Energy budgeting and greenhouse gas emission in cucumber under tunnel farming in Punjab, Pakistan

In agricultural production, it is important to determine where input usage saving can be implemented by taking energy use into consideration and to analyze the greenhouse gas emissions of agricultural activities. This study has been conducted to review orange (Citrus sinensis L.) production in terms of energy balance and greenhouse gas (GHG) emissions. This study was carried out during the 2015/2016 production season in Adana, a province in Turkey. Energy balance and GHG emissions have been defined by calculating the inputs and outputs of agricultural nature used in orange production. The findings of the study indicate that the distribution of energy inputs in orange production are 11,880 MJ ha−1 (34.10%) of electricity, 10,079.75 MJ ha−1 (28.93%) of chemical fertilizer energy, 7630 MJ ha−1 (21.90%) of chemical energy, 3052 MJ ha−1 (8.76%) of diesel fuel energy, 1348.91 MJ ha−1 (3.87%) of human labor energy, 378 MJ ha−1 (1.09%) of irrigation water energy, 351.22 MJ ha−1 (1.01%) of machinery energy and 118.80 MJ ha−1 (0.34%) of lime energy. In total, input energy (IE) in orange production has been calculated as 34,838.68 MJ ha−1 and the output energy (OE) has been calculated as 95,000 MJ ha−1. Energy use efficiency (EUE), specific energy (SE), energy productivity (EP) and net energy (NE) have been calculated as 2.73, 0.70 MJ kg−1, 1.44 kg MJ−1 and 60,161.32 MJ ha−1, respectively. The total energy input in the production of oranges was divided into: 47.82% direct, 52.18% indirect, 4.96% from renewable sources and 95.04% from non-renewable sources. The GHG emissions figure for orange production was 3794.26 kg CO2–eq ha−1, with electricity having the greatest share, 1983.96 (52.29%); the GHG ratio was 0.08 kg CO2–eq kg−1. According to the results, the production of orange was considered to be profitable in terms of EUE.

Abstract. This paper summarizes currently available data on greenhouse gas (GHG) emissions from African natural ecosystems and agricultural lands. The available data are used to synthesize current understanding of the drivers of change in GHG emissions, outline the knowledge gaps, and suggest future directions and strategies for GHG emission research. GHG emission data were collected from 75 studies conducted in 22 countries (n = 244) in sub-Saharan Africa (SSA). Carbon dioxide (CO2) emissions were by far the largest contributor to GHG emissions and global warming potential (GWP) in SSA natural terrestrial systems. CO2 emissions ranged from 3.3 to 57.0 Mg CO2 ha−1 yr−1, methane (CH4) emissions ranged from −4.8 to 3.5 kg ha−1 yr−1 (−0.16 to 0.12 Mg CO2 equivalent (eq.) ha−1 yr−1), and nitrous oxide (N2O) emissions ranged from −0.1 to 13.7 kg ha−1 yr−1 (−0.03 to 4.1 Mg CO2 eq. ha−1 yr−1). Soil physical and chemical properties, rewetting, vegetation type, forest management, and land-use changes were all found to be important factors affecting soil GHG emissions from natural terrestrial systems. In aquatic systems, CO2 was the largest contributor to total GHG emissions, ranging from 5.7 to 232.0 Mg CO2 ha−1 yr−1, followed by −26.3 to 2741.9 kg CH4 ha−1 yr−1 (−0.89 to 93.2 Mg CO2 eq. ha−1 yr−1) and 0.2 to 3.5 kg N2O ha−1 yr−1 (0.06 to 1.0 Mg CO2 eq. ha−1 yr−1). Rates of all GHG emissions from aquatic systems were affected by type, location, hydrological characteristics, and water quality. In croplands, soil GHG emissions were also dominated by CO2, ranging from 1.7 to 141.2 Mg CO2 ha−1 yr−1, with −1.3 to 66.7 kg CH4 ha−1 yr−1 (−0.04 to 2.3 Mg CO2 eq. ha−1 yr−1) and 0.05 to 112.0 kg N2O ha−1 yr−1 (0.015 to 33.4 Mg CO2 eq. ha−1 yr−1). N2O emission factors (EFs) ranged from 0.01 to 4.1 %. Incorporation of crop residues or manure with inorganic fertilizers invariably resulted in significant changes in GHG emissions, but results were inconsistent as the magnitude and direction of changes were differed by gas. Soil GHG emissions from vegetable gardens ranged from 73.3 to 132.0 Mg CO2 ha−1 yr−1 and 53.4 to 177.6 kg N2O ha−1 yr−1 (15.9 to 52.9 Mg CO2 eq. ha−1 yr−1) and N2O EFs ranged from 3 to 4 %. Soil CO2 and N2O emissions from agroforestry were 38.6 Mg CO2 ha−1 yr−1 and 0.2 to 26.7 kg N2O ha−1 yr−1 (0.06 to 8.0 Mg CO2 eq. ha−1 yr−1), respectively. Improving fallow with nitrogen (N)-fixing trees led to increased CO2 and N2O emissions compared to conventional croplands. The type and quality of plant residue in the fallow is an important control on how CO2 and N2O emissions are affected. Throughout agricultural lands, N2O emissions slowly increased with N inputs below 150 kg N ha−1 yr−1 and increased exponentially with N application rates up to 300 kg N ha−1 yr−1. The lowest yield-scaled N2O emissions were reported with N application rates ranging between 100 and 150 kg N ha−1. Overall, total CO2 eq. emissions from SSA natural ecosystems and agricultural lands were 56.9 ± 12.7 × 109 Mg CO2 eq. yr−1 with natural ecosystems and agricultural lands contributing 76.3 and 23.7 %, respectively. Additional GHG emission measurements are urgently required to reduce uncertainty on annual GHG emissions from the different land uses and identify major control factors and mitigation options for low-emission development. A common strategy for addressing this data gap may include identifying priorities for data acquisition, utilizing appropriate technologies, and involving international networks and collaboration.

