Investigation of CO2 emission from soil and farm inputs in different farming systems in wheat-maize rotation

Investigation of CO2 emission from soil and farm inputs in different farming systems in wheat-maize rotation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of CO2 emission from soil and farm inputs in different farming systems in wheat-maize rotation Eisa bougari, Mohammad Amin Asoodar, Afshin Marzban, navab kazemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3684936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, the amounts of greenhouse gas (equivalent to CO 2 ) emitted from inputs consumption and CO 2 emission from the soil in the various wheat-maize farming systems for one year were evaluated in an experimental design. In the studied systems, the CO 2 eq emission from the inputs for wheat and maize ranged from 258 to 365 kg.ton − 1 and 140 to 313 kg.ton − 1 respectively, which electricity, fertilizer and fuel inputs had the largest share. By changing the agricultural systems, there was a potential to reduce CO 2 eq emission from the inputs by up to 29% per ton of wheat and up to 55% per ton of maize. Results showed that residue management, irrigation method, and tillage had significant effects on CO 2 emission from the soil, while planting method showed no significant effect. Systems with residue (R 1 ) had about 28% higher CO 2 emission from the soil, by changing the tillage method from conventional tillage (T 1 ) to no-tillage (T 3 ) and changing the irrigation method from flood (I 1 ) to drip (I 2 ), a reduction of approximately 20 and 12% in emission was observed, respectively. Among the studied systems, the highest amount of CO 2 emission from the soil was observed in the system with residue, conventional tillage, flood irrigation, and flat planting (R 1 T 1 I 1 P 1 ) with a rate of 7.35 kg.ha − 1 per day. The highest total CO 2 emission from inputs and emission from the soil were observed in R 1 T 1 I 1 P 1 system with a rate of 6129.47 kg.ha − 1 per year, which decreased to 4471.28 kg.ha − 1 per year in R 2 T 3 I 2 P 2 system. CO2 emission No tillage Drip irrigation Raised bed planting Residue Farming systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Agriculture accounts for approximately 14% of greenhouse gas emissions, which has a high potential for reduction (Cichorowski et al, 2015). The adoption of conservation agriculture aims to promote a more sustainable and environmental friendly management system compared to conventional agriculture (Habbs et al, 2008). Crop rotation and soil conservation are principles of conservation agriculture, which have shown 18.9% reduction in carbon emissions in crop rotation compared to monoculture systems, and 14.7% reduction in no-till system compared to conventional tillage (Yin et al, 2017). Cultivation as a crucial agricultural management technique impacts the dispersion and conversion of soil organic carbon (SOC), stability of soil particles, water retention capacity, and soil temperature by regulating pathways for air and water exchange between the soil surface and the atmosphere (Bregaglio et al, 2022). So selecting proper cultivation system is essential to meet sustainability goals and farm productivity. Proper selection of agricultural operations can lead to a decrease in pollutant gases. Soil management is one of the most influential operations affecting carbon storage and carbon dioxide emissions in agricultural systems (Page et al, 2014). Compared to soilless agriculture, soil-based agriculture has a 58% higher CO 2 emissions. CO 2 emission from soil have a direct relationship with soil and air temperature, and intensive soil tillage, heavy rainfall, or extra irrigation increase CO 2 emission from the soil. In a study on alfalfa and wheat crops in North Dakota, irrigation and intensive tillage have resulted in a 27% and 58% increase in CO 2 emission compared to non-irrigated and soilless conditions, respectively (Sainju et al, 2006). Irrigation and soil surface cover also potentially affect carbon and nitrogen cycling, and as a results affect the emissions of CO 2 and NO 2 gases from the soil. Kallenbach et al. (2010), demonstrated that soil covered with residues had 40% and 15% higher CO 2 emission compared to bare soil under surface and drip irrigation methods, respectively. In covered soil with residues, CO 2 and NO 2 emissions increased by 425 milligrams and 60 micrograms per square meter per hour compared to bare soil, respectively. Fossil fuels are also significant contributors to emission of greenhouse gases from the farm, and their consumption is directly related to the number of operations carried out on the farm. Therefore, by managing these operations, fuel and other related inputs reduce and as a results greenhouse gas emissions decrease. Another important factor in the emission of greenhouse gases is the way in which agricultural residues is managed. Burning residues as a managing way releases particulate matter and pollutants into the air. In many developing countries, burning residues is a common practice, and in Iran, unfortunately it is a common way to manage the residues for many farmers. Burning crop residues significantly increases the emission of fine particles in the atmosphere, affecting the air quality in the surrounding areas of the fields and even distant locations. In this regard, a study by Dhammapala et al. (2006), in eastern Washington and northern Idaho reported that 4% of particles smaller than 2.5 microns and 34.5% of carbon emissions were related to burning residues in the agricultural sector. In Iran, there are also numerous cases of burned farmlands, for example in the Khuzestan province as one of the most important province of crops productions, approximately crop residues of 195,000 ha agricultural lands were set on fire in 2019 (Bogari et al, 2023). Therefore, recommending a production pattern that reduces environmental pollutants is essential for sustainable agriculture, and this requires replacing current production methods with new patterns and practices in agriculture. The impact of different land uses in agriculture on the emission of greenhouse gases was investigated using a closed chamber and gas chromatography method (Mahdipour and Landi, 2010). In this study, CO 2 emission from wheat, canola, citrus orchards, and fallow were estimated as 4.47, 3.72, 3.38, and 1.89 t/ha/y, respectively. Dong et al. (2017), examined the effect of tillage on short-term and long-term CO 2 emission in dry regions of China. In this study, three tillage treatments including conventional tillage with a moldboard plow, no tillage with wheat straw mulching, and no tillage without crop residues were studied for their impact on CO 2 emission. Their results showed that immediately after tillage and up to 48 hours, the CO 2 emissions from conventional tillage were significantly higher, but the annual emissions from the wheat straw mulching treatment were higher than the other two tillage methods. The highest emission were estimated as 0.2 grams per square meter per hour in winter, while the lowest emission were 0.04 grams per square meter per hour in summer. CO 2 emission from soil is an important part of the terrestrial ecosystem cycle. Tillage is an influential operation in carbon storage and CO 2 emission in agricultural ecosystems (Page et al, 2014). Increasing concerns about global warming and changes in soil management in agriculture can have significant effects on CO 2 emissions (Sheehy et al, 2015). Research has shown varied results on the impact of tillage on CO 2 emissions, with some studies reporting lower levels (Shahidi et al, 2014) and others indicating higher emissions (Fuetes et al, 2012), while some studies have reported that tillage had no significant effect on gases emission (Aslam et al, 2000). The effect of soil managements on CO 2 emission from soil depends on factors such as soil temperature, soil moisture content, or the interaction between these two factors (Lu et al, 2015). Research suggests that soil temperature and moisture can cause variations of 76 to 96 percent in CO 2 emission from soil (Xu and Qi, 2001). The release of NO 2 and CO 2 from agricultural soils is a result of complex interactions between water, biological, chemical, and physical properties of the soil (Oorts et al, 2007). The agricultural soils in Khuzestan province are often characterized by poor organic matter content and low stability, which can lead to soil structure degradation with excessive flooding irrigation. Implementing drip irrigation with reduced speed and quantity of water can help maintain soil stability (Bougari et al, 2021). Water management can influence these mentioned properties and have an impact on the emission of greenhouse gases (Kallenbach et al, 2010). An increase in greenhouse gas emissions can occur during irrigation hours and several hours after irrigation (Khalil and Bags, 2005). Soil management practices have the potential to reduce CO 2 emission from the soil (Mangalassery et al, 2014 and Abdalla et al,. 2016). In a study, CO 2 emission were examined in different farming systems in sugarcane cultivation, including conventional tillage, minimum tillage, and reduced tillage with residue retention. The highest CO 2 emission was observed in conventional tillage, followed by minimum tillage and reduced tillage, with values of 350, 51.7, and 5.5 grams per square meter, respectively. CO 2 emission in conventional tillage can lead to up to an 80% reduction in soil carbon, emphasizing the need for conservative tillage (Silva-olaya et al, 2013). Wei et al. (2018), investigated the effects of surface and subsurface irrigation on the potential for global warming through the emissions of NO 2 and CO 2 in a laboratory-scale study using soil that had been prepared after tomato harvest. The results of this study showed that the emissions of NO 2 and CO 2 in surface irrigation were 28.9% and 19.4% lower, respectively, compared to subsurface irrigation at 72 and 168 hours after irrigation. In a study conducted in China, it was found that no-tillage resulted in less CO2 emission compared to conventional tillage, while the use of residue mulching in each tillage led to an increase in the emission (Yao et al,. 2023). In general, sustainable agriculture has the potential to reduce greenhouse gas emissions, without a bad side effect on yield (Zhong et al,. 2022). However, no study has been conducted to examine the effects of tillage, irrigation, residue, planting and their interactions on the emission of CO 2 from the soil and CO 2 eq emission from the inputs in wheat-maize rotation. Therefore, this study was carried out to examine CO 2 emission from the soil, CO 2 eq emission from the inputs and the total CO 2 emission in different farming systems, aiming to suggest the best system and estimate effect of each change and their interactions. 2 Materials and Methods 2.1 Site description and experimental design This study was conducted on a farm at Agricultural Sciences and Natural Resources University of Khuzestan (31° 58' N, 48° 35' E) located at north of Ahwaz county during 2018–2019 (autumn of 2018 to spring of 2019). The experimental field was under fallow for one year prior to our experiment. The soil has a clay loam texture (44.5% silt, 36% clay and 19.5% sand) same as most of the region. The experiment was conducted in the form of a split-plot strip factorial design with complete random blocks and three replications. Each replication consisted of 24 treatment combinations, where each treatment combination represented a crop management system. In this design, the main plots were assigned to residue management (including residue retention and residue removal), the subplots were assigned to irrigation methods (including flood irrigation and drip irrigation), and the sub-subplots were assigned to tillage methods (including conventional tillage, reduced tillage, and no-tillage) and planting patterns (including flat planting and raised bed planting). The size of main plot, subplot and sub-subplot were 94 \(\times\) 15 m, 46 \(\times\) 32 m and 6 \(\times\) 15 m, respectively. The distance around the plots was considered to be two meters. This design was implemented for wheat-maize rotation (common rotation in Khuzestan province). The description of the systems studied in this research is provided in Table 1 . Table 1 The studied systems in this study. N description of the systems N description of the systems 1 with residue, conventional, flood, flat (R 1 T 1 I 1 P 1 ) 13 without residue, conventional, flood, flat (R 2 T 1 I 1 P 1 ) 2 with residue, conventional, flood, raised bed (R 1 T 1 I 1 P 2 ) 14 without residue, conventional, flood, raised bed (R 2 T 1 I 1 P 2 ) 3 with residue, reduced, flood, flat (R 1 T 2 I 1 P 1 ) 15 without residue, reduced, flood, flat (R 2 T 2 I 1 P 1 ) 4 with residue, reduced, flood I, raised bed (R 1 T 2 I 1 P 2 ) 16 without residue, reduced, flood, raised bed (R 2 T 2 I 1 P 2 ) 5 with residue, no till, flood, flat (R 1 T 3 I 1 P 1 ) 17 without residue, no till, flood, flat (R 2 T 3 I 1 P 1 ) 6 with residue, no till, flood, raised bed (R 1 T 3 I 1 P 2 ) 18 without residue, no till, flood, raised bed (R 2 T 3 I 1 P 2 ) 7 with residue, conventional, drip I, Flat (R 1 T 1 I 2 P 1 ) 19 without residue, conventional, drip, flat (R 2 T 1 I 2 P 1 ) 8 with residue, conventional, drip, raised bed (R 1 T 1 I 2 P 2 ) 20 without residue, conventional, drip, raised bed (R 1 T 1 I 2 P 2 ) 9 with residue, reduced, drip, flat (R 1 T 2 I 2 P 1 ) 21 without residue, reduced, drip, flat (R 2 T 2 I 2 P 1 ) 10 with residue, reduced, drip, raised bed (R 1 T 2 I 2 P 2 ) 22 without residue, reduced, drip, raised bed (R 2 T 2 I 2 P 2 ) 11 with residue, no till, drip, flat (R 1 T 3 I 2 P 1 ) 23 without residue, no till, drip, flat (R 2 T 3 I 2 P 1 ) 12 with residue, no till, drip, raised bed (R 1 T 3 I 2 P 2 ) 24 without residue, no till, drip, raised bed (R 2 T 3 I 2 P 2 ) R1 = with residue, R2 = without residue I1 = flood, I2 = drip, T1 = conventional tillage T2 = reduced tillage, T3 = no tillage, P1 = flat planting, P2 = raised bed planting Table 1 . 