Energy Consumption, Carbon Emissions and Global Warming Potential of Wolfberry Production in Jingtai Oasis, Gansu Province, China
During the last decade, China's agro-food production has increased rapidly and been accompanied by the challenge of increasing greenhouse gas (GHG) emissions and other environmental pollutants from fertilizers, pesticides, and intensive energy use. Understanding the energy use and environmental impacts of crop production will help identify environmentally damaging hotspots of agro-production, allowing environmental impacts to be assessed and crop management strategies optimized. Conventional farming has been widely employed in wolfberry (Lycium barbarum) cultivation in China, which is an important cash tree crop not only for the rural economy but also from an ecological standpoint. Energy use and global warming potential (GWP) were investigated in a wolfberry production system in the Yellow River irrigated Jingtai region of Gansu. In total, 52 household farms were randomly selected to conduct the investigation using questionnaires. Total energy input and output were 321,800.73 and 166,888.80 MJ ha−1, respectively, in the production system. The highest share of energy inputs was found to be electricity consumption for lifting irrigation water, accounting for 68.52%, followed by chemical fertilizer application (11.37%). Energy use efficiency was 0.52 when considering both fruit and pruned wood. Nonrenewable energy use (88.52%) was far larger than the renewable energy input. The share of GWP of different inputs were 64.52% electricity, 27.72% nitrogen (N) fertilizer, 5.07% phosphate, 2.32% diesel, and 0.37% potassium, respectively. The highest share was related to electricity consumption for irrigation, followed by N fertilizer use. Total GWP in the wolfberry planting system was 26,018.64 kg CO2 eq ha−1 and the share of CO2, N2O, and CH4 were 99.47%, 0.48%, and negligible respectively with CO2 being dominant. Pathways for reducing energy use and GHG emission mitigation include: conversion to low carbon farming to establish a sustainable and cleaner production system with options of raising water use efficiency by adopting a seasonal gradient water pricing system and advanced irrigation techniques; reducing synthetic fertilizer use; and policy support: smallholder farmland transfer (concentration) for scale production, credit (small- and low-interest credit) and tax breaks.
KeywordsEnergy use Greenhouse gas emissions Global warming potential Wolfberry plantation
Global greenhouse gas (GHG) emissions from food production nearly doubled during the period between 1961 and 2011 (FAOSTAT 2014), and will continue to rise as global crop demand is projected to have a 100–110% increase between 2005 and 2050 (Tilman et al. 2011). This alarming increase is closely correlated with intensive energy use. Agriculture is one of the major energy consumers and has experienced rapid intensification in recent decades (Nemecek et al. 2011). The production, transportation, processing, etc. of the agro-food sector contributes ~20% to global anthropogenic GHG emissions (FAO 2012). Notably, emissions from agricultural production account for over 80–86% of the global total food system emissions (Vermeulen et al. 2012). Recent studies have suggested that the agro-food sector is a significant contributor to global warming (Beccali et al. 2009; Michos et al. 2012).
As the largest food producer and consumer in the world, China has been one of the largest anthropogenic GHG emitters and currently emits around 20% of global GHGs (Leggett et al. 2011). Agricultural GHG emissions have been estimated at 11% of China’s national emissions, growing rapidly from 605 Mt CO2 eq in 1994 to 820 Mt CO2 eq in 2005 with a mean annual growth rate of 2.8% (Nayak et al. 2015; Lin et al. 2015). China is also the largest chemical fertilizer consumer with a N2O emissions increase from 0.18 Tg in 1978 to 0.41 Tg in 2010 (Cui et al. 2013).
The Chinese government made a commitment at the 2009 U.N. Climate Change Conference in Copenhagen that, by 2020, China’s CO2 emissions will drop with a target of 40–45% above the emission level in 2005 (Yang and Chen 2013). Agriculture is among the major sectors earmarked to reduce energy use while low carbon approaches in crop production is part of China's national climate change mitigation strategy. Accordingly, Gansu province has been designated as a circular economic demonstration area in China and low carbon and organic farming initiatives are a key area to attain green growth (Deng 2014).