این تحقیق به بررسی انتشار گازهای گلخانه‌ای، انرژی مصرفی و هزینه‌های تولید پنبه در استان گلستان پرداخته است. اطلاعات از طریق پرسشنامه و مصاحبه حضوری با 43 پنبه‌کار گلستانی جمع‌آوری شد. نتایج نشان داد که مجموع انرژی ورودی برای تولید پنبه در استان گلستان 28898 مگاژول بر هکتار است. دو نهاده سوخت دیزل و ماشین‌های کشاورزی به‌ترتیب با 6/45 و 9/15 درصد، پرمصرف‌ترین نهاده‌های انرژی در تولید بودند. کارایی انرژی 58/1 به‌دست آمد. نتایج استفاده از تابع کاب داگلاس نشان داد، تأثیر نهاده‌های نیروی انسانی، سوخت دیزل، آب آبیاری، کودهای شیمیایی و کود حیوانی بر روی عملکرد مثبت و تأثیر نهاده‌های بذر، ماشین‌های کشاورزی و مواد شیمیایی بر عملکرد پنبه منفی است. نتایج تحلیل حساسیت ورودی‌های انرژی نشان داد با افزایش یک مگاژول انرژی نهاده‌های بذر و نیروی انسانی عملکرد به‌ترتیب به میزان 29/0 و 22/0 کیلوگرم افزایش می‌یابد. مجموع انتشار گازهای گلخانه‌ای kg CO2 eq ha-1 18/1430 محاسبه شد. سه نهاده سوخت دیزل، کود حیوانی و ماشین‌های کشاورزی با 2/45، 5/23 و 8/22 درصد، بیش‌ترین انتشار گازهای گلخانه‌ای را در تولید داشتند. سوخت دیزل با بیش‌ترین انرژی مصرفی و انتشار گازهای گلخانه‌ای در تولید پنبه، تنها در حدود 7/2 درصد از هزینه‌های متغیر را شامل می‌شد. همچنین نسبت سود به هزینه برای تولید پنبه در استان گلستان 16/1 محاسبه شد.