2.2 Measurement of CO 2 emission from the soil In this study, a portable environmental gas analyzer (CO 2 sensor) was used to measure CO 2 emission from the soil. Several chambers with a diameter of 20 cm and height of 25 cm were used to capture sample for gas analyzer. These chambers were placed upside down on the soil surface in a way that air could not enter or exit from outside the chambers. Two valves were installed in the chamber body, one for measuring temperature and humidity inside the chamber, and the other for connecting the chamber to the gas analyzer device. To ensure the accuracy of the measurements, the gas sensor was placed inside the chambers several times during the study, and the increase and emission of carbon dioxide were recorded. The chambers were transparent plastic containers (Fig. 1 ). Furthermore, during the study, the accuracy of the portable gas sensor was compared to gas chromatography by sampling from the chambers five times and measuring with both methods. The results of this comparison showed that the final values obtained from the environmental gas analyzer were on average about 27% higher than the gas chromatography values. krauss et al. (2017) and Yar Ahmadi et al. (2012) used stationary chambers and gas chromatography for gas measurements. Annachiara et al. (2017) used a stationary chamber connected to an environmental gas analyzer to measure greenhouse gases. Upendra et al. (2012) conducted a study to compare measurement methods of CO 2 emitted from the soil using a stationary chamber, gas chromatography, and a portable gas analyzer. They reported that all methods were validate and precise. The advantages of chamber and portable gas analyzer are being faster and easier measurements without the need for expensive laboratory equipment. However, on the other hand, the initial cost of purchasing portable gas analyzers is relatively high. CO 2 measurements were taken during 4 months of maize cultivation, 3 months of wheat cultivation, and 2 months during fallow time. In order to minimize errors, each measurement had 3 repeats and, the average was considered as the result. Before each sampling, the amount of CO 2 in the air of the field was measured, and the readings from each chamber were subtracted. The readings obtained from the device were in terms of volumetric ppm, which, considering the chamber temperature, the molar mass of CO 2 gas, and the volume of the chamber, were converted to the emission rate of the desired gas in terms of mass per unit area over time. Figure 1 illustrates the measurement of CO 2 emission from the soil. Figure 1 . 2.3 Measurement of CO 2 eq emission from the inputs In this part of the study, after preparing the project site and determining the relevant treatments for each system (treatment combination), the amount of input materials for each treatment was measured and recorded from the beginning to the end of the wheat and maize harvesting operation. The amount of the inputs include fuel, electricity, machinery and equipment, human labor, fertilizers, and pesticides that directly and indirectly produce CO 2 eq were determined and multiplied by CO 2 eq emission coefficients of each input. The fuel input for all land preparation, soil cultivation, planting, harvesting, and transportation operations was taken into consideration. In this study, since the required irrigation water is supplied through pumping from the river (the method used in most farms in the province) and electricity is used for pumping, the energy consumption in kWh and the amount of water obtained per cubic meter were calculated. Then, the water consumption for each treatment was measured using a water meter, and its electricity consumption was calculated in kWh. To determine the CO 2 eq emission from the machinery, first, the energy consumption equivalent per hectare was calculated, and then the equivalent CO 2 emission were considered. The majority of the energy consumed by machinery is related to the factories producing agricultural machinery and the operations related to their manufacturing. In this study, the energy consumption for machinery was calculated based on Erdal et al. (2007), which estimated the energy consumption for machinery as 62.7 MJ.h − 1 . The CO 2 eq emission from fuel, electricity, machinery, and other inputs were also calculated according to the coefficients in Table 2 . Table 2 CO 2 eq emission coefficients of the inputs Input Unit Equivalent CO 2 (kg CO 2 eq unit − 1 ) Fuel consumption liter 2.76 1 Electricity Kw h − 1 0.608 2 Machinery Mj 0.071 3 Fertilizers N P K Kg 1.3 4 0.2 4 0.2 2 Pesticides Kg 5.1 4 1 Erdal (2003) 2 Nabavi-Plesaraei (2014) 3 Dyer and Dejardins (2006) 4 Lal et al (2004) Table 2 . 2.4 Irrigation system To measure the volume of irrigation water and electricity used by pumping, a flow meter was installed on the outlet pipe of the water pump. To calculate the water requirement for each treatment, the proposed Eq. ( 1 ) by Alizadeh (2011) was used. $$V=\frac{({F}_{C}-{\theta }_{m})\times pb\times {D}_{root}\times A}{{E}_{I}}$$ 1 Where \(V\) is volume of irrigation water in cubic meters, \({F}_{C}\) is weighted moisture percentage at field capacity, \({\theta }_{m}\) is weighted moisture percentage before irrigation, pb is bulk density of soil in grams per cubic centimeter, \({D}_{root}\) is root development depth in meters, A is Irrigated area in square meters and \({E}_{I}\) Irrigation efficiency (The efficiency of floot and drip irrigation systems were considered as 50 and 85%, respectively), \(PWP\) is permanent wilting point, \(MAD\) is maximum allowable discharge and D stands for depth of root development. To determine the soil moisture before irrigation, a digital soil moisture sensor was used. 2.5 Yield measurement To determine the grain yield of wheat under different treatments, first, a few meters away from the plot edges, samples were taken from the quadrats. A wooden frame measuring one square meter was used for sampling, and three replicates were taken from each quadrat. Wheat plants within the frame were harvested using a sickle, and after threshing, the grains were carefully separated and weighed. The yield was then extrapolated to per hectare. For calculating the yield and yield components of corn, samples were taken and measured from the middle rows of each quadrat (the fourth and fifth rows) after passing through the plot edges. 2.6 Statistical analyses Data was analyzed Using MSTAC statistical software. Considering that the design used in this study (split-plot strip factorial design with complete random blocks) includes the main plot, sub-plot and sub-sub-plot (two factors) There are three factors of error in this plan. This means that there are three different errors. The linear statistical model for this experimental design is shown in Eq. ( 2 ). Significance was calculated based on F-tests and Duncan's multiple range test at the 0.01 and 0.05 probability levels. $${Y}_{hijkl}=\mu +{r}_{h}+{a}_{i}+{\delta }_{hi}+{b}_{j}+{ab}_{ij}+{\gamma }_{hij}+{c}_{k}+{ac}_{ik}+{bc}_{jk}+{abc}_{ijk}+{d}_{l}+{ad}_{il}+{bd}_{jl}+{abd}_{ijl}+{abcd}_{ijkl}+{\epsilon }_{hijkl}$$ 2 Where \({Y}_{hijkl}\) is the response (measurement) for h \(hijkl\) th observation, \(\mu\) stands for general mean effect, \({r}_{h}\) is the effect of \(h\) th block, \({a}_{i}\) is the effect of \(i\) th level of residue factor (main factor), \({\delta }_{hi}\) stands for the main plot error, \({b}_{j}\) is the effect of \(j\) th level of irrigation factor (subplot factor), \({ab}_{ij}\) is the interaction effect of \(i\) th level of residue factor and \(j\) th level of irrigation factor, \({\gamma }_{hij}\) is the subplot error, \({c}_{k}\) is the effect of \(k\) th level of tillage factor (sub-subplot factor), \({ac}_{ik}\) is the interaction effect of \(i\) th level of residue factor and \(k\) th level of tillage factor, \({bc}_{jk}\) stands for the effect of the interaction effect of \(j\) th level of irrigation factor and \(k\) th level of tillage factor, \({abc}_{ijk}\) is the interaction of the interaction effect of \(i\) th level of residue factor, \(j\) th level of irrigation factor and \(k\) th level of tillage factor factors, \({d}_{l}\) is the effect of \(l\) th level of planting patterns factor (sub-subplot factor), \({ad}_{ik}\) is the interaction effect of \(i\) th level of residue factor and \(l\) th level of planting patterns factor, \({bd}_{jl}\) stands for the effect of the interaction effect of \(j\) th level of irrigation factor and \(l\) th level of planting patterns factor, \({abd}_{ijl}\) is the interaction of the interaction effect of \(i\) th level of residue factor, \(j\) th level of irrigation factor and \(l\) th level of planting patterns factor factors, \({abcd}_{ijkl}\) is the interaction of the interaction effect of \(i\) th level of residue factor, \(j\) th level of irrigation factor, \(k\) th level of tillage factor factors and \(l\) th level of planting patterns factor factors and \({\epsilon }_{hijkd}\) stands for the sub-subplot error 3 Results and discussion 3.1 Results of CO 2 emission from the soil The CO 2 emission from the soil were measured during wheat-maize rotation in different systems. Statistical analysis was performed to identify the factors with significant effect on the CO 2 emission from the soil. The ANOVA results for CO 2 emission, wheat and maize yields is shown in Table 3 . The results of this study indicated that the amounts of CO 2 emission from the soil in different systems were significantly different. The ANOWA results showed that residue management, irrigation method, and tillage had a significant impact on CO 2 emission, while the planting methods had no significant effect. The results also showed that among all the interaction effects, only the interaction of irrigation and tillage had significant effect on CO 2 emission. On average, the treatments with residues had 28% lower CO 2 emission per ha per day (equivalent to 1.73 kg/ha/day) compared to the treatments without residue. The irrigation method also had a significant effect on CO 2 emission. Table 3 The ANOVA results for CO2 emission and wheat and maize yield. Resource df CO 2 wheat maize Replication 2 0.21 ns 77322 ns 392626 ns Residue 1 52.01 ** 1545282 ** 15469776 ** Error 2 0.13 125896 1634158 Irrigation method 1 14.85 ** 7601100 ** 958322783 ** Interaction of residue and irrigation method 1 2.52 ns 556 ns 4408648 * Error 4 0.085 50030 398101 Tillage 2 19.59 ** 344157 ns 10858073 ** Interaction of residue and tillage 2 0.342 ns 344780 ns 2078900 ns Interaction of irrigation and tillage 2 7. 