Cash tree production has increased rapidly in China over the last decade, making it one of the largest fruit producers in the world (Su 2012; Cerutti et al. 2014). Cash tree production is an intensive agricultural system with high inputs of fertilizers, pesticides, irrigation, fossil fuels, and other materials (Li et al. 2010). However, growers are generally motivated by the notion of "the more fertilizer and irrigation, the higher the yield output," instead of energy efficiency and judicious management, with extensive management as a result, causing environmental issues (Cao 2014; Jiao et al. 2016). Efficient energy use in agriculture would minimize environmental burdens, decrease reliance on nonrenewable energy, and form a sustainable and economical production system (Uhlin 1998). In recent years, many studies have been conducted to determine the energy use pattern and efficiency of cash tree production; for example apple production in Greece (Strapatsa et al. 2006), energy inputs, outputs, and GHG emissions in organic, integrated and conventional peach orchards (Michos et al. 2012), resource consumption and emissions in olive oil production (Avraamides and Fatta 2008), environmental impacts in citrus production (Dwivedi et al. 2012) and energy use and GHG emissions in almond production in the United States (Kendall et al. 2015). Liu et al (2010a) compared carbon footprints of organic and conventional pear planting in northern China using life cycle analysis and indicated options available to reduce energy use and carbon emissions. In addition, Wang et al (2015) assessed the impact of diversified management practices of winter wheat on total GHG emissions.
Wolfberry (Lycium barbarum L.), is a shrub with its fruits being served as tonic food and traditional Chinese medicine, sold not only in domestic market but also exported to other countries and regions with good and stable prices (Li et al. 2017). It is salt tolerant, drought resistant, fast-growing, and fruits in the first year of planting. It is widely used for saline land improvement and rural economic development. Thus the area under wolfberry cultivation has expanded in northern China over the last few decades. However, there is little information on energy use efficiency and global warming potential (GWP) in wolfberry production systems in China.
Therefore, a combination of energy input and environmental impact analysis in a production system is necessary to optimize crop management practices, reduce the environmental impacts and promote sustainable development (Ming et al. 2015). The objectives of this study were to: (i) analyze the output–input energy; (ii) calculate total GHG emissions (CO2, N2O, and CH4), and (iii) determine GWP per unit of chemical input and output in a wolfberry production system in Gansu, with the aim of identifying possible pathways to reducing energy consumption and mitigating environmental impacts in cash tree crop production.
Materials and Methods
The study was conducted in wolfberry plantations in the full bearing period in the irrigated area of Jingtai County (103°33′–104°43′ E, 36°43′–37°28′ N) in northern Gansu Province, northwest China in 2013–2014. Jingtai County is one of the main wolfberry producers in Gansu. The region has a dry continental climate with an average annual temperature of about 8.6 °C, a maximum temperature of 38.6 °C in July and a minimum temperature of −27.3 °C in January. Annual rainfall is ~180 mm, of which 90% falls between April and September.
The main field management activities involved in wolfberry planting in Jingtai, Gansu, China
Brief frequency or intensity description
Beginning of March
Apply sheep manurea (N = 0.65%, P2O5 = 0.47%, K2O = 0.21%) by spade
Beginning of March, beginning of May, be gaining of June, middle of July.
Apply chemical fertilizers by spade
End of May, end of June, middle of July
Spray KH2PO4 with tricycle driven sprayer
Pruning (winter, spring, and summer)
Beginning of December to end of March, middle to end of May, and end of May to end of June
Pruning with special scissors with heavy winter pruning
Before middle of May
By tiller rotary
After middle of May
Spray herbicides by sprayer manually
After end of April to end of October
8 times per year
Spray chemical pesticides 6 times with tricycle driven sprayer
Middle of June to beginning of September
Fruit air drying
The investigation was carried out in 52 household farms, selected with the simple random sampling method (Fan et al. 2016) in Jingtai’s wolfberry planting region. Data on farm practices, inputs, and consumption of resources at each stage of the production chain were collected with a household survey questionnaire via face–face interviews. In addition, information was also collected from local Forestry Bureau, Forestry and Agricultural Technical Extension Stations and Agricultural Machinery Service.