Population growth and world climate changes are putting high pressure on agri-food production systems. Exacerbating use of energy sources and expanding the environmental damaging symptoms are the results of these difficult situations. This study was conducted to determine the energy balance for saffron production cycle and investigate the corresponding greenhouse gas (GHG) emissions in Iran. Saffron (Crocus sativus L.) is one of the main spice that historically cultivated in Iran. Data were obtained from 127 randomly selected saffron growers using a face to face questionnaire technique. The results revealed that in 5 years of saffron production cycle, the overall input and output energy use were to be 163,912.09 and 184,868.28 MJ ha(-1), respectively. The highest-level of energy consumption belongs to seeds (23.7 %) followed by chemical fertilizers (23.4 %). Energy use efficiency, specific energy, net energy, and energy productivity of saffron production were 1.1, 13.4 MJ kg(-1), 20,956.2 MJ ha(-1), and 0.1 kg MJ(-1), respectively. The result shows that the cultivation of saffron emits 2325.5 kg CO2 eq. ha(-1) greenhouse gas, in which around 46.5 % belonged to electricity followed by chemical fertilizers. In addition the Cobb-Douglas production function was applied into EViews 7 software to define the functional relationship. The results of econometric model estimation showed that the impact of human labor, electricity, and water for irrigation on stigma, human labor, electricity, and seed on corm and also human labor and farmyard manure (FYM) on flower and leaf yield were found to be statistically significant. Sensitivity analysis results of the energy inputs demonstrated that the marginal physical productivity (MPP) worth of electricity energy was the highest for saffron stigma and corm, although saffron flower and leaf had more sensitivity on chemicals energy inputs. Moreover, MPP values of renewable and indirect energies were higher than non-renewable and direct energies, respectively.

A diachronic study of greenhouse gas emissions of French dairy farms according to adaptation pathways

Climate change and energy security concerns have driven the development of policies that encourage bioenergy production. Meeting EU targets for the consumption of transport fuels from bioenergy by 2020 will require a large increase in the production of bioenergy feedstock. Initially an increase in ‘first generation’ biofuels was observed, however ‘food competition’ concerns have generated interest in second generation biofuels (SGBs). These SGBs can be produced from co-products (e.g. cereal straw) or energy crops (e.g. miscanthus), with the former largely negating food competition concerns. In order to assess the sustainability of feedstock supply for SGBs, the financial, environmental and energy costs and benefits of the farm system must be quantified. Previous research has captured financial costs and benefits through linear programming (LP) approaches, whilst environmental and energy metrics have been largely been undertaken within life cycle analysis (LCA) frameworks. Assessing aspects of the financial, environmental and energy sustainability of supplying co-product second generation biofuel (CPSGB) feedstocks at the farm level requires a framework that permits the trade-offs between these objectives to be quantified and understood. The development of a modelling framework for Managing Energy and Emissions Trade-Offs in Agriculture (MEETA Model) that combines bio-economic process modelling and LCA is presented together with input data parameters obtained from literature and industry sources. The MEETA model quantifies arable farm inputs and outputs in terms of financial, energy and emissions results. The model explicitly captures fertiliser: crop-yield relationships, plus the incorporation of straw or removal for sale, with associated nutrient impacts of incorporation/removal on the following crop in the rotation. Key results of crop-mix, machinery use, greenhouse gas (GHG) emissions per kg of crop product and energy use per hectare are in line with previous research and industry survey findings. Results show that the gross margin – energy trade-off is £36GJ−1, representing the gross margin forgone by maximising net farm energy cf. maximising farm gross margin. The gross margin–GHG emission trade-off is £0.15kg−1 CO2 eq, representing the gross margin forgone per kg of CO2 eq reduced when GHG emissions are minimised cf. maximising farm gross margin. The energy–GHG emission trade-off is 0.03GJkg−1CO2 eq quantifying the reduction in net energy from the farm system per kg of CO2 eq reduced when minimising GHG emissions cf. maximising net farm energy. When both farm gross margin and net farm energy are maximised all the cereal straw is baled for sale. Sensitivity analysis of the model in relation to different prices of cereal straw shows that it becomes financially optimal to incorporate wheat straw at price of £11t−1 for this co-product. Local market conditions for straw and farmer attitudes towards incorporation or sale of straw will impact on the straw price at which farmers will supply this potential bioenergy feedstock and represent important areas for future research.