56 ** 215040 ** 1704898 ns Interaction of residue irrigation and tillage 2 0.007 ns 143073 ns 517024 ns Planting method 1 0.002 ns 3991254 ** 25532658 ** Interaction of residue and Planting method 1 0.025 ns 416167 ns 38364 ns Interaction of irrigation and Planting method 1 0.085 ns 158296 ns 4591460 ns Interaction of residue, irrigation and Planting method 2 0.216 ns 7160 ns 531137 ns Interaction of tillage and Planting method 2 0.15 ns 37606 ns 17235227 ns Interaction of residue, tillage and Planting method 2 0.66 ns 46005 ns 445278 ns Interaction of irrigation, tillage and Planting method 2 0.045 ns 76577 ns 4538295* Interaction of residue, irrigation, tillage and Planting 2 0.169 ns 31533 ns 573382 ns Error 40 0.117 138291 490917 Total 71 CV 6.75 9.53 13.81 Table 3 . Kallenbach et al. (2010) considered water management to be influential in the biological, chemical, and physical properties of the soil, which can affect the amount of CO 2 emission. In this study, drip irrigation had an average of 16% lower CO 2 emission per ha per day (equivalent to 0.92 kg/ha/day) compared to flood irrigation. This reduction in the emission is due to the lower volume of water entering the soil in drip irrigation compared to flood irrigation. In flood irrigation, a large amount of water enters the soil pores, leading to disturbance in the balance of soil moisture and air inside the pores. According to Chounian et al. (2008), the respiration of CO 2 in the ecosystem is highly sensitive to soil moisture during the plant growth period. Therefore, the higher amount of water in irrigation leads to an increase in CO 2 emission. Buragiene et al. (2019), suggested that CO 2 emission from soil had a positive linear correlation with soil moisture content. In another study, it was reported that CO 2 emission increases due to increase in organic matter oxidation with increased soil moisture (Jabro et al, 2008). Many studies such as Yerli et al. (2022), Zornoza et al. (2016), Sinaie et al. (2019) and Zhong et al. (2021) expressed that deficit irrigation reduces CO 2 emissions from soil. However, Fresno et al. (2022), reported that regulated deficit irrigation in maize using sprinkler irrigation did not reduce CO 2 emission. Also, the percentage of reduction of CO 2 emission from soil under deficit irrigation methods is usually different for each study. Reduced tillage and no tillage reduced CO 2 emission by 5.32 kg/ha/day (equivalent to 6.5%) and 1.14 kg/ha/day (equivalent to 20%) compared to conventional tillage, respectively. In a study on silage maize, it was reported that no tillage reduced CO 2 emissions of silage maize and fresh silage yield 5.1 and 26.1%, respectively (Yerli et al, 2022). It is assumed that conservative tillage systems through minimizing soil disturbance or the absence of soil disturbance in no tillage decrease microbial decomposition of organic matter and as a result reduce CO2 emission (Chaplot et al, 2015). Yerli et al. (2022), Buragiene et al. (2019) and Nyambo et al. (2020), also reported that intensive tillage lead to more CO 2 emission from soil compared to reduced tillage or no tillage. The decomposition of organic matter and carbon release is an aerobic process in which oxygen increases the activity of microbes that feed on organic matter. One of the soil properties that has significant effect on CO 2 emission from residue is soil pH (Yerli et al, 2022). It is reported that the lowest and highest CO 2 emission occurs in strongly acidic soils and slightly acidic soils (Ntonta et al, 2022). It is well known that the decomposition of organic substances increases soil acidity (Vaseghi et al, 2005). Under intensive tillage, the O 2 level of soil increase and suitable environment is provided for microbial activities, thus CO 2 emission increase (Yerli et al, 2022). The results of daily CO 2 emission from levels of the factors are shown in Fig. 2 . Figure 2 . According to the results in Table 2 , the interaction of irrigation and tillage had significant effect on CO 2 emission from the soil. The interaction effect of irrigation and tillage is shown in Fig. 3 . The highest emissions were observed in the treatments with flood irrigation and conventional tillage, with an average of 6.33 kg/ha/day, while the lowest emissions belonged to the treatments with drip irrigation and no-tillage, with an average of 4.2 kg/ha/day. As shown in the Fig. 3 , reduced tillage had no significant effect on CO2 emission compared to conventional tillage, but no tillage reduced the emission significantly in both flood and drip irrigation. Page et al. (2012) reported a reduction in CO2 emission with conservative tillage. Silva-olaya et al. (2013) also reported that conservative tillage had a significant reduction in CO2 emission compared to conventional tillage. Among the different systems, the highest CO2 emission from the soil were observed in the residue- conventional tillage- flood irrigation- and flat planting (R 1 T 1 I 1 P 1 ), with a rate of 7.35 kg/ha/day, while the lowest emission belonged to without residue- drip irrigation- no-tillage- raised bed planting (R 2 T 3 I 2 P 2 ), with a rate of 3.41 kg/ha/day. Figure 3 . In the Fig. 4 , the CO 2 emission from the soil in different months of the year is presented for four systems. The measurements of carbon dioxide emission in this study were conducted for maize cultivation, which took place on August 28, 2018, after the previous year's wheat harvest, and immediately after maize harvest, on December 15, 2018 Xu and Qi (2001) reported a significant correlation between the amount of CO 2 emission and factors such as soil temperature, soil moisture content, or their interaction. Lu et al. (2015) also reported a change of 76 to 96 percent in CO 2 output from the soil under the influence of temperature and soil moisture variations. In this study, significant changes in the amount of CO 2 emission from the soil were observed with the change of seasons and the growth stages of the plants. As shown in the (16 − 4) graph, there was an increase in CO 2 emission for all systems in September for maize cultivation and in December for wheat cultivation. In the second half of September, with the high growth of maize and the intense heat of the Khuzestan region, the amount of carbon dioxide released from the soil reached its peak. In the conventional tillage system with residual presence, flood irrigation, and flat cultivation, the emission reached 15.29 kg per day per hectare. However, in a similar system with a change in tillage method from conventional to no-till, the emission was reduced to 12.13 kg per day per hectare. Among the conservation systems, the system with residue, drip irrigation, no-till, and flat planting had the highest CO 2 emission of 9.61 kg per day per ha during the same period, indicating CO 2 emission can be reduced significantly by using conservation tillage and pressurized irrigation. In wheat production, due to the higher growth rate and soil moisture content resulted from irrigation and rainfall, CO 2 emission reached its peak in December. In March and April, due to the cold weather and less irrigation, and in May, due to land drying, the emission was at its lowest level. However, the difference in CO 2 emission among different systems during the growing season was significant, and systems with no-tillage practices had the lowest emission. Figure 4 . 3.2 The results of CO 2 eq emission from the inputs In Fig. 5 , the contribution of different inputs to CO 2 eq emission in the systems is shown. In this study, electricity, fertilizers, and fuel had the highest share among the inputs in CO 2 eq emission. In a study by Khoshnavisan et al. (2013), on the emissions of greenhouse gases in wheat production in Isfahan province, electricity and chemical fertilizers had the highest contribution. In another study, fuel and chemical fertilizers had the highest share in CO 2 eq emissions in maize production (Pishkar et al, 2011). In the current study, the contribution of chemical fertilizers and pesticides was the same in all treatments, at 760 kg and 20.4 kg CO 2 eq, respectively. However, the emissions resulting from machinery inputs, fuel, and electricity (for pumping irrigation water) varied among different systems. The highest emission for machinery and fuel inputs were observed in the system 2, with 87.43 kg and 488.46 kg CO 2 eq, respectively. The lowest emissions were observed in the system number 23, with 39.21 kg and 247.24 kg CO 2 eq. These differences are due to differences in tillage and planting operations, as no-tillage removed heavy plowing, disking, and residue incorporation. The energy required for irrigation had the highest share in energy consumption and CO 2 eq emission among the inputs in wheat-maize rotation. The highest emission was observed in the system 13, with 3537.06 kg CO 2 eq. In this system, due to frequent heavy flood irrigation, the absence of residue, and conventional tillage, the soil quickly loses its moisture and requires more frequent irrigation. Among conservation systems with flood irrigation, the system 6 had an 1855.17 kg CO 2 eq emission due to the electricity used for pumping irrigation water. Figure 5 . The findings of this study indicated that the inputs had a significant difference in their contribution to the CO 2 eq emission in the different systems. The CO 2 eq emission from the inputs for wheat ranged from 1064 to 1293 kg CO 2 eq per hectare, and for maize, it ranged from 1505 to 2325 kg CO 2 eq per hectare. Based on the yield, CO 2 eq for wheat calculated in a range of 258 to 365 kg per ton, and for maize, it ranged from 140 to 313 kg per ton. Rajabi et al. (2012), reported an average CO 2 eq emission of 8.103 to 5.271 kg per ton of wheat produced in Gorgan province. One of the main reasons for these differences is the higher wheat yield in Gorgan province. Figure 6 illustrates the impact of irrigation methods on the CO2 eq emission from the inputs under tillage systems and planting methods. Reduced tillage and no tillage reduced CO 2 eq emission compared to conventional tillage due to less field operations. In fact, with the change from conventional tillage to no-tillage, the emission decreased from 3534 kg to 3027 kg CO 2 eq. Changing the irrigation method from flood irrigation to drip irrigation also resulted in a reduction in the CO 2 eq emission in all treatments. Most agricultural lands in Khuzestan province are irrigated through pumping water from rivers or wells, which requires a significant amount of the energy. Drip irrigation, due to its high irrigation efficiency, reduced irrigation water requirements by more than 50% in all treatments, led to a reduction in energy consumption. Figure 6. 3.3 The total CO 2 emission and wheat and maize yields Figure 7 shows the total CO 2 emission from the inputs and the soil in different systems. As it is shown, R 1 T 1 I 1 P 1 (system 1) had the highest total emission with 6129.47 kg.ha − 1 per year. The minimum CO 2 emission occurred in the system of R 2 T 3 I 2 P 2 (system 24) with 3900 kg.ha − 1 . By changing conventional tillage and flood irrigation to no tillage and drip irrigation, the system of R 1 T 3 I 2 P 1 had an emission of 4471.28 kg.ha − 1 per year (system 11). One of the reasons for the reduction in this emission can be attributed to the lower fuel consumption resulting from the reduction in the number of required soil cultivation and planting operations. One of the other reason can be attributed to reduction in CO 2 emission from the soil under no tillage and drip irrigation. The CO 2 emission from the soil increased due to keeping residue on surface, but the CO 2 eq emission from the inputs reduced. However, the total CO 2 emission increased due to keeping residue. Figure 7. In Fig. 8 , wheat and maize grain yields in in different systems is shown. Both wheat and maize had the highest yield in the system 8 (R 1 T 1 I 2 P 2 ) with 4.74 and 11.87 ton.ha − 1 , respectively. As it can be seen, first 12 systems (with residue) had higher yield in both wheat and maize production compared to systems 13 to 24 (burned residue). Residue keeps water available to plant for a longer time and it would improve crop yield especially in semi-arid and arid areas. No tillage and reduced tillage reduced yield in both wheat and maize production, but it expected that in long term, conservation tillage improve the yield. In the short term, it is not possible to increase earthworm populations and improve soil structure, and only lower disturbance to the soil is main difference. Drip irrigation significantly increased wheat and maize grain yields. In a study in Khuzestan province on wheat, Farahani et al. (2020), also reported that pressurized irrigation (sprinkler irrigation) increased wheat grain yield compared to flood irrigation. Figure 8 . The results showed that changing the irrigation method from flood to drip irrigation and reducing the intensity of soil tillage significantly reduced the CO 2 eq emission from the inputs due to reduction in the fuel and electricity consumption. The study also revealed that the CO 2 emission from the soil varied significantly in different months, with the highest emission occurring in September for maize and in December for wheat. In September, the high growth rate and development of maize and intense heat increased the emission of CO 2 from the soil. However, in the same period, the change in soil tillage from conventional to no tillage and a change in irrigation from flood to drip irrigation, decreased the CO 2 emission from 15.29 kg per day per ha to 9.61 kg per day per ha. Similarly, in December, with the same changes in wheat production, the emission