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description Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2020 Netherlands, FrancePublisher:Elsevier BV Ebrahim Zeinali; M.K. van Ittersum; Afshin Soltani; S M Alimagham; S. Ghassemi; Vincent Vadez; Vincent Vadez; A. Nehbandani; Eskandar Zand; B. Torabi; Thomas R. Sinclair;Assessing the food availability and food security of countries is a critical exercise in which crop simulation models are essential. Application of crop models has been limited often to estimate yield per unit area of one or a few important field crops, whereas what is really required is the total national production of diverse crops including forages, vegetables and fruit trees that compete for limited resources of land and water. In this study a simple crop model (SSM-iCrop2; Simple Simulation Models) was set up for an entire country using a bottom-up approach such that it provides representative estimates of potential yield and other crop properties at provincial level as influenced by climate, soil, management and cultivar. The information is then used to calculate total plant production at province and country levels, as influenced by available land and water resources and by the efficiency of utilizing the resources using the concepts relative yield gap and irrigation efficiency. Iran was used as a case study to develop the modeling framework and illustrative outputs. Development of the framework resulted in accumulation of large bodies of valuable geospatial information and statistics across disciplines that are critical for analysis of plant production at a country level. The framework allows different scenarios of national plant production to be evaluated. This includes assessing the possibility of increasing national plant production via intensification, optimizing water allocation across plant species at province and country levels by changing the cropping pattern, and assessing and prioritizing possible ways of adapting a country's agriculture to limited land and water resources and climate change.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 21 citations 21 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Springer Science and Business Media LLC Authors: Mohsen Abshenas; Behnam Kamkar; Afshin Soltani; Hossein Kazemi;pmid: 36068442
Climate change is one of the most important threats to food security. Earth's temperature is reported to increase by 1.5 to 4 °C by the end of the twenty-first century, compared to the base period (1850-1900), and will continue after 2100. Different models have been used to investigate the effects of climate change on different plant responses, including the exponential downscale statistical model of SDSM. Photosynthesis, respiration, and production are some of the first components to be affected by temperature which are discussed here. This study was aimed to introduce and compare the best interpolation method of main temperatures and precipitation to simulate the rate of photosynthesis, total respiration (total growth and maintenance respiration), and dry matter production of wheat in Golestan Province under climate change. Long-term data of 38 synoptic meteorological stations were used to interpolate the main temperature variables and provide reliable maps. Then, temperature change (ΔT) was used to simulate photosynthesis, total respiration, and dry matter production using the canopy photosynthesis simulation model (Can_Phs). The results clearly showed that by changing the minimum temperature by 1.1 to 3.1 °C and the maximum temperature by 2.3 to 4 °C, the amount of wheat production in the study area will be affected in 2050. This increase in temperature can reduce the length of the growing season in autumn wheat and limit the duration of intercepting light and capturing other resources, which in turn leads to a decrease in photosynthesis and increased respiration during the growing season.
Environmental Monito... arrow_drop_down Environmental Monitoring and AssessmentArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10661-022-10428-7&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
more_vert Environmental Monito... arrow_drop_down Environmental Monitoring and AssessmentArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10661-022-10428-7&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2013Publisher:Elsevier BV Authors: Elias Soltani; Ebrahim Zeinali; Afshin Soltani; M H Rajabi;Abstract The objectives of this study were to analyze energy use and greenhouse gases (GHG) emissions in various wheat production scenarios in north eastern Iran and to identify measures to reduce energy use and GHG emissions. Three high-input, a low-input, a better crop management and a usual production scenarios were included. All activities and production processes were monitored and recorded. Averages of total energy input and output were 15.58 and 94.4 GJ ha−1, respectively. Average across scenarios, GHG emissions of 1137 kg CO2-eq ha−1 and 291 kg CO2-eq t−1 were estimated. The key factors relating to energy use and GHG emissions were seedbed preparation and sowing and applications of nitrogen fertilizer. The better crop management production scenario required 38% lower nitrogen fertilizer (and 33% lower total fertilizer), consumed 11% less input energy and resulted in 33% more grain yield and output energy compared to the usual production scenario. It also resulted in 20% less GHG emissions per unit field area and 40% less GHG emissions per ton of grain. It was concluded that this scenario was the cleaner production scenario in terms of energy use and GHG emissions. Measures of improvement in energy use and GHG emission were identified.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2012.12.022&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu108 citations 108 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2012.12.022&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2020 France, NetherlandsPublisher:Elsevier BV M.K. van Ittersum; S. Rahban; Z. Mohammadzadeh; Z. Mohammadzadeh; A. Nehbandani; S. Pourshirazi; H. Kamari; B. Torabi; Thomas R. Sinclair; M. Zahed; S M Alimagham; R Hosseini; R Hosseini; Afshin Soltani; S. Keramat; A. Dadrasi; O. Alasti; Ebrahim Zeinali; R. Arabameri; H. Fayazi; Vincent Vadez; Vincent Vadez; Eskandar Zand; S. Ghassemi; S. Mohammadi;Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senescence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straight-forward. The resultant model (SSM-iCrop2) was parameterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102855&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 34 citations 34 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102855&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2018Publisher:Elsevier BV Authors: Behnam Kamkar; Hossein Kazemi; Mohsen Shokrgozar; Afshin Soltani;Abstract In this study, the energy pattern of cotton production was analyzed and compared by energy indicators in the Darab (with arid climate) and Gorgan (with sub-humid climate) regions, in Iran. For this purpose, different climatic conditions, agronomic managements, energy inputs and cotton varieties were considered. The data were collected from a survey of 30 cotton fields in each region during 2013–2014. All agricultural managements in the studied fields were monitored and recorded. Then, some energy related indicators, including renewable and non-renewable energies, energy use efficiency, direct and indirect energies, net energy, energy productivity and specific energy were calculated. On the base of obtained results, total energy consumption of cotton production was estimated as 36,189.03 in Darab and 31,860.6 MJ ha−1 in Gorgan. The factors relating to energy consumption were diesel fuel (Darab 39.09% and Gorgan 59.94%), and fertilizers (Darab 16.9% and Gorgan 15.25%). The cotton output energy was being as 34,076.04 MJ ha−1for Darab and 35,231.26 MJ ha−1for Gorgan. Also, energy use efficiency was calculated as 0.942 in Darab (as an arid climate) and 1.106 in Gorgan (as a sub-humid climate). The indirect energy and non-renewable energy were relatively high in Gorgan compared to Darab. It was concluded that energy productivity index implies that lower units of output was obtained per unit energy in Darab region. Also, the high ratio of non-renewable energy in total used energy inputs causes negative effects on the sustainability of cotton production systems.
Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2018 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.jclepro.2018.04.195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu27 citations 27 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2018 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.jclepro.2018.04.195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors: Behnam Kamkar; Saeid Hassanpour Bourkheili; Afshin Soltani; Noor Mohammad Nazari; +2 AuthorsBehnam Kamkar; Saeid Hassanpour Bourkheili; Afshin Soltani; Noor Mohammad Nazari; Hossein Kazemi; Kambiz Gharanjic;Abstract Increasing the use of energy inputs in agricultural section has been led to numerous environmental concerns such as greenhouse gas (GHG) emissions, high consumption of non-renewable resources, loss of biodiversity and environment pollutions. The study was aimed to analyze the energy use efficiency (EUE) and estimation of GHG emissions from rainfed–based canola production systems (RCPSs) in Iran. In this study, data were collected from 35 farms in Golestan province (northeast of Iran) by a face to face questionnaire performed and statistical yearbooks of 2014. The amount of GHG emissions (per hectare) from inputs used in RCPSs was calculated using CO 2 emissions coefficient of agricultural inputs. Results showed that the EUE and net energy (NE) were as 3.44 and 35,537.81 MJ ha −1 , respectively. The value of these indices for the study area indicated that surveyed fields are approximately efficient in the use of energy for canola production. The highest share of energy consumption belonged to nitrogen fertilizer (42.09%) followed by diesel fuel (39.81%). In production of rainfed canola, GHG emission was estimated as 1009.91 kg CO 2 equivalent per hectare. Based on the results, nitrogen fertilizer (44.15%), diesel fuel (30.16%) and machinery (14.49%) for field operations had the highest share of GHG emission. The total consumed energy by inputs could be classified as direct energy (40.09%), and indirect energy (59.91%) or renewable energy (2.02%) and nonrenewable energy (97.98%). These results demonstrate that the share of renewable energies in canola production is very low in the studied region and agriculture in Iran is very much dependent on non-renewable energies. In this study, the energy use status in RCPSs has analyzed and the main involved causes have been interpreted.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2016.10.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu36 citations 36 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2016.10.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Springer Science and Business Media LLC Funded by:UKRI | DER Centres – A National ...UKRI| DER Centres – A National Network of PEMD Centres of ExcellenceShabnam Pourshirazi; Afshin Soltani; Ebrahim Zeinali; Benjamin Torabi; Adnan Arshad;pmid: 35437651
Alfalfa is a major forage crop in Iran. To quantify the impact of climate change on its yield and water application for irrigation in Iran, the SSM-iCrop2 simulation model and two GCMs of IPSL and HadGEM were used under RCP4.5 and RCP8.5 for the 2050s. Despite increased temperatures, alfalfa forage yield will increase in most of the regions across the country due to acceleration of spring regrowth, a higher number of cuttings, increased incident and received photosynthetically active radiation because of increased growing season length due to increased temperatures, and positive effect of CO2 on photosynthesis and radiation use efficiency. Changes in climatic conditions have had a significant impact on alfalfa net irrigation water, and the sum of net irrigation water has a direct relationship with alfalfa yield. Due to increased temperature, changes in rainfall, and improved concentration of atmospheric CO2, the forage yield of alfalfa will fluctuate highly under all climatic scenarios. The highest increase and decrease in the average yield using the HadGEM model under RCP8.5 was 32 and - 33%, respectively. The average net irrigation water of alfalfa increased by 36% in the HadGEM model under RCP8.5 and decreased by - 41% in the IPSL model under RCP8.5. Therefore, to improve alfalfa yield in Iran in the future, strategies compatible such as high temperature-tolerant cultivars may be the most reasonable approaches.