Abstract. Although the concept of producing higher yields with reduced greenhouse gas (GHG) emissions is a goal that attracts increasing public and scientific attention, the trade-off between high yields and GHG emissions in intensive agricultural production is not well understood. Here, we hypothesize that there exists a mechanistic relationship between wheat grain yield and GHG emission, and that could be transformed into better agronomic management. A total 33 sites of on-farm experiments were investigated to evaluate the relationship between grain yield and GHG emissions using two systems (conventional practice, CP; high-yielding systems, HY) of intensive winter wheat (Triticum aestivum L.) in China. Furthermore, we discussed the potential to produce higher yields with lower GHG emissions based on a survey of 2938 farmers. Compared to the CP system, grain yield was 39% (2352 kg ha−1) higher in the HY system, while GHG emissions increased by only 10%, and GHG emission intensity was reduced by 21%. The current intensive winter wheat system with farmers' practice had a median yield and maximum GHG emission rate of 6050 kg ha−1 and 4783 kg CO2 eq ha−1, respectively; however, this system can be transformed to maintain yields while reducing GHG emissions by 26% (6077 kg ha−1, and 3555 kg CO2 eq ha−1). Further, the HY system was found to increase grain yield by 39% with a simultaneous reduction in GHG emissions by 18% (8429 kg ha−1, and 3905 kg CO2 eq ha−1, respectively). In the future, we suggest moving the trade-off relationships and calculations from grain yield and GHG emissions to new measures of productivity and environmental protection using innovative management technologies.

Biosolid Management Options in Cassava Starch Industries of Thailand: Present Practice and Future Possibilities

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In this research, the energy use efficiency (EUE) and greenhouse gas emissions (GHG) of cotton cultivation in Beşiri district of Batman province in Turkey were determined. This research was conducted through face-to-face surveys with 64 farms selected by simple random sampling method in the 2018-2019 cultivation season. The energy input (EI) and energy output (EO) in cotton cultivation were calculated as 52,302.62 MJ/ha and 60,341.03 MJ/ha. Energy inputs consist of electricity energy with 19,948.86 MJ/ha(38.14%), chemical fertilizers energy with 14,163.83 MJ/ha (27.08%), diesel fuel energy with 13,218.49 (25.27%), irrigation water energy with 2563.79 MJ/ha(4.90%), machinery energy with 1071.14 MJ/ha(2.05%), chemicals energy with 797.96 MJ/ha (1.53%), seed energy with 291.46 MJ/ha (0.56%) and human labour energy with 247.09 MJ/ha(0.47%), respectively. Total energy inputs in cotton cultivation can be categorized as 68.79% direct, 31.21% indirect, 5.93% renewable and 94.07% non-renewable. EUE, specific energy (SE), energy productivity (EP) and net energy (NE) in cotton cultivation were calculated as 1.15, 10.23 MJ/kg, 0.10 kg /MJ and 8038.41 MJ/ ha, respectively. Total GHG was calculated as 3742.59 kgCO2-eq ha-1 for cotton cultivation with the greatest share taken by nitrogen (26.19%). Nitrogen was followed by electricity (24.73%), irrigation water (18.48%), diesel fuel (17.31%), seed (5.04%), chemicals (2.93%), phosphorous (2.74%), human labour (2.36%), potassium (0.19%) and machinery (0.03%), respectively. GHG ratio value was calculated as 0.73 kgCO2-eq kg-1 in cotton cultivation.

The environmental sustainability of food production systems, including net greenhouse gas (GHG) emissions, is of increasing importance. In Norwegian pork production, animal performance is high in terms of reproduction, growth, and health. The development and use of an IPCC methodology-based model for estimating GHG emissions from pork production could be helpful in identifying the effects of progress in genetics and management. The objective was to investigate whether an IPCC methodology-based model was able to reflect the effects of the progress in genetics and management in pork production on the GHG emissions per kg carcass weight (CW). It is hypothesized that this progress has led to low GHG emissions intensities in Norwegian pork compared to global levels and that expected improvements will give a lasting reduction in GHG emissions intensities. A model ‘HolosNorPork’ for estimating net farm gate GHG emissions intensities was developed, including allocation procedures, at the pig production unit level. The model was run with pig production data from in average 632 farms from 2014 to 2019. The estimates include emissions of enteric and manure storage methane, manure storage nitrous oxide emissions, as well as GHG emissions from production and transportation of purchased feeds, and direct and indirect GHG emissions caused by energy use in pig-barns. The model was able to estimate the effects on net GHG emissions intensities from pork production on the basis of production characteristics. The estimated net GHG emissions intensity was found to have decreased from on average 2.49 to 2.34 kg CO2 eq. kg−1 CW over the investigated period. For 2019 the net GHG emission for the one-third lower performing farms was estimated to 2.56 kg CO2 eq. kg−1 CW, whereas for the one-third medium and one-third best performing farms the estimates were 2.36 and 2.16 kg CO2 eq. kg−1 CW, respectively. The net GHG emissions intensity for pork carcasses from boars was estimated to be 2.07 kg CO2 eq. kg−1 CW. For the health regimes investigated, Conventional and Specific-Pathogen Free (SPF), the estimated GHG emissions intensities for 2019 were 2.37 and 2.24 kg CO2 eq. kg−1 CW, respectively. The effects on net GHG emissions intensities of breeding and management measures were estimated to be profound, and this progress in pig production systems contributes to an on-going strengthening of pork as a sustainable source for human food supply.