decreased from 11.6 kg per day per ha to 10.49 kg per day per ha, indicating the significant impact of conservation agriculture practices and pressurized irrigation in reducing the CO 2 emission from the soil. In general, the study showed that residue management, irrigation method, and tillage system had a significant effect on CO 2 emission, while planting method had no significant effect. The residue increased the CO 2 emission from the soil, but reduced the CO2 eq emission from the inputs. Totally, the treatments without residue had 28% less CO 2 emission compared to the treatments with residue. Drip irrigation resulted in an average reduction of 16% in CO 2 emission compared to flood irrigation. In flood irrigation, a large volume of water enters the soil pores, causing imbalances in soil moisture and air, which leads to an increase in CO 2 emission from the soil. Changing tillage method from conventional to reduced tillage and no tillage resulted in a reduction of 5.6% and 20% in CO 2 emission, respectively. The interaction between irrigation and tillage also had a significant effect on CO 2 emission from the soil. The highest emissions were observed in flood irrigation and conventional tillage treatments with an average of 6.33 kg of CO 2 emission per ha per year, while the lowest emissions belonged to drip irrigation and no tillage treatments with an average of 4.2 kg of CO 2 emission per ha per year. 4 Conclusion The results showed that electricity, fertilizers, and fuel had the highest contribution to CO 2 eq emission from agricultural inputs. The emission from the inputs per ton of wheat production ranged from 258 to 365 kg CO 2 eq, and for maize, it ranged from 140 to 313 kg of CO 2 eq, depending on the different systems. The amount of CO2 emission from the soil also varied from 3900 to 6219 kg per ha per year. One of the most important practices for conservation agriculture is residue management. Keeping residue on the field has benefits such as reducing water consumption, increasing soil organic matter, and reducing compaction, ultimately leading to an increase in productivity. In this study, however, the total CO 2 emission increased due to keeping residue. Although, it should be noted that in Iran, residue is usually removed by burning on the field, and by considering the emissions from residue burning and the benefits of residue, it is suggested to keep residue on the field. Considering the numerous advantages of conservative tillage and drip irrigation, especially in Iran's dry and semi-dry conditions, this study recommends systems 10 and 12, which are considered conservation systems that have relatively lower emissions from the inputs and the soil compared to other systems. Declarations Ethics approval 'Not applicable' Consent to Participate 'Not applicable' Consent to Publish 'Not applicable' Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Eisa Bougari], [Mohammad Amin Asoodar], [Afshin Marzban] and [Navab Kazemi]. The first draft of the manuscript was written by [Eisa Bougari] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript Funding The Agricultural Sciences and Natural Resources University of Khuzestan supported this work . This research is derived from the doctoral thesis of the first author of the article, which was conducted at this university. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Non-financial interests : none. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3684936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265782498,"identity":"0aeb8b23-7a83-44fb-9e1a-f03619aa51ab","order_by":0,"name":"Eisa bougari","email":"","orcid":"","institution":"Agricultural Sciences and Natural Resources University of Khuzestan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eisa","middleName":"","lastName":"bougari","suffix":""},{"id":265782499,"identity":"1ef7e1e1-2617-4e60-ba00-9d835033a975","order_by":1,"name":"Mohammad Amin 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05:18:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3684936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3684936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49363924,"identity":"2bcbf31b-50ff-4e42-a687-ab377da94a4c","added_by":"auto","created_at":"2024-01-09 11:03:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1518905,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil, A: placing the box on the plot, B: placing the box between the maize rows, C: reading outside the box through the built-in taps (CO\u003csub\u003e2\u003c/sub\u003e emission, humidity and temperature), D: reading from inside the box.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/15716c47824c559cc0083e78.png"},{"id":49363922,"identity":"8ea1b950-a530-4e08-ae52-f1e4e71518c2","added_by":"auto","created_at":"2024-01-09 11:03:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38685,"visible":true,"origin":"","legend":"\u003cp\u003eComparing average daily CO\u003csub\u003e2\u003c/sub\u003e emission from the soil in levels of the factors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/a4e16187d3684feda42150b0.png"},{"id":49363468,"identity":"20516b7a-070d-4f5a-a02a-088ebff1e050","added_by":"auto","created_at":"2024-01-09 10:55:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17832,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect of irrigation and tillage on CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/a7923ff27112d5367bd41be3.png"},{"id":49364162,"identity":"adff2a8e-4d6d-4785-a6a8-07e355713366","added_by":"auto","created_at":"2024-01-09 11:11:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54182,"visible":true,"origin":"","legend":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emission from the soil in different months of the year in the four systems\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/a16ba7a8b379d620fd52164d.png"},{"id":49363471,"identity":"0cc5be23-f0cc-4e41-99a3-d67f5aafbc28","added_by":"auto","created_at":"2024-01-09 10:55:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33517,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of the inputs to CO\u003csub\u003e2\u003c/sub\u003e eq emission in the different systems\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/5f774b1f1099570b51c5b3cb.png"},{"id":49363925,"identity":"648c19d1-9463-494b-9a81-4323a7334752","added_by":"auto","created_at":"2024-01-09 11:03:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32597,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of irrigation methods on the CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs under tillage systems and planting methods.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/681b86133916ba162f39f1dd.png"},{"id":49363473,"identity":"6f4b4612-e287-4a61-8fd4-f62d79204a33","added_by":"auto","created_at":"2024-01-09 10:55:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":40960,"visible":true,"origin":"","legend":"\u003cp\u003eTotal CO\u003csub\u003e2\u003c/sub\u003e emission from the inputs and soil in different systems.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/7381dcb76de5dc682c6dbaa7.png"},{"id":49363475,"identity":"e27e407e-5ee9-4056-ae53-77cf5df7cf27","added_by":"auto","created_at":"2024-01-09 10:55:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":36051,"visible":true,"origin":"","legend":"\u003cp\u003eWheat and maize grain yields in different systems\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/4d8a9eff4d84f2c2057c2438.png"},{"id":56358539,"identity":"d63e675c-51ee-4ed5-8720-0cb2bb7ea84e","added_by":"auto","created_at":"2024-05-13 06:58:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2392201,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3684936/v1/eb877a25-52a4-4a3e-96e5-2311c41e0f69.pdf"}],"financialInterests":"","formattedTitle":"Investigation of CO2 emission from soil and farm inputs in different farming systems in wheat-maize rotation","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAgriculture accounts for approximately 14% of greenhouse gas emissions, which has a high potential for reduction (Cichorowski et al, 2015). The adoption of conservation agriculture aims to promote a more sustainable and environmental friendly management system compared to conventional agriculture (Habbs et al, 2008). Crop rotation and soil conservation are principles of conservation agriculture, which have shown 18.9% reduction in carbon emissions in crop rotation compared to monoculture systems, and 14.7% reduction in no-till system compared to conventional tillage (Yin et al, 2017). Cultivation as a crucial agricultural management technique impacts the dispersion and conversion of soil organic carbon (SOC), stability of soil particles, water retention capacity, and soil temperature by regulating pathways for air and water exchange between the soil surface and the atmosphere (Bregaglio et al, 2022). So selecting proper cultivation system is essential to meet sustainability goals and farm productivity.\u003c/p\u003e \u003cp\u003eProper selection of agricultural operations can lead to a decrease in pollutant gases. Soil management is one of the most influential operations affecting carbon storage and carbon dioxide emissions in agricultural systems (Page et al, 2014). Compared to soilless agriculture, soil-based agriculture has a 58% higher CO\u003csub\u003e2\u003c/sub\u003e emissions. CO\u003csub\u003e2\u003c/sub\u003e emission from soil have a direct relationship with soil and air temperature, and intensive soil tillage, heavy rainfall, or extra irrigation increase CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. In a study on alfalfa and wheat crops in North Dakota, irrigation and intensive tillage have resulted in a 27% and 58% increase in CO\u003csub\u003e2\u003c/sub\u003e emission compared to non-irrigated and soilless conditions, respectively (Sainju et al, 2006). Irrigation and soil surface cover also potentially affect carbon and nitrogen cycling, and as a results affect the emissions of CO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e gases from the soil. Kallenbach et al. (2010), demonstrated that soil covered with residues had 40% and 15% higher CO\u003csub\u003e2\u003c/sub\u003e emission compared to bare soil under surface and drip irrigation methods, respectively. In covered soil with residues, CO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e emissions increased by 425 milligrams and 60 micrograms per square meter per hour compared to bare soil, respectively. Fossil fuels are also significant contributors to emission of greenhouse gases from the farm, and their consumption is directly related to the number of operations carried out on the farm. Therefore, by managing these operations, fuel and other related inputs reduce and as a results greenhouse gas emissions decrease. Another important factor in the emission of greenhouse gases is the way in which agricultural residues is managed. Burning residues as a managing way releases particulate matter and pollutants into the air. In many developing countries, burning residues is a common practice, and in Iran, unfortunately it is a common way to manage the residues for many farmers. Burning crop residues significantly increases the emission of fine particles in the atmosphere, affecting the air quality in the surrounding areas of the fields and even distant locations. In this regard, a study by Dhammapala et al. (2006), in eastern Washington and northern Idaho reported that 4% of particles smaller than 2.5 microns and 34.5% of carbon emissions were related to burning residues in the agricultural sector. In Iran, there are also numerous cases of burned farmlands, for example in the Khuzestan province as one of the most important province of crops productions, approximately crop residues of 195,000 ha agricultural lands were set on fire in 2019 (Bogari et al, 2023). Therefore, recommending a production pattern that reduces environmental pollutants is essential for sustainable agriculture, and this requires replacing current production methods with new patterns and practices in agriculture.