The fruit yield and pruning wood were designated as the energy output. The energy inputs included human labor, machinery, diesel fuel, chemical fertilizers, pesticides, electricity, and irrigation water. Input energy in wolfberry production systems can be divided into direct, indirect, and renewable and nonrenewable energies. Direct energy in the study system involved human labor, diesel fuel, water for irrigation, and electricity. Indirect energy included chemical fertilizer, manure, pesticide, machinery, and tools. Also, renewable energy resources were human labor, water for irrigation, and manure and nonrenewable energy resources were electricity, chemical fertilizer, diesel fuel, pesticide, machinery, and tools.
Energy equivalents of inputs and outputs
Inputs and output
Energy equivalent (MJ unit−1)
1. Human labor
(Taylor et al. 1993)
(Liu et al. 2010a)
(b) Rotary tiller
(c) Agricultural tricycle
3. Diesel fuel
4. Chemical fertilizer
(Yin et al. 1998)
(a) Nitrogen (N)
(b) Phosphate (P2O5)
(c) Potassium (K2O)
(Liu et al. 2010a)
(Liu et al. 2010a)
6. Farmyard manure
(Liu et al. 2010a)
8. Water for irrigation
(Rajaeifar et al. 2014)
9. Tools (scissors, hoes, spades, etc.)
(Liu et al. 2010a)
(Xu et al. 2007)
Gaseous emissions (g) per unit of chemical sources and their global warming potential (GWP)
1. Diesel (L)
Yang et al. (2014)
2. Nitrogen fertilizer (kg)
Yang et al. (2014)
3. Phosphate (P2O5) (kg)
Yang et al. (2014)
4. Potassium (K2O) (kg)
Yang et al. (2014)
5. Electricity (kW h)
Yang et al. (2014)
GWP CO2 equivalence factor
Yang et al. (2014)
Results and Discussion
Energy Input–Output Analysis in Wolfberry Production
Energy inputs, outputs, and the ratio in wolfberry production systems
Inputs and output (unit)
Quantity per unit area (ha)
Total energy equivalents
1. Human labor (h)
2. Machinery (kg)
3. Diesel fuel (L)
4. Chemical fertilizer (kg)
5. Farmyard manure (kg)
6. Chemicals (kg)
7. Electricity (kW h)
8. Water for irrigation (M3)
9. Tools (scissors, hoes, spades, etc.)
Total input energy
Total output energy
Of the fertilizer energy input, the share of nitrogen fertilizer was the highest (8.48%), incurred by heavy use and high embodied energy intensity; phosphate the second (2.83%), and potassium the third (0.06%). Nitrogen application makes up the highest share in the fertilizers energy input in apricot production in Turkey (Esengun et al. 2007). Similar trends have also been reported for pistachio, orange, and peach production respectively (Külekci and Aksoy 2013; Ozkan et al. 2004; Ghatrehsamani et al. 2016).
In terms of the chemicals energy input, the share of pesticides use was the highest (4.64%) and herbicides input the second (0.81%). A higher share of pesticides in the total input energy is also found in peach production system in Turkey (Yildiz et al. 2016).
Human labor, a renewable source of energy, was in the fourth place. Both fruit harvest and pruning consist of the bulk of the labor energy input with fruit harvest accounting for 60% and pruning for 13%, respectively, in wolfberry production systems in Jingtai region (Wang et al. 2015). The highest use of human labor is also found in harvesting (56%) and pruning operations (23%) in apple production in Iran (Rafiee et al 2010) as well as in fruit harvest (46%) in walnut production systems in Turkey (Gundogmus 2013).
