Environmental impacts of irrigated and rain-fed barley production in Iran using life cycle assessment (LCA)

Houshyar, Ehsan

doi:10.5424/sjar/2017152-9094

Cited by 15 publications

(9 citation statements)

References 45 publications

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“…Data were derived from multiple sources. Demographic data (2006,2011,2016) Energy consumption and GHG for crop types were from life cycle analysis studies conducted either in the IARB or other areas of Iran with similar climatological conditions and cultivation practices [wheat: [83]; barley: [84,85]; alfalfa: [86]; legumes: [87]; fruits: [88,89]. Data on irrigation water, land-uses, hours of labor, and the economic return of crop types were from the 2012 and 2015 series of the Updating Studies on Holistic Plan for Water Management of Iran for the IARB [81,82].…”

Section: Data Sourcesmentioning

confidence: 99%

A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin

Taghdisian¹,

Bukkens

Giampietro

2022

Sustainability

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We implemented the semantically open conceptual framework ‘Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism’ (MuSIASEM) to deal with nexus challenges in agricultural production systems in transboundary river basins, using the Iranian Aras River Basin as a case study. The performance of the agricultural sector was characterized for relevant typologies of crop production using metabolic profiles, i.e., inputs and outputs per ton of crop produced, per hectare of land use, and per hour of labor. This analysis was contextualized across hierarchical levels of analysis, including the agronomic context at the regional level (rainfed versus irrigated cultivation), the socio-economic and political context at the national level (food sovereignty; urbanization), and the hydro-ecological context of the larger transboundary river basin (water constraints, GHG emissions). We found that the simultaneous use of two different interrelated logics of aggregation—the productivity of land and labor (relevant for the agronomic and socio-economic dimension) and the density of flows under different land uses (relevant for the hydrological and ecological dimension)—allowed for the identification of trade-offs in policy deliberations. In the case of Iran, it showed that striving for strategic autonomy will exacerbate the current water crisis; with the current cropping patterns, agronomic improvements will not suffice to avert a water crisis. It was concluded that the proposed approach fills an important gap in nexus research, but to effectively guide nexus governance in the region, a co-production of the analysis with social actors as well as more complete data sets at the river basin level would be essential.

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Section: Data Sourcesmentioning

confidence: 99%

A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin

Taghdisian¹,

Bukkens

Giampietro

2022

Sustainability

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“…Conservation tillage systems, new machines, and suitable cultivars were also recommended to reduce the environmental burdens of barley production (Houshyar 2017). The depletion of abiotic resources is the use of resources such as minerals (e.g., phosphate rock) or fossil fuels, which can reduce the access of future generations to these resources.…”

Section: Resourcesmentioning

confidence: 99%

Predicting of Agro-environmental Footprint with Artificial Intelligence (Soybean cultivation in various scenarios)

Kashka

Sarvestani

Pirdashti

et al. 2022

Preprint

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This study employed two artificial intelligence (AI) methods called the ANFIS–FCM algorithm as a novel computational method and an artificial neural network (ANN) as a conventional computational method in order to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The 175 data of life cycle assessment (LCA) method were collected from soybean farms. The two methods called the adaptive neuro-fuzzy inference based on fuzzy C-means clustering algorithm (FCM) and the artificial neural network (ANN) were adopted to predict environmental parameters. For this purpose, the life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF×m2×yr, and 563.55 to 880.61 kg CO2-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment in which the R-S scenario had the best environmental performance. Notably, the emissions caused by the production and application of the diesel fuel followed by chemical fertilizers, particularly N and P fertilizers, were recognized as the most important environmental hotspots in soybean production. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The RMSE and MAPE values obtained from ANFIS–FCM were lower than the values obtained from the ANN model for all environmental indicators. For the ANFIS–FCM and ANN algorithms, R2 ranged between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.

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“…Such 3 times less yield resulting barley imports; on which in 2020 thousand dollars invested which is equivalent to 50/33% of annual average yield (Abid, 2020). Among the Major production of cereal crops in the world after Rice, wheat and Maiz on 4 th rank Hordeum vulgare which is known as Barley is standing (Houshyar, 2017). And even barley is being produced in various areas of the world where important cereals like rice and main are not being grown (Zhou, 2009).…”

Section: Introductionmentioning

confidence: 99%

Identification of Effective Seed Dressing Synthetic Fungicide against Covered Smut of Barley

Ghouri,

Waris

2024

Phytopathogenomics and Disease Control

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Barley is an important field crop. The present study was designed to identify the efficient seed dressing synthetic fungicide against Ustilago hordei causing covered smut of barley. The treatments included three seed dressing fungicides (Bayton 10 DS, Topsin-M, Dividend-Star 36 F.S) and 4th treatment was set as control to compare their efficacy against the disease. The findings of the treatment indicated that crop stand, seedling mortality, smut infected grains and yield characters of barley were found significantly (P<0.05) controlled with treatment of seed inoculation/ seed dressing with treated fungicides. Fungicide Topsin-M ranked first and sowing of barley with Topsin inoculated seed resulted in 198.67 seed germination m-2, 0.70% seedling mortality, 1.34 grains infected by covered smut, 2.08 percent infected grain, 413.33 spikes m-2, 38.16 grains spike-1, 10.75cm spike length and 3220.70kg ha-1 grain yield. The fungicide Bayton 10 DS ranked second and sowing of barley with Bayton inoculated seed resulted in 182.67 seed germination m-2, 1.11% seedling mortality, 2.14 grains infected by covered smut, 3.05 percent infected grain, 395.67 spikes m-2, 36.50 grains spike-1, 10.43cm spike length and 3037.70kg ha-1 grain yield. The seed dressing fungicide Dividend-Star 36 FS ranked third and sowing of barley with Dividend inoculated seed resulted in 171.67 seed germination m-2, 1.69% seedling mortality, 2.51 grains infected by covered smut, 4.85 percent infected grain, 380.67 spikes m-2, 34.61 grains spike-1, 10.30cm spike length and 2980.70kg ha-1 grain yield. The control plots, where the barley was sown without seed inoculation ranked least (4th) and resulted in 159 seed germination m-2, 3.68% seedling mortality, 3.81 grains infected by covered smut, 10.63 percent infected grain, 333.33 spikes m-2, 34.67 grains spike-1, 10.03cm spike length and 2591kg ha-1 yield of the grain. Concluded findings showed that all the treatments showed effective results as compare to control and minimized the covered smut disease in barley crop; however, among all treated seed dressing fungicides, Topsin-M fungicide was identified with better findings to minimize the disease as compared other above captioned fungicides.

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Environmental impacts of irrigated and rain-fed barley production in Iran using life cycle assessment (LCA)

Cited by 15 publications

References 45 publications

A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin

A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin

Predicting of Agro-environmental Footprint with Artificial Intelligence (Soybean cultivation in various scenarios)

Identification of Effective Seed Dressing Synthetic Fungicide against Covered Smut of Barley

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