\u003c/p\u003e \u003cp\u003eThe impact of different land uses in agriculture on the emission of greenhouse gases was investigated using a closed chamber and gas chromatography method (Mahdipour and Landi, 2010). In this study, CO\u003csub\u003e2\u003c/sub\u003e emission from wheat, canola, citrus orchards, and fallow were estimated as 4.47, 3.72, 3.38, and 1.89 t/ha/y, respectively. Dong et al. (2017), examined the effect of tillage on short-term and long-term CO\u003csub\u003e2\u003c/sub\u003e emission in dry regions of China. In this study, three tillage treatments including conventional tillage with a moldboard plow, no tillage with wheat straw mulching, and no tillage without crop residues were studied for their impact on CO\u003csub\u003e2\u003c/sub\u003e emission. Their results showed that immediately after tillage and up to 48 hours, the CO\u003csub\u003e2\u003c/sub\u003e emissions from conventional tillage were significantly higher, but the annual emissions from the wheat straw mulching treatment were higher than the other two tillage methods. The highest emission were estimated as 0.2 grams per square meter per hour in winter, while the lowest emission were 0.04 grams per square meter per hour in summer. CO\u003csub\u003e2\u003c/sub\u003e emission from soil is an important part of the terrestrial ecosystem cycle. Tillage is an influential operation in carbon storage and CO\u003csub\u003e2\u003c/sub\u003e emission in agricultural ecosystems (Page et al, 2014). Increasing concerns about global warming and changes in soil management in agriculture can have significant effects on CO\u003csub\u003e2\u003c/sub\u003e emissions (Sheehy et al, 2015). Research has shown varied results on the impact of tillage on CO\u003csub\u003e2\u003c/sub\u003e emissions, with some studies reporting lower levels (Shahidi et al, 2014) and others indicating higher emissions (Fuetes et al, 2012), while some studies have reported that tillage had no significant effect on gases emission (Aslam et al, 2000).\u003c/p\u003e \u003cp\u003eThe effect of soil managements on CO\u003csub\u003e2\u003c/sub\u003e emission from soil depends on factors such as soil temperature, soil moisture content, or the interaction between these two factors (Lu et al, 2015). Research suggests that soil temperature and moisture can cause variations of 76 to 96 percent in CO\u003csub\u003e2\u003c/sub\u003e emission from soil (Xu and Qi, 2001). The release of NO\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e from agricultural soils is a result of complex interactions between water, biological, chemical, and physical properties of the soil (Oorts et al, 2007). The agricultural soils in Khuzestan province are often characterized by poor organic matter content and low stability, which can lead to soil structure degradation with excessive flooding irrigation. Implementing drip irrigation with reduced speed and quantity of water can help maintain soil stability (Bougari et al, 2021). Water management can influence these mentioned properties and have an impact on the emission of greenhouse gases (Kallenbach et al, 2010). An increase in greenhouse gas emissions can occur during irrigation hours and several hours after irrigation (Khalil and Bags, 2005).\u003c/p\u003e \u003cp\u003eSoil management practices have the potential to reduce CO\u003csub\u003e2\u003c/sub\u003e emission from the soil (Mangalassery et al, 2014 and Abdalla et al,. 2016). In a study, CO\u003csub\u003e2\u003c/sub\u003e emission were examined in different farming systems in sugarcane cultivation, including conventional tillage, minimum tillage, and reduced tillage with residue retention. The highest CO\u003csub\u003e2\u003c/sub\u003e emission was observed in conventional tillage, followed by minimum tillage and reduced tillage, with values of 350, 51.7, and 5.5 grams per square meter, respectively. CO\u003csub\u003e2\u003c/sub\u003e emission in conventional tillage can lead to up to an 80% reduction in soil carbon, emphasizing the need for conservative tillage (Silva-olaya et al, 2013). Wei et al. (2018), investigated the effects of surface and subsurface irrigation on the potential for global warming through the emissions of NO\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e in a laboratory-scale study using soil that had been prepared after tomato harvest. The results of this study showed that the emissions of NO\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e in surface irrigation were 28.9% and 19.4% lower, respectively, compared to subsurface irrigation at 72 and 168 hours after irrigation. In a study conducted in China, it was found that no-tillage resulted in less \u003csub\u003eCO2\u003c/sub\u003e emission compared to conventional tillage, while the use of residue mulching in each tillage led to an increase in the emission (Yao et al,. 2023). In general, sustainable agriculture has the potential to reduce greenhouse gas emissions, without a bad side effect on yield (Zhong et al,. 2022). However, no study has been conducted to examine the effects of tillage, irrigation, residue, planting and their interactions on the emission of CO\u003csub\u003e2\u003c/sub\u003e from the soil and CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs in wheat-maize rotation. Therefore, this study was carried out to examine CO\u003csub\u003e2\u003c/sub\u003e emission from the soil, CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs and the total CO\u003csub\u003e2\u003c/sub\u003e emission in different farming systems, aiming to suggest the best system and estimate effect of each change and their interactions.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Site description and experimental design\u003c/h2\u003e \u003cp\u003eThis study was conducted on a farm at Agricultural Sciences and Natural Resources University of Khuzestan (31\u0026deg; 58' N, 48\u0026deg; 35' E) located at north of Ahwaz county during 2018\u0026ndash;2019 (autumn of 2018 to spring of 2019). The experimental field was under fallow for one year prior to our experiment. The soil has a clay loam texture (44.5% silt, 36% clay and 19.5% sand) same as most of the region. The experiment was conducted in the form of a split-plot strip factorial design with complete random blocks and three replications. Each replication consisted of 24 treatment combinations, where each treatment combination represented a crop management system. In this design, the main plots were assigned to residue management (including residue retention and residue removal), the subplots were assigned to irrigation methods (including flood irrigation and drip irrigation), and the sub-subplots were assigned to tillage methods (including conventional tillage, reduced tillage, and no-tillage) and planting patterns (including flat planting and raised bed planting). The size of main plot, subplot and sub-subplot were 94\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e15 m, 46\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e32 m and 6\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e15 m, respectively. The distance around the plots was considered to be two meters. This design was implemented for wheat-maize rotation (common rotation in Khuzestan province). The description of the systems studied in this research is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe studied systems in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edescription of the systems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edescription of the systems\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, conventional, flood, flat (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, conventional, flood, flat (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, conventional, flood, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, conventional, flood, raised bed (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, reduced, flood, flat (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, reduced, flood, flat (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, reduced, flood I, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, reduced, flood, raised bed (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, no till, flood, flat (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, no till, flood, flat (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, no till, flood, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, no till, flood, raised bed (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, conventional, drip I, Flat (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, conventional, drip, flat (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, conventional, drip, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, conventional, drip, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, reduced, drip, flat (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, reduced, drip, flat (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, reduced, drip, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, reduced, drip, raised bed (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e2\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, no till, drip, flat (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, no till, drip, flat (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewith residue, no till, drip, raised bed (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout residue, no till, drip, raised bed (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eR1\u0026thinsp;=\u0026thinsp;with residue, R2\u0026thinsp;=\u0026thinsp;without residue I1\u0026thinsp;=\u0026thinsp;flood, I2\u0026thinsp;=\u0026thinsp;drip, T1\u0026thinsp;=\u0026thinsp;conventional tillage T2\u0026thinsp;=\u0026thinsp;reduced tillage, T3\u0026thinsp;=\u0026thinsp;no tillage, P1\u0026thinsp;=\u0026thinsp;flat planting, P2\u0026thinsp;=\u0026thinsp;raised bed planting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil\u003c/h2\u003e \u003cp\u003eIn this study, a portable environmental gas analyzer (CO\u003csub\u003e2\u003c/sub\u003e sensor) was used to measure CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. Several chambers with a diameter of 20 cm and height of 25 cm were used to capture sample for gas analyzer. These chambers were placed upside down on the soil surface in a way that air could not enter or exit from outside the chambers. Two valves were installed in the chamber body, one for measuring temperature and humidity inside the chamber, and the other for connecting the chamber to the gas analyzer device. To ensure the accuracy of the measurements, the gas sensor was placed inside the chambers several times during the study, and the increase and emission of carbon dioxide were recorded. The chambers were transparent plastic containers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, during the study, the accuracy of the portable gas sensor was compared to gas chromatography by sampling from the chambers five times and measuring with both methods. The results of this comparison showed that the final values obtained from the environmental gas analyzer were on average about 27% higher than the gas chromatography values. krauss et al. (2017) and Yar Ahmadi et al. (2012) used stationary chambers and gas chromatography for gas measurements. Annachiara et al. (2017) used a stationary chamber connected to an environmental gas analyzer to measure greenhouse gases. Upendra et al. (2012) conducted a study to compare measurement methods of CO\u003csub\u003e2\u003c/sub\u003e emitted from the soil using a stationary chamber, gas chromatography, and a portable gas analyzer. They reported that all methods were validate and precise. The advantages of chamber and portable gas analyzer are being faster and easier measurements without the need for expensive laboratory equipment. However, on the other hand, the initial cost of purchasing portable gas analyzers is relatively high.\u003c/p\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e measurements were taken during 4 months of maize cultivation, 3 months of wheat cultivation, and 2 months during fallow time. In order to minimize errors, each measurement had 3 repeats and, the average was considered as the result. Before each sampling, the amount of CO\u003csub\u003e2\u003c/sub\u003e in the air of the field was measured, and the readings from each chamber were subtracted. The readings obtained from the device were in terms of volumetric ppm, which, considering the chamber temperature, the molar mass of CO\u003csub\u003e2\u003c/sub\u003e gas, and the volume of the chamber, were converted to the emission rate of the desired gas in terms of mass per unit area over time. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the measurement of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs\u003c/h2\u003e \u003cp\u003eIn this part of the study, after preparing the project site and determining the relevant treatments for each system (treatment combination), the amount of input materials for each treatment was measured and recorded from the beginning to the end of the wheat and maize harvesting operation. The amount of the inputs include fuel, electricity, machinery and equipment, human labor, fertilizers, and pesticides that directly and indirectly produce CO\u003csub\u003e2\u003c/sub\u003e eq were determined and multiplied by CO\u003csub\u003e2\u003c/sub\u003e eq emission coefficients of each input. The fuel input for all land preparation, soil cultivation, planting, harvesting, and transportation operations was taken into consideration. In this study, since the required irrigation water is supplied through pumping from the river (the method used in most farms in the province) and electricity is used for pumping, the energy consumption in kWh and the amount of water obtained per cubic meter were calculated. Then, the water consumption for each treatment was measured using a water meter, and its electricity consumption was calculated in kWh. To determine the CO\u003csub\u003e2\u003c/sub\u003e eq emission from the machinery, first, the energy consumption equivalent per hectare was calculated, and then the equivalent CO\u003csub\u003e2\u003c/sub\u003e emission were considered. The majority of the energy consumed by machinery is related to the factories producing agricultural machinery and the operations related to their manufacturing. In this study, the energy consumption for machinery was calculated based on Erdal et al. (2007), which estimated the energy consumption for machinery as 62.7 MJ.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The CO\u003csub\u003e2\u003c/sub\u003e eq emission from fuel, electricity, machinery, and other inputs were also calculated according to the coefficients in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e eq emission coefficients of the inputs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquivalent CO\u003csub\u003e2\u003c/sub\u003e (kg CO\u003csup\u003e2\u003c/sup\u003e eq unit\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuel consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eliter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKw h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.608\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMachinery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizers\u003c/p\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e0.2\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e0.2\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003e Erdal (2003)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e2\u003c/sup\u003e Nabavi-Plesaraei (2014)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e3\u003c/sup\u003e Dyer and Dejardins (2006)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e4\u003c/sup\u003e Lal et al (2004)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Irrigation system\u003c/h2\u003e \u003cp\u003eTo measure the volume of irrigation water and electricity used by pumping, a flow meter was installed on the outlet pipe of the water pump. To calculate the water requirement for each treatment, the proposed Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) by Alizadeh (2011) was used.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$V=\\frac{({F}_{C}-{\\theta }_{m})\\times pb\\times {D}_{root}\\times A}{{E}_{I}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(V\\)\u003c/span\u003e\u003c/span\u003e is volume of irrigation water in cubic meters, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({F}_{C}\\)\u003c/span\u003e\u003c/span\u003e is weighted moisture percentage at field capacity,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{m}\\)\u003c/span\u003e\u003c/span\u003e is weighted moisture percentage before irrigation, pb is bulk density of soil in grams per cubic centimeter, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{root}\\)\u003c/span\u003e\u003c/span\u003e is root development depth in meters, A is Irrigated area in square meters and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{I}\\)\u003c/span\u003e\u003c/span\u003e Irrigation efficiency (The efficiency of floot and drip irrigation systems were considered as 50 and 85%, respectively), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PWP\\)\u003c/span\u003e\u003c/span\u003e is permanent wilting point, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(MAD\\)\u003c/span\u003e\u003c/span\u003e is maximum allowable discharge and D stands for depth of root development. To determine the soil moisture before irrigation, a digital soil moisture sensor was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.5 Yield measurement\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo determine the grain yield of wheat under different treatments, first, a few meters away from the plot edges, samples were taken from the quadrats. A wooden frame measuring one square meter was used for sampling, and three replicates were taken from each quadrat. Wheat plants within the frame were harvested using a sickle, and after threshing, the grains were carefully separated and weighed. The yield was then extrapolated to per hectare. For calculating the yield and yield components of corn, samples were taken and measured from the middle rows of each quadrat (the fourth and fifth rows) after passing through the plot edges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analyses\u003c/h2\u003e \u003cp\u003eData was analyzed Using MSTAC statistical software. Considering that the design used in this study (split-plot strip factorial design with complete random blocks) includes the main plot, sub-plot and sub-sub-plot (two factors) There are three factors of error in this plan. This means that there are three different errors.\u003c/p\u003e \u003cp\u003eThe linear statistical model for this experimental design is shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significance was calculated based on F-tests and Duncan's multiple range test at the 0.01 and 0.05 probability levels.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${Y}_{hijkl}=\\mu +{r}_{h}+{a}_{i}+{\\delta }_{hi}+{b}_{j}+{ab}_{ij}+{\\gamma }_{hij}+{c}_{k}+{ac}_{ik}+{bc}_{jk}+{abc}_{ijk}+{d}_{l}+{ad}_{il}+{bd}_{jl}+{abd}_{ijl}+{abcd}_{ijkl}+{\\epsilon }_{hijkl}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{hijkl}\\)\u003c/span\u003e\u003c/span\u003e is the response (measurement) for h \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(hijkl\\)\u003c/span\u003e\u003c/span\u003eth observation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e stands for general mean effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({r}_{h}\\)\u003c/span\u003e\u003c/span\u003e is the effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(h\\)\u003c/span\u003e\u003c/span\u003eth block, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor (main factor), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{hi}\\)\u003c/span\u003e\u003c/span\u003e stands for the main plot error, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor (subplot factor), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ab}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma }_{hij}\\)\u003c/span\u003e\u003c/span\u003e is the subplot error, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({c}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003eth level of tillage factor (sub-subplot factor), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ac}_{ik}\\)\u003c/span\u003e\u003c/span\u003e is the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003eth level of tillage factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({bc}_{jk}\\)\u003c/span\u003e\u003c/span\u003e stands for the effect of the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003eth level of tillage factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({abc}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e is the interaction of the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003eth level of tillage factor factors, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{l}\\)\u003c/span\u003e\u003c/span\u003e is the effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(l\\)\u003c/span\u003e\u003c/span\u003eth level of planting patterns factor (sub-subplot factor), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ad}_{ik}\\)\u003c/span\u003e\u003c/span\u003e is the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(l\\)\u003c/span\u003e\u003c/span\u003eth level of planting patterns factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({bd}_{jl}\\)\u003c/span\u003e\u003c/span\u003e stands for the effect of the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(l\\)\u003c/span\u003e\u003c/span\u003eth level of planting patterns factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({abd}_{ijl}\\)\u003c/span\u003e\u003c/span\u003e is the interaction of the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(l\\)\u003c/span\u003e\u003c/span\u003eth level of planting patterns factor factors,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({abcd}_{ijkl}\\)\u003c/span\u003e\u003c/span\u003e is the interaction of the interaction effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003eth level of residue factor,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003eth level of irrigation factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003eth level of tillage factor factors and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(l\\)\u003c/span\u003e\u003c/span\u003eth level of planting patterns factor factors and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{hijkd}\\)\u003c/span\u003e\u003c/span\u003e stands for the sub-subplot error\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Results of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil\u003c/h2\u003e \u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e emission from the soil were measured during wheat-maize rotation in different systems. Statistical analysis was performed to identify the factors with significant effect on the CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. The ANOVA results for CO\u003csub\u003e2\u003c/sub\u003e emission, wheat and maize yields is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of this study indicated that the amounts of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil in different systems were significantly different. The ANOWA results showed that residue management, irrigation method, and tillage had a significant impact on CO\u003csub\u003e2\u003c/sub\u003e emission, while the planting methods had no significant effect. The results also showed that among all the interaction effects, only the interaction of irrigation and tillage had significant effect on CO\u003csub\u003e2\u003c/sub\u003e emission. On average, the treatments with residues had 28% lower CO\u003csub\u003e2\u003c/sub\u003e emission per ha per day (equivalent to 1.73 kg/ha/day) compared to the treatments without residue. The irrigation method also had a significant effect on CO\u003csub\u003e2\u003c/sub\u003e emission.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ANOVA results for CO2 emission and wheat and maize yield.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewheat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emaize\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77322\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e392626\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1545282\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15469776\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1634158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigation method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.85\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7601100\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e958322783\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue and irrigation method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.52\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e556\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4408648\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e398101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344157\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10858073\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue and tillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.342\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e344780\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2078900\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of irrigation and tillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7. 56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215040\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1704898\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue irrigation and tillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143073\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e517024\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3991254\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25532658\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue and Planting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416167\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38364\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of irrigation and Planting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158296\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4591460\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue, irrigation and Planting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7160\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e531137\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of tillage and Planting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37606\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17235227\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue, tillage and Planting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46005\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e445278\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of irrigation, tillage and Planting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76577\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4538295*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction of residue, irrigation, tillage and Planting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31533\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e573382\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e490917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eKallenbach et al. (2010) considered water management to be influential in the biological, chemical, and physical properties of the soil, which can affect the amount of CO\u003csub\u003e2\u003c/sub\u003e emission. In this study, drip irrigation had an average of 16% lower CO\u003csub\u003e2\u003c/sub\u003e emission per ha per day (equivalent to 0.92 kg/ha/day) compared to flood irrigation. This reduction in the emission is due to the lower volume of water entering the soil in drip irrigation compared to flood irrigation. In flood irrigation, a large amount of water enters the soil pores, leading to disturbance in the balance of soil moisture and air inside the pores. According to Chounian et al. (2008), the respiration of CO\u003csub\u003e2\u003c/sub\u003e in the ecosystem is highly sensitive to soil moisture during the plant growth period. Therefore, the higher amount of water in irrigation leads to an increase in CO\u003csub\u003e2\u003c/sub\u003e emission. Buragiene et al. (2019), suggested that CO\u003csub\u003e2\u003c/sub\u003e emission from soil had a positive linear correlation with soil moisture content. In another study, it was reported that CO\u003csub\u003e2\u003c/sub\u003e emission increases due to increase in organic matter oxidation with increased soil moisture (Jabro et al, 2008). Many studies such as Yerli et al. (2022), Zornoza et al. (2016), Sinaie et al. (2019) and Zhong et al. (2021) expressed that deficit irrigation reduces CO\u003csub\u003e2\u003c/sub\u003e emissions from soil. However, Fresno et al. (2022), reported that regulated deficit irrigation in maize using sprinkler irrigation did not reduce CO\u003csub\u003e2\u003c/sub\u003e emission. Also, the percentage of reduction of CO\u003csub\u003e2\u003c/sub\u003e emission from soil under deficit irrigation methods is usually different for each study.\u003c/p\u003e \u003cp\u003eReduced tillage and no tillage reduced CO\u003csub\u003e2\u003c/sub\u003e emission by 5.32 kg/ha/day (equivalent to 6.5%) and 1.14 kg/ha/day (equivalent to 20%) compared to conventional tillage, respectively. In a study on silage maize, it was reported that no tillage reduced CO\u003csub\u003e2\u003c/sub\u003e emissions of silage maize and fresh silage yield 5.1 and 26.1%, respectively (Yerli et al, 2022). It is assumed that conservative tillage systems through minimizing soil disturbance or the absence of soil disturbance in no tillage decrease microbial decomposition of organic matter and as a result reduce CO2 emission (Chaplot et al, 2015). Yerli et al. (2022), Buragiene et al. (2019) and Nyambo et al. (2020), also reported that intensive tillage lead to more CO\u003csub\u003e2\u003c/sub\u003e emission from soil compared to reduced tillage or no tillage. The decomposition of organic matter and carbon release is an aerobic process in which oxygen increases the activity of microbes that feed on organic matter. One of the soil properties that has significant effect on CO\u003csub\u003e2\u003c/sub\u003e emission from residue is soil pH (Yerli et al, 2022). It is reported that the lowest and highest CO\u003csub\u003e2\u003c/sub\u003e emission occurs in strongly acidic soils and slightly acidic soils (Ntonta et al, 2022). It is well known that the decomposition of organic substances increases soil acidity (Vaseghi et al, 2005). Under intensive tillage, the O\u003csub\u003e2\u003c/sub\u003e level of soil increase and suitable environment is provided for microbial activities, thus CO\u003csub\u003e2\u003c/sub\u003e emission increase (Yerli et al, 2022). The results of daily CO\u003csub\u003e2\u003c/sub\u003e emission from levels of the factors are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAccording to the results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the interaction of irrigation and tillage had significant effect on CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. The interaction effect of irrigation and tillage is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The highest emissions were observed in the treatments with flood irrigation and conventional tillage, with an average of 6.33 kg/ha/day, while the lowest emissions belonged to the treatments with drip irrigation and no-tillage, with an average of 4.2 kg/ha/day. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, reduced tillage had no significant effect on CO2 emission compared to conventional tillage, but no tillage reduced the emission significantly in both flood and drip irrigation. Page et al. (2012) reported a reduction in CO2 emission with conservative tillage. Silva-olaya et al. (2013) also reported that conservative tillage had a significant reduction in CO2 emission compared to conventional tillage. Among the different systems, the highest CO2 emission from the soil were observed in the residue- conventional tillage- flood irrigation- and flat planting (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e), with a rate of 7.35 kg/ha/day, while the lowest emission belonged to without residue- drip irrigation- no-tillage- raised bed planting (R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e), with a rate of 3.41 kg/ha/day.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the CO\u003csub\u003e2\u003c/sub\u003e emission from the soil in different months of the year is presented for four systems. The measurements of carbon dioxide emission in this study were conducted for maize cultivation, which took place on August 28, 2018, after the previous year's wheat harvest, and immediately after maize harvest, on December 15, 2018 Xu and Qi (2001) reported a significant correlation between the amount of CO\u003csub\u003e2\u003c/sub\u003e emission and factors such as soil temperature, soil moisture content, or their interaction. Lu et al. (2015) also reported a change of 76 to 96 percent in CO\u003csub\u003e2\u003c/sub\u003e output from the soil under the influence of temperature and soil moisture variations. In this study, significant changes in the amount of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil were observed with the change of seasons and the growth stages of the plants. As shown in the (16\u0026thinsp;\u0026minus;\u0026thinsp;4) graph, there was an increase in CO\u003csub\u003e2\u003c/sub\u003e emission for all systems in September for maize cultivation and in December for wheat cultivation. In the second half of September, with the high growth of maize and the intense heat of the Khuzestan region, the amount of carbon dioxide released from the soil reached its peak. In the conventional tillage system with residual presence, flood irrigation, and flat cultivation, the emission reached 15.29 kg per day per hectare. However, in a similar system with a change in tillage method from conventional to no-till, the emission was reduced to 12.13 kg per day per hectare. Among the conservation systems, the system with residue, drip irrigation, no-till, and flat planting had the highest CO\u003csub\u003e2\u003c/sub\u003e emission of 9.61 kg per day per ha during the same period, indicating CO\u003csub\u003e2\u003c/sub\u003e emission can be reduced significantly by using conservation tillage and pressurized irrigation. In wheat production, due to the higher growth rate and soil moisture content resulted from irrigation and rainfall, CO\u003csub\u003e2\u003c/sub\u003e emission reached its peak in December. In March and April, due to the cold weather and less irrigation, and in May, due to land drying, the emission was at its lowest level. However, the difference in CO\u003csub\u003e2\u003c/sub\u003e emission among different systems during the growing season was significant, and systems with no-tillage practices had the lowest emission.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The results of CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the contribution of different inputs to CO\u003csub\u003e2\u003c/sub\u003e eq emission in the systems is shown. In this study, electricity, fertilizers, and fuel had the highest share among the inputs in CO\u003csub\u003e2\u003c/sub\u003e eq emission. In a study by Khoshnavisan et al. (2013), on the emissions of greenhouse gases in wheat production in Isfahan province, electricity and chemical fertilizers had the highest contribution. In another study, fuel and chemical fertilizers had the highest share in CO\u003csub\u003e2\u003c/sub\u003e eq emissions in maize production (Pishkar et al, 2011). In the current study, the contribution of chemical fertilizers and pesticides was the same in all treatments, at 760 kg and 20.4 kg CO\u003csub\u003e2\u003c/sub\u003e eq, respectively. However, the emissions resulting from machinery inputs, fuel, and electricity (for pumping irrigation water) varied among different systems. The highest emission for machinery and fuel inputs were observed in the system 2, with 87.43 kg and 488.46 kg CO\u003csub\u003e2\u003c/sub\u003e eq, respectively. The lowest emissions were observed in the system number 23, with 39.21 kg and 247.24 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;These differences are due to differences in tillage and planting operations, as no-tillage removed heavy plowing, disking, and residue incorporation. The energy required for irrigation had the highest share in energy consumption and CO\u003csub\u003e2\u003c/sub\u003e eq emission among the inputs in wheat-maize rotation. The highest emission was observed in the system 13, with 3537.06 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;In this system, due to frequent heavy flood irrigation, the absence of residue, and conventional tillage, the soil quickly loses its moisture and requires more frequent irrigation. Among conservation systems with flood irrigation, the system 6 had an 1855.17 kg CO\u003csub\u003e2\u003c/sub\u003e eq emission due to the electricity used for pumping irrigation water.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe findings of this study indicated that the inputs had a significant difference in their contribution to the CO\u003csub\u003e2\u003c/sub\u003e eq emission in the different systems. The CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs for wheat ranged from 1064 to 1293 kg CO\u003csub\u003e2\u003c/sub\u003e eq per hectare, and for maize, it ranged from 1505 to 2325 kg CO\u003csub\u003e2\u003c/sub\u003e eq per hectare. Based on the yield, CO\u003csub\u003e2\u003c/sub\u003e eq for wheat calculated in a range of 258 to 365 kg per ton, and for maize, it ranged from 140 to 313 kg per ton. Rajabi et al. (2012), reported an average CO\u003csub\u003e2\u003c/sub\u003e eq emission of 8.103 to 5.271 kg per ton of wheat produced in Gorgan province. One of the main reasons for these differences is the higher wheat yield in Gorgan province.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;6 illustrates the impact of irrigation methods on the CO2 eq emission from the inputs under tillage systems and planting methods. Reduced tillage and no tillage reduced CO\u003csub\u003e2\u003c/sub\u003e eq emission compared to conventional tillage due to less field operations. In fact, with the change from conventional tillage to no-tillage, the emission decreased from 3534 kg to 3027 kg CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;Changing the irrigation method from flood irrigation to drip irrigation also resulted in a reduction in the CO\u003csub\u003e2\u003c/sub\u003e eq emission in all treatments. Most agricultural lands in Khuzestan province are irrigated through pumping water from rivers or wells, which requires a significant amount of the energy. Drip irrigation, due to its high irrigation efficiency, reduced irrigation water requirements by more than 50% in all treatments, led to a reduction in energy consumption.