The wolfberry fruit yield was 4500 kg ha−1 on average and total brushwood pruned was 4560 kg ha−1 in the production system. Accordingly, their energy equivalents were 82,620 and 84,268.8 MJ ha−1, respectively. Total energy output was calculated for both fruit and trimmings energy equivalents. Pruning is an important part of a wolfberry production system with a view to gaining a stable and high yield. Pruned wood is a byproduct of wolfberry planting, used as farm household fuel wood in the wolfberry planting area.
Energy Use Indicator Analysis in Wolfberry Production Systems
Energy indices in wolfberry planting
Consumed energy intensity
Produced energy intensity
Energy use efficiency
Total energy input in the form of direct, indirect, renewable, and nonrenewable for wolfberry production
Quantity (MJ ha−1)
Total energy input
GHG Emissions and Global Warming Potential (GWP)
Gaseous emissions (kg ha−1) from chemical sources and their GWP in wolfberry production system
Total GWP (kg CO2 eq)
1. Diesel (L)
2. N Fertilizer (kg)
3. Phosphate (P2O5) (kg)
4. Potassium (K2O) (kg)
5. Electricity (kW h)
Total GHG (kg)
Total GWP (kg CO2 eq)
In the wolfberry production system, the production of wolfberry fruits would cause GWP generation of 5.78 kg CO2 eq kg−1, 2.6 kg CO2 eq m−2, 0.08 kg CO2 eq MJ−1 by input energy, or 0.16 kg CO2eq MJ−1 of energy output. The production of 1 kg of almonds generates 1.5 kg CO2 eq emissions in California, the USA (Kendall et al. 2015). Pergola et al (2013) reported that the GWP of conventional and organic lemon as well as orange production were 0.12, 0.04, 0.13, and 0.04 kg CO2 eq kg−1, respectively, in Sicily, Italy. GWPs for organic and conventional orange production on small farms (<75 ha) are 0.084 and 0.112 CO2 eq kg−1, respectively, in Brazil (Knudsen et al. 2011). GHG emissions for truly efficient and inefficient orange orchards are 0.075, 0.0939, and 0.126 kg CO2 eq m−2, respectively, in Iran (Nabavi-Pelesaraei et al. 2014) while that for apple production system is 0.26 kg CO2 eq m−2 in Switzerland (Mouron et al. 2006). In addition, Yousefi et al (2015) reported GWP generation of 1.67 kg kg−1, 1.17 kg m−2, and 0.19 kg CO2 eq MJ−1 of input energy in irrigated wheat production systems and 0.37 kg kg−1, 0.07 kg m−2, and 0.05 kg CO2 eq MJ−1 by input energy in rain-fed wheat production as well. Sugar beet production has a GWP generation of 0.024 ton CO2 eq ton−1 clean beets harvested in the UK, while it has been estimated to be between 0.174 and 0.093 ton CO2 eq ton−1 winter wheat grain in Europe (Tzilivakis et al. 2005). Clearly, the wolfberry production system is not efficient in the use of energy and resources.
Pathways for Improving Energy use and Abating GHG Emissions
The threat of climate change has called for the reorientation of development direction. Low carbon agriculture is one of the key sectors to achieve transformation towards low carbon growth and the shift to low carbon farming is a critical step in this connection.
From a policy perspective, innovative policy strategies should be formulated to underpin green growth initiatives. First, smallholder farmland transfer (concentration) should be encouraged through cooperatives, companies, and family farms for scale production. Large farms (>5 ha) uses less chemical fertilizer and consume lower energy for irrigation while the total energy output is higher compared with small farms (<1 ha) and medium farms (1–5 ha) (Pishgar-Komleh et al. 2012). Second, credit (small credit and low interest credit), tax breaks, and subsidies are needed to encourage the shift to low carbon farming.
Efficient transfer of knowledge to farmers through innovative extension systems with the combination of top-down and bottom-up pathways should be carried out and research deliver robust and cost-effective technologies; nonetheless farmers’ involvement in them is particularly important.
A seasonal gradient water pricing system, consisting of a basic quota price based on the crop water requirement for the growing season as well as an escalating pricing mechanism for the nongrowing season, should be in place, to leverage substantial water saving.