Environmental Scienc... arrow_drop_down Environmental Science and Pollution ResearchArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11356-022-20287-x&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Environmental Scienc... arrow_drop_down Environmental Science and Pollution ResearchArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11356-022-20287-x&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022 South Africa, Germany, FrancePublisher:Elsevier BV Kritika Kothari; Rafael Battisti; Kenneth J. Boote; Sotirios Archontoulis; Adriana Confalone; Julie Constantin; Santiago Vianna Cuadra; Philippe Debaeke; Babacar Faye; Brian Grant; Gerrit Hoogenboom; Qi Jing; Michael van der Laan; Fernando Antônio Macena da Silva; Fábio Ricardo Marin; Alireza Nehbandani; Claas Nendel; Larry C. Purcell; Budong Qian; Alex C. Ruane; Céline Schoving; Evandro Henrique Figueiredo Moura da Silva; Ward Smith; Afshin Soltani; Amit Kumar Srivastava; Nilson Aparecido Vieira; Stacey Slone; Montserrat Salmerón;Une estimation précise du rendement des cultures dans les scénarios de changement climatique est essentielle pour quantifier notre capacité à nourrir une population croissante et à développer des adaptations agronomiques pour répondre à la demande alimentaire future. Une évaluation coordonnée des simulations de rendement à partir de modèles écophysiologiques basés sur les processus pour l'évaluation de l'impact du changement climatique fait toujours défaut pour le soja, la légumineuse à grains la plus cultivée et la principale source de protéines dans notre chaîne alimentaire. Dans cette première étude multimodèle sur le soja, nous avons utilisé dix modèles de premier plan capables de simuler le rendement du soja sous différentes températures et concentrations atmosphériques de CO2 [CO2] pour quantifier l'incertitude dans les simulations de rendement du soja en réponse à ces facteurs. Les modèles ont d'abord été paramétrés avec des données mesurées de haute qualité provenant de cinq environnements contrastés. Nous avons trouvé une variabilité considérable entre les modèles dans les réponses de rendement simulées à l'augmentation de la température et du [CO2]. Par exemple, en cas d'augmentation de la température de + 3 °C dans notre endroit le plus frais en Argentine, certains modèles ont simulé que le rendement diminuerait jusqu'à 24 %, tandis que d'autres simulaient une augmentation du rendement allant jusqu'à 29 %. Dans notre emplacement le plus chaud au Brésil, les modèles ont simulé une réduction du rendement allant d'une diminution de 38 % sous + 3 °C à une augmentation de la température sans effet sur le rendement. De même, en augmentant le [CO2] de 360 à 540 ppm, les modèles ont simulé une augmentation du rendement allant de 6% à 31%. L'étalonnage du modèle n'a pas réduit la variabilité entre les modèles, mais a eu un effet inattendu sur la modification des réponses du rendement à la température pour certains des modèles. La forte incertitude dans les réponses des modèles indique l'applicabilité limitée des modèles individuels pour les projections alimentaires du changement climatique. Cependant, la moyenne d'ensemble des simulations à travers les modèles était un outil efficace pour réduire la forte incertitude dans les simulations de rendement du soja associées aux modèles individuels et à leur paramétrage. Les réponses du rendement moyen de l'ensemble à la température et au [CO2] étaient similaires à celles rapportées dans la littérature. Notre étude est la première démonstration des avantages obtenus en utilisant un ensemble de modèles de légumineuses à grains pour les projections alimentaires du changement climatique, et souligne qu'un développement plus poussé du modèle du soja avec des expériences sous des [CO2] et des températures élevées est nécessaire pour réduire l'incertitude des modèles individuels. Una estimación precisa del rendimiento de los cultivos en escenarios de cambio climático es esencial para cuantificar nuestra capacidad para alimentar a una población en crecimiento y desarrollar adaptaciones agronómicas para satisfacer la demanda futura de alimentos. Todavía falta una evaluación coordinada de las simulaciones de rendimiento a partir de modelos ecofisiológicos basados en procesos para la evaluación del impacto del cambio climático para la soja, la leguminosa de grano más cultivada y la principal fuente de proteínas en nuestra cadena alimentaria. En este primer estudio multimodelo de soja, utilizamos diez modelos prominentes capaces de simular el rendimiento de la soja a diferentes temperaturas y concentraciones de CO2 atmosférico [CO2] para cuantificar la incertidumbre en las simulaciones de rendimiento de soja en respuesta a estos factores. Los modelos se parametrizaron por primera vez con datos medidos de alta calidad de cinco entornos contrastantes. Encontramos una variabilidad considerable entre los modelos en las respuestas de rendimiento simuladas al aumento de la temperatura y [CO2]. Por ejemplo, bajo un aumento de temperatura de + 3 ° C en nuestra ubicación más fresca en Argentina, algunos modelos simularon que el rendimiento se reduciría hasta un 24%, mientras que otros simularon aumentos de rendimiento de hasta un 29%. En nuestra ubicación más cálida en Brasil, los modelos simularon una reducción del rendimiento que va desde una disminución del 38% con un aumento de temperatura de + 3 ° C hasta ningún efecto en el rendimiento. Del mismo modo, al aumentar [CO2] de 360 a 540 ppm, los modelos simularon un aumento del rendimiento que osciló entre el 6% y el 31%. La calibración del modelo no redujo la variabilidad entre los modelos, pero tuvo un efecto inesperado en la modificación de las respuestas de rendimiento a la temperatura para algunos de los modelos. La alta incertidumbre en las respuestas de los modelos indica la aplicabilidad limitada de los modelos individuales para las proyecciones alimentarias del cambio climático. Sin embargo, la media del conjunto de simulaciones entre modelos fue una herramienta efectiva para reducir la alta incertidumbre en las simulaciones de rendimiento de soja asociadas con modelos individuales y su parametrización. Las respuestas de rendimiento medio del conjunto a la temperatura y [CO2] fueron similares a las informadas en la literatura. Nuestro estudio es la primera demostración de los beneficios logrados al utilizar un conjunto de modelos de leguminosas de grano para las proyecciones de alimentos del cambio climático, y destaca que se necesita un mayor desarrollo del modelo de soja con experimentos bajo [CO2] y temperatura elevadas para reducir la incertidumbre de los modelos individuales. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models. يعد التقدير الدقيق لمحصول المحاصيل في ظل سيناريوهات تغير المناخ أمرًا ضروريًا لتحديد قدرتنا على إطعام عدد متزايد من السكان وتطوير التكيفات الزراعية لتلبية الطلب على الغذاء في المستقبل. لا يزال التقييم المنسق لمحاكاة الغلة من النماذج الفسيولوجية البيئية القائمة على العمليات لتقييم تأثير تغير المناخ مفقودًا بالنسبة لفول الصويا، وهو بقول الحبوب الأكثر زراعة على نطاق واسع والمصدر الرئيسي للبروتين في سلسلتنا الغذائية. في هذه الدراسة الأولى متعددة النماذج لفول الصويا، استخدمنا عشرة نماذج بارزة قادرة على محاكاة محصول فول الصويا تحت درجات حرارة متفاوتة وتركيز ثاني أكسيد الكربون في الغلاف الجوي [CO2] لقياس عدم اليقين في محاكاة محصول فول الصويا استجابة لهذه العوامل. تم قياس النماذج أولاً ببيانات مقاسة عالية الجودة من خمس بيئات متباينة. وجدنا تباينًا كبيرًا بين النماذج في استجابات العائد المحاكاة لزيادة درجة الحرارة و [CO2]. على سبيل المثال، في ظل ارتفاع درجة الحرارة بمقدار + 3 درجات مئوية في أروع موقع لنا في الأرجنتين، قامت بعض النماذج بمحاكاة أن العائد سيقلل بنسبة تصل إلى 24 ٪، بينما يزيد العائد المحاكى الآخر بنسبة تصل إلى 29 ٪. في موقعنا الأكثر دفئًا في البرازيل، قامت النماذج بمحاكاة انخفاض العائد الذي يتراوح بين انخفاض بنسبة 38 ٪ تحت + ارتفاع درجة حرارة 3 درجات مئوية إلى عدم التأثير على العائد. وبالمثل، عند زيادة [ثاني أكسيد الكربون] من 360 إلى 540 جزء في المليون، قامت النماذج بمحاكاة زيادة العائد التي تراوحت من 6 ٪ إلى 31 ٪. لم تقلل معايرة النموذج من التباين عبر النماذج ولكن كان لها تأثير غير متوقع على تعديل استجابات الخضوع لدرجة الحرارة لبعض النماذج. يشير عدم اليقين الشديد في الاستجابات النموذجية إلى التطبيق المحدود للنماذج الفردية للتوقعات الغذائية لتغير المناخ. ومع ذلك، كان المتوسط الجماعي للمحاكاة عبر النماذج أداة فعالة للحد من عدم اليقين العالي في محاكاة غلة فول الصويا المرتبطة بالنماذج الفردية ومعلماتها. كانت استجابات متوسط العائد على درجة الحرارة و [CO2] متشابهة مع تلك الواردة في الأدبيات. دراستنا هي أول عرض توضيحي للفوائد التي تحققت من استخدام مجموعة من نماذج البقوليات لتوقعات تغير المناخ الغذائية، وتسلط الضوء على الحاجة إلى مزيد من تطوير نموذج فول الصويا مع التجارب تحت [CO2] ودرجة الحرارة المرتفعة لتقليل عدم اليقين من النماذج الفردية.
UP Research Data Rep... arrow_drop_down UP Research Data RepositoryArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Institut National de la Recherche Agronomique: ProdINRAArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)Publikationsserver der Universität PotsdamArticle . 2022License: CC BYData sources: Publikationsserver der Universität Potsdamadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 35 citations 35 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert UP Research Data Rep... arrow_drop_down UP Research Data RepositoryArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Institut National de la Recherche Agronomique: ProdINRAArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)Publikationsserver der Universität PotsdamArticle . 2022License: CC BYData sources: Publikationsserver der Universität Potsdamadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020Publisher:EDP Sciences Authors: Alireza Nehbandani; Afshin Soltani; Faranak Nourbakhsh; Amir Dadrasi;doi: 10.1051/ocl/2020057
Crop modelling has the potential to contribute to food security. In this study, to provide a simple model for estimating the soybean potential yield and phenological stages in Iran, a simulation model (SSM_iCrop2) was parameterized and tested. This model estimates the soybean phenological stages and potential yield based on the weather data (minimum and maximum temperature, solar radiation and rainfall) using the phenological models such as leaf area development, mass production and partitioning and soil water balance. Regarding the model parametrization, the two maturities groups of 3 and 5 with the temperature unit of 2000 and 2400 growth degrees day (GDD) were chosen. The model evaluation results indicated that the soybean yield ranged between 1.9 and 4.8 with the average of 3.5 t.ha−1, while the range of simulated yield changes between 1.8 and 4.7 with the average of 3.7 t.ha−1. Comparing the observed yield to the simulated yield, values of r, CV and RMSE were obtained 0.84, 13%, 0.5 t.ha−1 which indicates the high accuracy of the model. All of these results indicated that the estimated model parameters are high accuracy for use in the simulation of soybean yield at the country level.
Oilseeds and fats, c... arrow_drop_down Oilseeds and fats, crops and lipidsArticle . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Oilseeds and fats, c... arrow_drop_down Oilseeds and fats, crops and lipidsArticle . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/ocl/2020057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Elsevier BV Authors: Hossein Kazemi; Ebrahim Zeinali; S M Alimagham; Afshin Soltani;Abstract Soybean ( Glycine max L.) is grown in cropping systems of Gorgan (northeast of Iran) as an oil crop. Energy flow and greenhouse gas (GHG) emissions of soybean production were analyzed based on four major production scenarios in this region. The study aimed to evaluate fuel and energy consumption and GHG emissions in order to identify and introduce the most efficient and environmentally friendly scenarios in the region. Four scenarios were included: Scenarios I, II, III and IV. The first two scenarios are known as mechanized scenarios and constitute the production systems adopted by the practices of local farmers in recent years. Scenario I included modern equipment, e.g. no-till, gun-sprinklers system, and high amount of chemical use. Scenario II, encompassed combination machine, center pivot-sprinklers system, and high consumption of fertilizers and chemicals applied by most of the farmers. Scenarios III and IV are known as conventional scenarios: where all operations (i.e. tillage, sowing and spraying) were done with less powerful tractors (60–75 hp) with manually performed fertilizer treatments. These scenarios were different only in terms of tillage operation. In this research, data were collected from 26 farmers using a face-to-face questionnaire-based survey, in 2015. Results revealed that the highest (3.18) energy use efficiency was obtained in Scenario IV (conventional scenario). Water consumption in Scenario II was less than other scenarios. Also, the lowest amount of GHG emissions was 1265.1 kg eq-CO 2 ha −1 in Scenario IV and the highest amount belonged to Scenario II (2969.2 kg eq-CO 2 ha −1 ). These results demonstrate that electricity accounted for the highest energy use in Scenarios I (78%) and II (48%), while fuel was the predominant energy consumed in Scenarios III (44%) and IV (37%). In general, conventional scenarios were found to be more environmentally friendly in Gorgan. These results revealed that there is a huge potential for improving energy efficiency in studied scenarios, especially in mechanized scenarios.
Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2017 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu30 citations 30 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2017 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2020 Netherlands, FrancePublisher:Elsevier BV Ebrahim Zeinali; M.K. van Ittersum; Afshin Soltani; S M Alimagham; S. Ghassemi; Vincent Vadez; Vincent Vadez; A. Nehbandani; Eskandar Zand; B. Torabi; Thomas R. Sinclair;Assessing the food availability and food security of countries is a critical exercise in which crop simulation models are essential. Application of crop models has been limited often to estimate yield per unit area of one or a few important field crops, whereas what is really required is the total national production of diverse crops including forages, vegetables and fruit trees that compete for limited resources of land and water. In this study a simple crop model (SSM-iCrop2; Simple Simulation Models) was set up for an entire country using a bottom-up approach such that it provides representative estimates of potential yield and other crop properties at provincial level as influenced by climate, soil, management and cultivar. The information is then used to calculate total plant production at province and country levels, as influenced by available land and water resources and by the efficiency of utilizing the resources using the concepts relative yield gap and irrigation efficiency. Iran was used as a case study to develop the modeling framework and illustrative outputs. Development of the framework resulted in accumulation of large bodies of valuable geospatial information and statistics across disciplines that are critical for analysis of plant production at a country level. The framework allows different scenarios of national plant production to be evaluated. This includes assessing the possibility of increasing national plant production via intensification, optimizing water allocation across plant species at province and country levels by changing the cropping pattern, and assessing and prioritizing possible ways of adapting a country's agriculture to limited land and water resources and climate change.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 21 citations 21 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102859&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Springer Science and Business Media LLC Authors: Mohsen Abshenas; Behnam Kamkar; Afshin Soltani; Hossein Kazemi;pmid: 36068442
Climate change is one of the most important threats to food security. Earth's temperature is reported to increase by 1.5 to 4 °C by the end of the twenty-first century, compared to the base period (1850-1900), and will continue after 2100. Different models have been used to investigate the effects of climate change on different plant responses, including the exponential downscale statistical model of SDSM. Photosynthesis, respiration, and production are some of the first components to be affected by temperature which are discussed here. This study was aimed to introduce and compare the best interpolation method of main temperatures and precipitation to simulate the rate of photosynthesis, total respiration (total growth and maintenance respiration), and dry matter production of wheat in Golestan Province under climate change. Long-term data of 38 synoptic meteorological stations were used to interpolate the main temperature variables and provide reliable maps. Then, temperature change (ΔT) was used to simulate photosynthesis, total respiration, and dry matter production using the canopy photosynthesis simulation model (Can_Phs). The results clearly showed that by changing the minimum temperature by 1.1 to 3.1 °C and the maximum temperature by 2.3 to 4 °C, the amount of wheat production in the study area will be affected in 2050. This increase in temperature can reduce the length of the growing season in autumn wheat and limit the duration of intercepting light and capturing other resources, which in turn leads to a decrease in photosynthesis and increased respiration during the growing season.
Environmental Monito... arrow_drop_down Environmental Monitoring and AssessmentArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10661-022-10428-7&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
more_vert Environmental Monito... arrow_drop_down Environmental Monitoring and AssessmentArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s10661-022-10428-7&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2013Publisher:Elsevier BV Authors: Elias Soltani; Ebrahim Zeinali; Afshin Soltani; M H Rajabi;Abstract The objectives of this study were to analyze energy use and greenhouse gases (GHG) emissions in various wheat production scenarios in north eastern Iran and to identify measures to reduce energy use and GHG emissions. Three high-input, a low-input, a better crop management and a usual production scenarios were included. All activities and production processes were monitored and recorded. Averages of total energy input and output were 15.58 and 94.4 GJ ha−1, respectively. Average across scenarios, GHG emissions of 1137 kg CO2-eq ha−1 and 291 kg CO2-eq t−1 were estimated. The key factors relating to energy use and GHG emissions were seedbed preparation and sowing and applications of nitrogen fertilizer. The better crop management production scenario required 38% lower nitrogen fertilizer (and 33% lower total fertilizer), consumed 11% less input energy and resulted in 33% more grain yield and output energy compared to the usual production scenario. It also resulted in 20% less GHG emissions per unit field area and 40% less GHG emissions per ton of grain. It was concluded that this scenario was the cleaner production scenario in terms of energy use and GHG emissions. Measures of improvement in energy use and GHG emission were identified.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2012.12.022&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu108 citations 108 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2012.12.022&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal , Other literature type 2020 France, NetherlandsPublisher:Elsevier BV M.K. van Ittersum; S. Rahban; Z. Mohammadzadeh; Z. Mohammadzadeh; A. Nehbandani; S. Pourshirazi; H. Kamari; B. Torabi; Thomas R. Sinclair; M. Zahed; S M Alimagham; R Hosseini; R Hosseini; Afshin Soltani; S. Keramat; A. Dadrasi; O. Alasti; Ebrahim Zeinali; R. Arabameri; H. Fayazi; Vincent Vadez; Vincent Vadez; Eskandar Zand; S. Ghassemi; S. Mohammadi;Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senescence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straight-forward. The resultant model (SSM-iCrop2) was parameterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102855&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 34 citations 34 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.agsy.2020.102855&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2018Publisher:Elsevier BV Authors: Behnam Kamkar; Hossein Kazemi; Mohsen Shokrgozar; Afshin Soltani;Abstract In this study, the energy pattern of cotton production was analyzed and compared by energy indicators in the Darab (with arid climate) and Gorgan (with sub-humid climate) regions, in Iran. For this purpose, different climatic conditions, agronomic managements, energy inputs and cotton varieties were considered. The data were collected from a survey of 30 cotton fields in each region during 2013–2014. All agricultural managements in the studied fields were monitored and recorded. Then, some energy related indicators, including renewable and non-renewable energies, energy use efficiency, direct and indirect energies, net energy, energy productivity and specific energy were calculated. On the base of obtained results, total energy consumption of cotton production was estimated as 36,189.03 in Darab and 31,860.6 MJ ha−1 in Gorgan. The factors relating to energy consumption were diesel fuel (Darab 39.09% and Gorgan 59.94%), and fertilizers (Darab 16.9% and Gorgan 15.25%). The cotton output energy was being as 34,076.04 MJ ha−1for Darab and 35,231.26 MJ ha−1for Gorgan. Also, energy use efficiency was calculated as 0.942 in Darab (as an arid climate) and 1.106 in Gorgan (as a sub-humid climate). The indirect energy and non-renewable energy were relatively high in Gorgan compared to Darab. It was concluded that energy productivity index implies that lower units of output was obtained per unit energy in Darab region. Also, the high ratio of non-renewable energy in total used energy inputs causes negative effects on the sustainability of cotton production systems.
Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2018 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.jclepro.2018.04.195&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu27 citations 27 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2018 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2016Publisher:Elsevier BV Authors: Behnam Kamkar; Saeid Hassanpour Bourkheili; Afshin Soltani; Noor Mohammad Nazari; +2 AuthorsBehnam Kamkar; Saeid Hassanpour Bourkheili; Afshin Soltani; Noor Mohammad Nazari; Hossein Kazemi; Kambiz Gharanjic;Abstract Increasing the use of energy inputs in agricultural section has been led to numerous environmental concerns such as greenhouse gas (GHG) emissions, high consumption of non-renewable resources, loss of biodiversity and environment pollutions. The study was aimed to analyze the energy use efficiency (EUE) and estimation of GHG emissions from rainfed–based canola production systems (RCPSs) in Iran. In this study, data were collected from 35 farms in Golestan province (northeast of Iran) by a face to face questionnaire performed and statistical yearbooks of 2014. The amount of GHG emissions (per hectare) from inputs used in RCPSs was calculated using CO 2 emissions coefficient of agricultural inputs. Results showed that the EUE and net energy (NE) were as 3.44 and 35,537.81 MJ ha −1 , respectively. The value of these indices for the study area indicated that surveyed fields are approximately efficient in the use of energy for canola production. The highest share of energy consumption belonged to nitrogen fertilizer (42.09%) followed by diesel fuel (39.81%). In production of rainfed canola, GHG emission was estimated as 1009.91 kg CO 2 equivalent per hectare. Based on the results, nitrogen fertilizer (44.15%), diesel fuel (30.16%) and machinery (14.49%) for field operations had the highest share of GHG emission. The total consumed energy by inputs could be classified as direct energy (40.09%), and indirect energy (59.91%) or renewable energy (2.02%) and nonrenewable energy (97.98%). These results demonstrate that the share of renewable energies in canola production is very low in the studied region and agriculture in Iran is very much dependent on non-renewable energies. In this study, the energy use status in RCPSs has analyzed and the main involved causes have been interpreted.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2016.10.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu36 citations 36 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2016.10.010&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022Publisher:Springer Science and Business Media LLC Funded by:UKRI | DER Centres – A National ...UKRI| DER Centres – A National Network of PEMD Centres of ExcellenceShabnam Pourshirazi; Afshin Soltani; Ebrahim Zeinali; Benjamin Torabi; Adnan Arshad;pmid: 35437651
Alfalfa is a major forage crop in Iran. To quantify the impact of climate change on its yield and water application for irrigation in Iran, the SSM-iCrop2 simulation model and two GCMs of IPSL and HadGEM were used under RCP4.5 and RCP8.5 for the 2050s. Despite increased temperatures, alfalfa forage yield will increase in most of the regions across the country due to acceleration of spring regrowth, a higher number of cuttings, increased incident and received photosynthetically active radiation because of increased growing season length due to increased temperatures, and positive effect of CO2 on photosynthesis and radiation use efficiency. Changes in climatic conditions have had a significant impact on alfalfa net irrigation water, and the sum of net irrigation water has a direct relationship with alfalfa yield. Due to increased temperature, changes in rainfall, and improved concentration of atmospheric CO2, the forage yield of alfalfa will fluctuate highly under all climatic scenarios. The highest increase and decrease in the average yield using the HadGEM model under RCP8.5 was 32 and - 33%, respectively. The average net irrigation water of alfalfa increased by 36% in the HadGEM model under RCP8.5 and decreased by - 41% in the IPSL model under RCP8.5. Therefore, to improve alfalfa yield in Iran in the future, strategies compatible such as high temperature-tolerant cultivars may be the most reasonable approaches.
Environmental Scienc... arrow_drop_down Environmental Science and Pollution ResearchArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11356-022-20287-x&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Environmental Scienc... arrow_drop_down Environmental Science and Pollution ResearchArticle . 2022 . Peer-reviewedLicense: Springer TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s11356-022-20287-x&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2022 South Africa, Germany, FrancePublisher:Elsevier BV Kritika Kothari; Rafael Battisti; Kenneth J. Boote; Sotirios Archontoulis; Adriana Confalone; Julie Constantin; Santiago Vianna Cuadra; Philippe Debaeke; Babacar Faye; Brian Grant; Gerrit Hoogenboom; Qi Jing; Michael van der Laan; Fernando Antônio Macena da Silva; Fábio Ricardo Marin; Alireza Nehbandani; Claas Nendel; Larry C. Purcell; Budong Qian; Alex C. Ruane; Céline Schoving; Evandro Henrique Figueiredo Moura da Silva; Ward Smith; Afshin Soltani; Amit Kumar Srivastava; Nilson Aparecido Vieira; Stacey Slone; Montserrat Salmerón;Une estimation précise du rendement des cultures dans les scénarios de changement climatique est essentielle pour quantifier notre capacité à nourrir une population croissante et à développer des adaptations agronomiques pour répondre à la demande alimentaire future. Une évaluation coordonnée des simulations de rendement à partir de modèles écophysiologiques basés sur les processus pour l'évaluation de l'impact du changement climatique fait toujours défaut pour le soja, la légumineuse à grains la plus cultivée et la principale source de protéines dans notre chaîne alimentaire. Dans cette première étude multimodèle sur le soja, nous avons utilisé dix modèles de premier plan capables de simuler le rendement du soja sous différentes températures et concentrations atmosphériques de CO2 [CO2] pour quantifier l'incertitude dans les simulations de rendement du soja en réponse à ces facteurs. Les modèles ont d'abord été paramétrés avec des données mesurées de haute qualité provenant de cinq environnements contrastés. Nous avons trouvé une variabilité considérable entre les modèles dans les réponses de rendement simulées à l'augmentation de la température et du [CO2]. Par exemple, en cas d'augmentation de la température de + 3 °C dans notre endroit le plus frais en Argentine, certains modèles ont simulé que le rendement diminuerait jusqu'à 24 %, tandis que d'autres simulaient une augmentation du rendement allant jusqu'à 29 %. Dans notre emplacement le plus chaud au Brésil, les modèles ont simulé une réduction du rendement allant d'une diminution de 38 % sous + 3 °C à une augmentation de la température sans effet sur le rendement. De même, en augmentant le [CO2] de 360 à 540 ppm, les modèles ont simulé une augmentation du rendement allant de 6% à 31%. L'étalonnage du modèle n'a pas réduit la variabilité entre les modèles, mais a eu un effet inattendu sur la modification des réponses du rendement à la température pour certains des modèles. La forte incertitude dans les réponses des modèles indique l'applicabilité limitée des modèles individuels pour les projections alimentaires du changement climatique. Cependant, la moyenne d'ensemble des simulations à travers les modèles était un outil efficace pour réduire la forte incertitude dans les simulations de rendement du soja associées aux modèles individuels et à leur paramétrage. Les réponses du rendement moyen de l'ensemble à la température et au [CO2] étaient similaires à celles rapportées dans la littérature. Notre étude est la première démonstration des avantages obtenus en utilisant un ensemble de modèles de légumineuses à grains pour les projections alimentaires du changement climatique, et souligne qu'un développement plus poussé du modèle du soja avec des expériences sous des [CO2] et des températures élevées est nécessaire pour réduire l'incertitude des modèles individuels. Una estimación precisa del rendimiento de los cultivos en escenarios de cambio climático es esencial para cuantificar nuestra capacidad para alimentar a una población en crecimiento y desarrollar adaptaciones agronómicas para satisfacer la demanda futura de alimentos. Todavía falta una evaluación coordinada de las simulaciones de rendimiento a partir de modelos ecofisiológicos basados en procesos para la evaluación del impacto del cambio climático para la soja, la leguminosa de grano más cultivada y la principal fuente de proteínas en nuestra cadena alimentaria. En este primer estudio multimodelo de soja, utilizamos diez modelos prominentes capaces de simular el rendimiento de la soja a diferentes temperaturas y concentraciones de CO2 atmosférico [CO2] para cuantificar la incertidumbre en las simulaciones de rendimiento de soja en respuesta a estos factores. Los modelos se parametrizaron por primera vez con datos medidos de alta calidad de cinco entornos contrastantes. Encontramos una variabilidad considerable entre los modelos en las respuestas de rendimiento simuladas al aumento de la temperatura y [CO2]. Por ejemplo, bajo un aumento de temperatura de + 3 ° C en nuestra ubicación más fresca en Argentina, algunos modelos simularon que el rendimiento se reduciría hasta un 24%, mientras que otros simularon aumentos de rendimiento de hasta un 29%. En nuestra ubicación más cálida en Brasil, los modelos simularon una reducción del rendimiento que va desde una disminución del 38% con un aumento de temperatura de + 3 ° C hasta ningún efecto en el rendimiento. Del mismo modo, al aumentar [CO2] de 360 a 540 ppm, los modelos simularon un aumento del rendimiento que osciló entre el 6% y el 31%. La calibración del modelo no redujo la variabilidad entre los modelos, pero tuvo un efecto inesperado en la modificación de las respuestas de rendimiento a la temperatura para algunos de los modelos. La alta incertidumbre en las respuestas de los modelos indica la aplicabilidad limitada de los modelos individuales para las proyecciones alimentarias del cambio climático. Sin embargo, la media del conjunto de simulaciones entre modelos fue una herramienta efectiva para reducir la alta incertidumbre en las simulaciones de rendimiento de soja asociadas con modelos individuales y su parametrización. Las respuestas de rendimiento medio del conjunto a la temperatura y [CO2] fueron similares a las informadas en la literatura. Nuestro estudio es la primera demostración de los beneficios logrados al utilizar un conjunto de modelos de leguminosas de grano para las proyecciones de alimentos del cambio climático, y destaca que se necesita un mayor desarrollo del modelo de soja con experimentos bajo [CO2] y temperatura elevadas para reducir la incertidumbre de los modelos individuales. An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models. يعد التقدير الدقيق لمحصول المحاصيل في ظل سيناريوهات تغير المناخ أمرًا ضروريًا لتحديد قدرتنا على إطعام عدد متزايد من السكان وتطوير التكيفات الزراعية لتلبية الطلب على الغذاء في المستقبل. لا يزال التقييم المنسق لمحاكاة الغلة من النماذج الفسيولوجية البيئية القائمة على العمليات لتقييم تأثير تغير المناخ مفقودًا بالنسبة لفول الصويا، وهو بقول الحبوب الأكثر زراعة على نطاق واسع والمصدر الرئيسي للبروتين في سلسلتنا الغذائية. في هذه الدراسة الأولى متعددة النماذج لفول الصويا، استخدمنا عشرة نماذج بارزة قادرة على محاكاة محصول فول الصويا تحت درجات حرارة متفاوتة وتركيز ثاني أكسيد الكربون في الغلاف الجوي [CO2] لقياس عدم اليقين في محاكاة محصول فول الصويا استجابة لهذه العوامل. تم قياس النماذج أولاً ببيانات مقاسة عالية الجودة من خمس بيئات متباينة. وجدنا تباينًا كبيرًا بين النماذج في استجابات العائد المحاكاة لزيادة درجة الحرارة و [CO2]. على سبيل المثال، في ظل ارتفاع درجة الحرارة بمقدار + 3 درجات مئوية في أروع موقع لنا في الأرجنتين، قامت بعض النماذج بمحاكاة أن العائد سيقلل بنسبة تصل إلى 24 ٪، بينما يزيد العائد المحاكى الآخر بنسبة تصل إلى 29 ٪. في موقعنا الأكثر دفئًا في البرازيل، قامت النماذج بمحاكاة انخفاض العائد الذي يتراوح بين انخفاض بنسبة 38 ٪ تحت + ارتفاع درجة حرارة 3 درجات مئوية إلى عدم التأثير على العائد. وبالمثل، عند زيادة [ثاني أكسيد الكربون] من 360 إلى 540 جزء في المليون، قامت النماذج بمحاكاة زيادة العائد التي تراوحت من 6 ٪ إلى 31 ٪. لم تقلل معايرة النموذج من التباين عبر النماذج ولكن كان لها تأثير غير متوقع على تعديل استجابات الخضوع لدرجة الحرارة لبعض النماذج. يشير عدم اليقين الشديد في الاستجابات النموذجية إلى التطبيق المحدود للنماذج الفردية للتوقعات الغذائية لتغير المناخ. ومع ذلك، كان المتوسط الجماعي للمحاكاة عبر النماذج أداة فعالة للحد من عدم اليقين العالي في محاكاة غلة فول الصويا المرتبطة بالنماذج الفردية ومعلماتها. كانت استجابات متوسط العائد على درجة الحرارة و [CO2] متشابهة مع تلك الواردة في الأدبيات. دراستنا هي أول عرض توضيحي للفوائد التي تحققت من استخدام مجموعة من نماذج البقوليات لتوقعات تغير المناخ الغذائية، وتسلط الضوء على الحاجة إلى مزيد من تطوير نموذج فول الصويا مع التجارب تحت [CO2] ودرجة الحرارة المرتفعة لتقليل عدم اليقين من النماذج الفردية.
UP Research Data Rep... arrow_drop_down UP Research Data RepositoryArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Institut National de la Recherche Agronomique: ProdINRAArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)Publikationsserver der Universität PotsdamArticle . 2022License: CC BYData sources: Publikationsserver der Universität Potsdamadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eja.2022.126482&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 35 citations 35 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert UP Research Data Rep... arrow_drop_down UP Research Data RepositoryArticle . 2022License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Institut National de la Recherche Agronomique: ProdINRAArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)Publikationsserver der Universität PotsdamArticle . 2022License: CC BYData sources: Publikationsserver der Universität Potsdamadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eja.2022.126482&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020Publisher:EDP Sciences Authors: Alireza Nehbandani; Afshin Soltani; Faranak Nourbakhsh; Amir Dadrasi;doi: 10.1051/ocl/2020057
Crop modelling has the potential to contribute to food security. In this study, to provide a simple model for estimating the soybean potential yield and phenological stages in Iran, a simulation model (SSM_iCrop2) was parameterized and tested. This model estimates the soybean phenological stages and potential yield based on the weather data (minimum and maximum temperature, solar radiation and rainfall) using the phenological models such as leaf area development, mass production and partitioning and soil water balance. Regarding the model parametrization, the two maturities groups of 3 and 5 with the temperature unit of 2000 and 2400 growth degrees day (GDD) were chosen. The model evaluation results indicated that the soybean yield ranged between 1.9 and 4.8 with the average of 3.5 t.ha−1, while the range of simulated yield changes between 1.8 and 4.7 with the average of 3.7 t.ha−1. Comparing the observed yield to the simulated yield, values of r, CV and RMSE were obtained 0.84, 13%, 0.5 t.ha−1 which indicates the high accuracy of the model. All of these results indicated that the estimated model parameters are high accuracy for use in the simulation of soybean yield at the country level.
Oilseeds and fats, c... arrow_drop_down Oilseeds and fats, crops and lipidsArticle . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/ocl/2020057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert Oilseeds and fats, c... arrow_drop_down Oilseeds and fats, crops and lipidsArticle . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1051/ocl/2020057&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Elsevier BV Authors: Hossein Kazemi; Ebrahim Zeinali; S M Alimagham; Afshin Soltani;Abstract Soybean ( Glycine max L.) is grown in cropping systems of Gorgan (northeast of Iran) as an oil crop. Energy flow and greenhouse gas (GHG) emissions of soybean production were analyzed based on four major production scenarios in this region. The study aimed to evaluate fuel and energy consumption and GHG emissions in order to identify and introduce the most efficient and environmentally friendly scenarios in the region. Four scenarios were included: Scenarios I, II, III and IV. The first two scenarios are known as mechanized scenarios and constitute the production systems adopted by the practices of local farmers in recent years. Scenario I included modern equipment, e.g. no-till, gun-sprinklers system, and high amount of chemical use. Scenario II, encompassed combination machine, center pivot-sprinklers system, and high consumption of fertilizers and chemicals applied by most of the farmers. Scenarios III and IV are known as conventional scenarios: where all operations (i.e. tillage, sowing and spraying) were done with less powerful tractors (60–75 hp) with manually performed fertilizer treatments. These scenarios were different only in terms of tillage operation. In this research, data were collected from 26 farmers using a face-to-face questionnaire-based survey, in 2015. Results revealed that the highest (3.18) energy use efficiency was obtained in Scenario IV (conventional scenario). Water consumption in Scenario II was less than other scenarios. Also, the lowest amount of GHG emissions was 1265.1 kg eq-CO 2 ha −1 in Scenario IV and the highest amount belonged to Scenario II (2969.2 kg eq-CO 2 ha −1 ). These results demonstrate that electricity accounted for the highest energy use in Scenarios I (78%) and II (48%), while fuel was the predominant energy consumed in Scenarios III (44%) and IV (37%). In general, conventional scenarios were found to be more environmentally friendly in Gorgan. These results revealed that there is a huge potential for improving energy efficiency in studied scenarios, especially in mechanized scenarios.
Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2017 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.jclepro.2017.02.118&type=result"></script>'); --> </script>
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more_vert Journal of Cleaner P... arrow_drop_down Journal of Cleaner ProductionArticle . 2017 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.jclepro.2017.02.118&type=result"></script>'); --> </script>
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