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;6.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The total CO\u003csub\u003e2\u003c/sub\u003e emission and wheat and maize yields\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;7 shows the total CO\u003csub\u003e2\u003c/sub\u003e emission from the inputs and the soil in different systems. As it is shown, R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e (system 1) had the highest total emission with 6129.47 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per year. The minimum CO\u003csub\u003e2\u003c/sub\u003e emission occurred in the system of R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e (system 24) with 3900 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. By changing conventional tillage and flood irrigation to no tillage and drip irrigation, the system of R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e had an emission of 4471.28 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per year (system 11). One of the reasons for the reduction in this emission can be attributed to the lower fuel consumption resulting from the reduction in the number of required soil cultivation and planting operations. One of the other reason can be attributed to reduction in CO\u003csub\u003e2\u003c/sub\u003e emission from the soil under no tillage and drip irrigation. The CO\u003csub\u003e2\u003c/sub\u003e emission from the soil increased due to keeping residue on surface, but the CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs reduced. However, the total CO\u003csub\u003e2\u003c/sub\u003e emission increased due to keeping residue.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;7.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e, wheat and maize grain yields in in different systems is shown. Both wheat and maize had the highest yield in the system 8 (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e) with 4.74 and 11.87 ton.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. As it can be seen, first 12 systems (with residue) had higher yield in both wheat and maize production compared to systems 13 to 24 (burned residue). Residue keeps water available to plant for a longer time and it would improve crop yield especially in semi-arid and arid areas. No tillage and reduced tillage reduced yield in both wheat and maize production, but it expected that in long term, conservation tillage improve the yield. In the short term, it is not possible to increase earthworm populations and improve soil structure, and only lower disturbance to the soil is main difference. Drip irrigation significantly increased wheat and maize grain yields. In a study in Khuzestan province on wheat, Farahani et al. (2020), also reported that pressurized irrigation (sprinkler irrigation) increased wheat grain yield compared to flood irrigation.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results showed that changing the irrigation method from flood to drip irrigation and reducing the intensity of soil tillage significantly reduced the CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs due to reduction in the fuel and electricity consumption. The study also revealed that the CO\u003csub\u003e2\u003c/sub\u003e emission from the soil varied significantly in different months, with the highest emission occurring in September for maize and in December for wheat. In September, the high growth rate and development of maize and intense heat increased the emission of CO\u003csub\u003e2\u003c/sub\u003e from the soil. However, in the same period, the change in soil tillage from conventional to no tillage and a change in irrigation from flood to drip irrigation, decreased the CO\u003csub\u003e2\u003c/sub\u003e emission from 15.29 kg per day per ha to 9.61 kg per day per ha. Similarly, in December, with the same changes in wheat production, the emission decreased from 11.6 kg per day per ha to 10.49 kg per day per ha, indicating the significant impact of conservation agriculture practices and pressurized irrigation in reducing the CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. In general, the study showed that residue management, irrigation method, and tillage system had a significant effect on CO\u003csub\u003e2\u003c/sub\u003e emission, while planting method had no significant effect. The residue increased the CO\u003csub\u003e2\u003c/sub\u003e emission from the soil, but reduced the CO2 eq emission from the inputs. Totally, the treatments without residue had 28% less CO\u003csub\u003e2\u003c/sub\u003e emission compared to the treatments with residue. Drip irrigation resulted in an average reduction of 16% in CO\u003csub\u003e2\u003c/sub\u003e emission compared to flood irrigation. In flood irrigation, a large volume of water enters the soil pores, causing imbalances in soil moisture and air, which leads to an increase in CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. Changing tillage method from conventional to reduced tillage and no tillage resulted in a reduction of 5.6% and 20% in CO\u003csub\u003e2\u003c/sub\u003e emission, respectively. The interaction between irrigation and tillage also had a significant effect on CO\u003csub\u003e2\u003c/sub\u003e emission from the soil. The highest emissions were observed in flood irrigation and conventional tillage treatments with an average of 6.33 kg of CO\u003csub\u003e2\u003c/sub\u003e emission per ha per year, while the lowest emissions belonged to drip irrigation and no tillage treatments with an average of 4.2 kg of CO\u003csub\u003e2\u003c/sub\u003e emission per ha per year.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThe results showed that electricity, fertilizers, and fuel had the highest contribution to CO\u003csub\u003e2\u003c/sub\u003e eq emission from agricultural inputs. The emission from the inputs per ton of wheat production ranged from 258 to 365 kg CO\u003csub\u003e2\u003c/sub\u003e eq, and for maize, it ranged from 140 to 313 kg of CO\u003csub\u003e2\u003c/sub\u003e eq, depending on the different systems. The amount of \u003csub\u003eCO2\u003c/sub\u003e emission from the soil also varied from 3900 to 6219 kg per ha per year. One of the most important practices for conservation agriculture is residue management. Keeping residue on the field has benefits such as reducing water consumption, increasing soil organic matter, and reducing compaction, ultimately leading to an increase in productivity. In this study, however, the total CO\u003csub\u003e2\u003c/sub\u003e emission increased due to keeping residue. Although, it should be noted that in Iran, residue is usually removed by burning on the field, and by considering the emissions from residue burning and the benefits of residue, it is suggested to keep residue on the field. \u0026nbsp;Considering the numerous advantages of conservative tillage and drip irrigation, especially in Iran\u0026apos;s dry and semi-dry conditions, this study recommends systems 10 and 12, which are considered conservation systems that have relatively lower emissions from the inputs and the soil compared to other systems.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Publish\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Eisa Bougari], [Mohammad Amin Asoodar], [Afshin Marzban] and [Navab Kazemi]. The first draft of the manuscript was written by [Eisa Bougari] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Agricultural Sciences and Natural Resources University of Khuzestan supported this work\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e This research is derived from the doctoral thesis of the first author of the article, which was conducted at this university.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Non-financial interests\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003enone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interest\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdalla K, Chivenge P, Ciais P, Chaplot V (2016) No-tillage lessens soil CO\u003csub\u003e2\u003c/sub\u003e emissions the most under arid and sandy soil conditions: results from a meta-analysis. 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Agricultural Water Management. 271, p, 107791.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYin W, Chai Q, Guo Y, Feng F, Zhao C, Yu A, Liu C, Fan Z, Hu F, Chen G (2017) Reducing carbon emissions and enhancing crop productivity through strip intercropping with improved agricultural practices in an arid area.\u0026nbsp;Journal of Cleaner Production.\u0026nbsp;166:197-208.\u003c/li\u003e\n \u003cli\u003eZhang H, Hobbie E.A, Feng P. and Hu K \u0026nbsp;(2022) Can conservation agriculture mitigate climate change and reduce environmental impacts for intensive cropping systems in North China Plain?. Science of The Total Environment, 806, p.151194.\u003c/li\u003e\n \u003cli\u003eZhong Y, Li, J, Xiong H. 2021. Effect of deficit irrigation on soil CO2 and N2O emissions and winter wheat yield. Journal of Cleaner Production. 279: 123718.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZornoza, R, Rosales RM, Acosta JA, Rosa JM, Arcenegui V, Faz A, Perez-Pastor A (2016) Efficient irrigation management can contribute to reduce soil CO\u003csub\u003e2\u003c/sub\u003e emissions in agriculture. Geoderma. 263: 70-77.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CO2 emission, No tillage, Drip irrigation, Raised bed planting, Residue, Farming systems","lastPublishedDoi":"10.21203/rs.3.rs-3684936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3684936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, the amounts of greenhouse gas (equivalent to CO\u003csub\u003e2\u003c/sub\u003e) emitted from inputs consumption and CO\u003csub\u003e2\u003c/sub\u003e emission from the soil in the various wheat-maize farming systems for one year were evaluated in an experimental design. In the studied systems, the CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs for wheat and maize ranged from 258 to 365 kg.ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 140 to 313 kg.ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively, which electricity, fertilizer and fuel inputs had the largest share. By changing the agricultural systems, there was a potential to reduce CO\u003csub\u003e2\u003c/sub\u003e eq emission from the inputs by up to 29% per ton of wheat and up to 55% per ton of maize. Results showed that residue management, irrigation method, and tillage had significant effects on CO\u003csub\u003e2\u003c/sub\u003e emission from the soil, while planting method showed no significant effect. Systems with residue (R\u003csub\u003e1\u003c/sub\u003e) had about 28% higher CO\u003csub\u003e2\u003c/sub\u003e emission from the soil, by changing the tillage method from conventional tillage (T\u003csub\u003e1\u003c/sub\u003e) to no-tillage (T\u003csub\u003e3\u003c/sub\u003e) and changing the irrigation method from flood (I\u003csub\u003e1\u003c/sub\u003e) to drip (I\u003csub\u003e2\u003c/sub\u003e), a reduction of approximately 20 and 12% in emission was observed, respectively. Among the studied systems, the highest amount of CO\u003csub\u003e2\u003c/sub\u003e emission from the soil was observed in the system with residue, conventional tillage, flood irrigation, and flat planting (R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e) with a rate of 7.35 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per day. The highest total CO\u003csub\u003e2\u003c/sub\u003e emission from inputs and emission from the soil were observed in R\u003csub\u003e1\u003c/sub\u003eT\u003csub\u003e1\u003c/sub\u003eI\u003csub\u003e1\u003c/sub\u003eP\u003csub\u003e1\u003c/sub\u003e system with a rate of 6129.47 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per year, which decreased to 4471.28 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e per year in R\u003csub\u003e2\u003c/sub\u003eT\u003csub\u003e3\u003c/sub\u003eI\u003csub\u003e2\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e system.\u003c/p\u003e","manuscriptTitle":"Investigation of CO2 emission from soil and farm inputs in different farming systems in wheat-maize rotation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-09 10:55:41","doi":"10.21203/rs.3.rs-3684936/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08896e0d-e0c4-4a43-a98f-18e48f8d5762","owner":[],"postedDate":"January 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-13T06:50:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-09 10:55:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3684936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3684936","identity":"rs-3684936","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}