Rationale: Greenhouse gas (GHG) emissions from crop agriculture are of great concern in the context of changing climatic conditions; however, in most cases, data based on lifecycle assessments are not available for grain yield variations or the carbon footprint of maize. The current study aimed to determine net carbon emissions and sequestration for maize grown in Bangladesh. Methods: The static closed-chamber technique was used to determine total GHG emissions using data on GHG emissions from maize fields and secondary sources for inputs. A secondary source for regional yield data was used in the current study. GHG emission intensity is defined as the ratio of total emissions to grain yield. The net GHG emission/carbon sequestration was determined by subtracting total GHG emissions (CO2 eq.) from net primary production (NPP). Results: Grain yields varied from 1590 to 9300 kg ha−1 in the wet season and from 680 to 11,820 kg ha−1 in the dry season. GHG emission intensities were 0.53–2.21 and 0.37–1.70 kg CO2 eq. kg−1 grain in the wet and dry seasons, respectively. In Bangladesh, the total estimated GHG emissions were 1.66–4.09 million tonnes (MT) CO2 eq. from 2015 to 2020, whereas the net total CO2 sequestration was 1.51–3.91 MT. The net CO2 sequestration rates were 984.3–5757.4 kg ha−1 in the wet season and 1188.62–5757.39 kg ha−1 in the dry season. This study observed spatial variations in carbon emissions and sequestration depending on growing seasons. In the rice–maize pattern, maize sequestered about 1.23 MT CO2 eq. per year−1, but rice emitted about 0.16 MT CO2 eq. per year−1. This study showed potential spatiotemporal variations in carbon footprints. Recommendation: Special care is needed to improve maize grain yields in the wet season. Fertiliser and water use efficiencies need to be improved to minimise GHG emissions under changing climatic conditions. Efforts to increase the area under cultivation with rice–maize or other non-rice crop-based cropping systems are needed to augment CO2 sequestration. The generation of a regional data bank on carbon footprints would be beneficial for combating the impact of climate change.

This study examined the input energy, economic indices, and Greenhouse Gas (GHG) emissions in sunflower farm enterprises of Kermanshah province of Iran. Different mechanization production systems involving traditional, semi‐mechanized, and mechanized ones were statistically compared. Results revealed that mechanized farms consumed more total inputs energy, while possessed significantly higher yield and better economic indices. In which, the human labor, diesel fuel, and fertilizer were the most predominant inputs in GHG emissions. In particular, traditional, semi‐mechanized and mechanized farms emitted 358, 386, and 438 kg CO2/ha, respectively. Also, technical efficiencies were reported as 0.88, 0.86, and 0.96, for traditional, semi‐mechanized, and mechanized farms, respectively. The relationship among different variables including energy inputs, GHG emissions, output energy, and benefit to cost ratio was studied using econometric modeling. Data envelopment analysis (DEA) and multi‐objective genetic algorithm (MOGA) were also applied to detect a set of Pareto frontiers in the combination of energy, environmental, and economic indices (energy consumption, GHG emissions, and benefit to cost ratio as three selected output parameters) for sunflower production. It has been observed that the capability of MOGA for energy saving was higher than DEA. Application results of DEA and MOGA combined algorithms showed that diesel fuel and water had the highest and lowest potential for total energy savings, respectively.

On the study of energy use and GHG (greenhouse gas) emissions in greenhouse cucumber production in Yazd province

Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks