:00:00+00:00 ##submission.copyrightStatement## https://riviste.fupress.net/index.php/IJAm/article/view/287 Wheat productivity and water use efficiency responses to irrigation, cobalt and weed management 2019-06-06T14:48:58+00:00 Ibrahim Mohamed El-Metwally im_elmetwally@yahoo.com Nadia Gad null@null.com <p class="p1">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/m<sup>2</sup>, 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.</p> 2019-06-03T17:14:19+00:00 ##submission.copyrightStatement## https://riviste.fupress.net/index.php/IJAm/article/view/288 How can policy influence innovation: An exploration of climate-smart activities in Emilia-Romagna 2019-06-11T18:44:59+00:00 Camilla Chieco c.chieco@ibimet.cnr.it Federica Rossi null@null.com Slaven Tadić null@null.com <p>Climate change is one of the main issues in agriculture. Considering its involvement in the global anthropogenic emissions (GHG) it is no wonder that research is devising ways on how to reduce such effects. A solution to such problems is climate-smart agriculture (CSA). In this paper, we analysed which are the main opportunities granted by agricultural policies when aimed at sustaining innovative agricultural models. A review of the ongoing 93 Rural Development Projects (RDPs) uncovered potential climate-smart solutions for the identified potential threats. The Ministry of Agriculture, Hunting and Fishing of the Region of Emilia-Romagna in Italy has given importance to RDPs to innovate the agricultural sector through policy measures. We analysed an Operational Group (OG) project as an overview of the work. In the case of Emilia-Romagna, the amount of innovation and solutions that can be achieved if policies invest in CSA is very clear. Emilia-Romagna is on the forefront of technological and practical advancements in the EU by implementing CSA as one of the primary solutions to the aforementioned problems and will continuously work on transitioning its agricultural practices to fight climate change.</p> 2019-06-03T17:15:25+00:00 ##submission.copyrightStatement## https://riviste.fupress.net/index.php/IJAm/article/view/289 Effect of drought and nitrogen fertilisation on quinoa (Chenopodium quinoa Willd.) under field conditions in Burkina Faso 2019-06-06T14:50:40+00:00 Jorge Alvar-Beltrán jorge.alvar@unifi.it Coulibaly Saturnin null@null.com Abdalla Dao null@null.com Anna Dalla Marta null@null.com Jacob Sanou null@null.com Simone Orlandini null@null.com <p class="p1"><em>Chenopodium quinoa</em> (Willd.) is an herbaceous C3 crop originating in the Andean Altiplano. Quinoa possesses a great deal of genetic variability, can adapt to diverse climatic conditions, besides of having seeds with high nutritional properties. An experiment conducted in Burkina Faso has determined the response of two quinoa varieties (Titicaca and Negra Collana) to different planting dates (November vs December), irrigation levels (Potential evapotranspiration-PET, 100, 80 and 60% PET), and N fertilization rates (100, 50 and 25 kg N ha<sup>-1</sup>). Main research findings have shown that quinoa can be highly performant under drought stress conditions and low nitrogen inputs, besides of coping with high temperatures typically of the Sahel. The highest yields (1.9 t ha<sup>-1</sup>) were achieved when sown in November at 60 % PET and 25 kg N ha<sup>-1</sup>. For this location, short cycle varieties, such as Titicaca, were recommended in order to avoid thermic stress conditions occurring prior to the onset of the rainy season (May-October).</p> 2019-06-03T00:00:00+00:00 ##submission.copyrightStatement##