Greater priority should be given to irrigation for GHG emissions reduction. Irrigation is a carbon-intensive operation. Batty and Keller (1980) reported that energy required for surface irrigation was 3184 (MJ ha−1) for 0 m lift, 56,250 (MJ ha−1) for 50 m lift and 109,317 (MJ ha−1) for 100 m lift. Increasing irrigation efficiency is vital in reducing GHG emissions and raising energy productivity in wolfberry production in the Jingtai region. Currently extravagant water use for irrigation leads to a lot of water wasted and in turn high electricity consumption for lifting water from the Yellow River. Low irrigation water use efficiency results from inefficient irrigation methods (flooding), high irrigation quotas, and an irrational water pricing mechanism (Wang et al. 2012). New irrigation techniques, for instance, small tube, drip, subsurface drip, etc. should be encouraged by precision technological extension and incentives. Moreover, the use of crop residue and gravel mulching provides another alternative to reduce evaporation from the soil surface, thus, raising water use efficiency and potentially increasing wolfberry yields (Zeng et al. 2013).
For agro-chemicals, synthetic fertilizers in particular nitrogenous fertilizer are a principal source of CO2 and N2O emissions (Lal 2004). Further, embodied fossil fuel carbon associated with nitrogen fertilizer accounts for one of the largest energy inputs to agriculture. The chemical fertilizer use rate in Gansu is close to that of developed countries, while the effective utilization rate is about 30% (Gao 2008). Hence nitrogen fertilizer is a top priority target for GHG reduction. Efforts should be directed to enhance nitrogen fertilizer use efficiency, reducing reliance on chemical fertilizers, and optimizing application rates without negatively affecting productivity and soil fertility. Fertilizer application based on soil nutrient diagnosis, precision placement, and appropriate timing of fertilization (for example, through fertigation by modern irrigation technology), farm manure, N-fixing legume crops, biogas residue, etc. are recommended.
In the wolfberry systems considered in this study, the largest share of energy inputs was electricity consumption (68.52%), related to lifting water for irrigation, followed by fertilizer use (11.37%) and chemicals (5.45%). Energy ratio was 0.52 with inclusion of pruning wood and the energy productivity was low (0.014 kg MJ−1). Direct energy inputs were much greater than indirect energy consumption and nonrenewable energy use far larger than the renewable energy input.
Total GHG emissions were 25,882.72 kg ha−1 with CO2 being overwhelming. And total GWP was 26,018.64 kg CO2 eq ha−1 with the highest share coming from electricity consumption for irrigation. The emission of CO2 contributed most to the GWP.
The production system highly depends on nonrenewable energy (88.52%) associated with electricity consumption for irrigation, fertilization, and biocide use and these operations are C intensive, intensifying GHG emissions.
Irrigation consumes a large amount of energy due to backward irrigation methods, mainly flood irrigation and broader border irrigation. Furthermore the water pricing system leads high irrigation quotas, as a consequent, contributing to increased GHG emissions.
A range of options can be employed to reduce the rate of nonrenewable energy use and mitigate environmental burdens, including conversion to low carbon farming, decreasing nonrenewable energy inputs, and increasing performance of nonrenewable energy inputs. Policy initiatives, including smallholder farmland transfer (concentration) for scale production, credits, tax breaks, and subsidies are strongly recommended to underpin GHG reductions. Efficient transfer of knowledge to farmers and robust and cost-effective technologies formulated by research are essential as well. Innovative water pricing systems, improvement of irrigation efficiency by the adoption of new techniques and optimized irrigation norms are crucial. In addition, greater priority should be given to judicious use of chemical fertilizers and biocides with particular attention to reducing the use of synthetic N fertilizers.
Funding for this work was provided by the Carbon Benefits Project (CBP) (Sub-award No. G-4280-3), a Global Environmental Facility (GEF) co-financed project and by Natural Science Foundation of China (Grant No. 31660232). We thank Dr William Critchley (Sustainable Land Management Associates Ltd) for valuable comments.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of Interest.
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