Energy and Greenhouse Gases Balances of Cotton Farming in Iran: A Case Study

Abstract

This study evaluated the impacts of cotton farming on the climate changes in terms of energy and greenhouse gas (GHG) emission indices. Energy consumption pattern and sensitivity of energy inputs were evaluated and share of each input in GHG emissions was determined in the form of direct and indirect emissions for cotton farms in Golestan province of Iran. The total energy input and energy output were calculated to be 34,424.19 and 41,496.67 MJ/ha respectively. The share of fertilizers by 45.0 % of total energy inputs was the highest. This was followed by energies of fuel (18.4 %) and irrigation (17.9 %) respectively. Fertilizers and fuels were also the biggest producers of GHGs in the farms with shares of 61.95 and 24.32 % of total GHGs emission. Energy ratio, energy balance, energy intensity and energy productivity were found as 1.21, 7,072.48 MJ/ha, 9.79 MJ/kg and 0.10 kg/MJ, respectively. Results of sensitivity analysis indicated that the cotton production was more sensitive to energies of seed and human labour than other inputs and an additional use of 1 MJ of each of these inputs would lead to a change in the yield by −0.75 and 0.73 kg/ha, respectively. The results also showed, in the process of cotton farming 897.80 and 1177.67 kg CO2 – equivalent of direct and indirect GHG were emitted per hectare respectively.

Determination of Energy Use Efficiency and Greenhouse Gas Emission (GHG) of Cotton Cultivation in Batman Province: A Case Study from Beşiri District

“…Similarly, N-RE energy ratio was calculated to be higher than RE by Dagistan et al (2009), Yilmaz et al (2010), Zahedi et al (2014), Kazemi et al (2018), Sami and Reyhani (2018), Semerci et al (2019), Afzalinia (2020), Baran et al (2021) in cotton cultivation. The GHG emission values are shown in Table 6.…”

“…According to Semerci et al (2019), Afzalinia (2020) calculated the ratio of electricity as 58.77% among the most used energy inputs. In other researches, Dagistan et al (2009), Pishgar-Komleh et al (2012b, Sami and Reyhani (2018) calculated the chemical fertilizers as the most used energy inputs. Baran and Gökdoğan / Selcuk J Agr Food Sci, (2022) The consumed total energy input was grouped as 68.79% DE, 31.21% IE, 5.93% RE and 94.07% N-RE (Table 5).…”

“…Studies on EUE and GHG emissions have been made and continue to be done in the world and in Turkey. The examples include cotton (Singh et al 2000;Yilmaz et al 2005;Oren and Ozturk 2006;Kousar et al 2006;Dagistan et al 2009;Khan et al 2009;Zahedi et al 2014;Kazemi et al 2018;Sami and Reyhani 2018;Semerci et al 2019;Afzalinia 2020), tobacco (Moraditochaee 2012; Loghmanpour-zarini and Abedi-firouzjaee 2013; Baran and Gokdogan 2015), sugar beet (Hacıseferoğulları et al 2003;Erdal et al 2007;, potato (Mohammadi et al 2008;Pishgar-Komleh et al 2012a;Gokdogan et al 2018), sunflower Bayhan 2016;Akdemir et al 2017), canola (Unakitan et al 2010Baran et al 2014), olive (Guzman and Alonso 2008;Gökdoğan and Erdoğan 2021), soybean (Mandal et al 2002) etc. In the current research, calculations have been made on the EUE of cotton production and the results have been used to make assessments.…”

A Study on the Factors Affecting Greenhouses Emissions in Cotton in Greece

“…The emissions observed from the application of fertilizers are 421.3 Kg CO2e ha -1 (17.51 %), while from the production of fertilizers 347.7 Kg CO2e ha -1 (14.45 %). The applied fertilization is considered as direct emissions (Sami & Reyhani, 2018) The lowest emissions were 335.3 Kg CO2e ha -1 which corresponds to 13.94 % from the application of plant protection products. The maximum CO2e emissions per kilo of product are observed again in energy use and are 0.34 kg CO2e kg -1 (Figure 3) corresponding to 45.67% (Chart 2) of the total emissions per kilo of product.…”

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

“…Energy flows modeling can be applied to evaluate what measures can be taken to make the system more energy‐efficient . Multiple linear regressions (MLRs) was one of the first mathematical models which was used to model the energy flow of agricultural production systems and several researches have used those models in order to energy flow modeling of crop production . MLR modeling was also used to determine the impacts of each energy inputs on cotton yields.…”