Loading [Contrib]/a11y/accessibility-menu.js
Research article Special Issues

A systematic literature review of quantitative models for sustainable supply chain management

  • Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws and global agreements, have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers and practitioners. The main objective of this study is to provide a synthesis of the key elements of the quantitative model offerings that use sustainability indicators in the design and management of forward supply chains. To achieve this objective, we developed a systematic literature review that includes seventy articles published during the last decade in peer-reviewed journals in English language. In addition a 4 W's analysis (When, Who, What, and Where) is applied and three structural dimensions are defined and grouped by categories: Supply chain management, modeling and sustainability. As part of the results we evidenced a continuous growth in the scientific production of this type of articles, with a predominance of deterministic mathematical programming models with an environmental economic perspective. Finally, we identified research gaps, highlighting the lack of integral inclusion of a life cycle analysis in the design of supply chain networks.

    Citation: Pablo Flores-Sigüenza, Jose Antonio Marmolejo-Saucedo, Joaquina Niembro-Garcia, Victor Manuel Lopez-Sanchez. A systematic literature review of quantitative models for sustainable supply chain management[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2206-2229. doi: 10.3934/mbe.2021111

    Related Papers:

    [1] Janice Liang, Travis Reynolds, Alemayehu Wassie, Cathy Collins, Atalel Wubalem . Effects of exotic Eucalyptus spp. plantations on soil properties in and around sacred natural sites in the northern Ethiopian Highlands. AIMS Agriculture and Food, 2016, 1(2): 175-193. doi: 10.3934/agrfood.2016.2.175
    [2] Dhanya Praveen, Ramachandran Andimuthu, K. Palanivelu . The urgent call for land degradation vulnerability assessment for conserving land quality in the purview of climate change: Perspective from South Indian Coast. AIMS Agriculture and Food, 2016, 1(3): 330-341. doi: 10.3934/agrfood.2016.3.330
    [3] Hans-Georg Schwarz-v. Raumer, Elisabeth Angenendt, Norbert Billen, Rüdiger Jooß . Economic and ecological impacts of bioenergy crop production—a modeling approach applied in Southwestern Germany. AIMS Agriculture and Food, 2017, 2(1): 75-100. doi: 10.3934/agrfood.2017.1.75
    [4] Muhammad Rendana, Wan Mohd Razi Idris, Sahibin Abdul Rahim, Zulfahmi Ali Rahman, Tukimat Lihan, Habibah Jamil . Reclamation of acid sulphate soils in paddy cultivation area with organic amendments. AIMS Agriculture and Food, 2018, 3(3): 358-371. doi: 10.3934/agrfood.2018.3.358
    [5] Boris Boincean, Amir Kassam, Gottlieb Basch, Don Reicosky, Emilio Gonzalez, Tony Reynolds, Marina Ilusca, Marin Cebotari, Grigore Rusnac, Vadim Cuzeac, Lidia Bulat, Dorian Pasat, Stanislav Stadnic, Sergiu Gavrilas, Ion Boaghii . Towards Conservation Agriculture systems in Moldova. AIMS Agriculture and Food, 2016, 1(4): 369-386. doi: 10.3934/agrfood.2016.4.369
    [6] María Concepción Ramos . Soil losses in rainfed Mediterranean vineyards under climate change scenarios. The effects of drainage terraces.. AIMS Agriculture and Food, 2016, 1(2): 124-143. doi: 10.3934/agrfood.2016.2.124
    [7] W. Mupangwa, S. Walker, E. Masvaya, M. Magombeyi, P. Munguambe . Rainfall risk and the potential of reduced tillage systems to conserve soil water in semi-arid cropping systems of southern Africa. AIMS Agriculture and Food, 2016, 1(1): 85-101. doi: 10.3934/agrfood.2016.1.85
    [8] Mark D. McDonald, Katie L. Lewis, Glen L. Ritchie, Paul B. DeLaune, Kenneth D. Casey, Lindsey C. Slaughter . Carbon dioxide mitigation potential of conservation agriculture in a semi-arid agricultural region. AIMS Agriculture and Food, 2019, 4(1): 206-222. doi: 10.3934/agrfood.2019.1.206
    [9] Jon Hellin, Santiago López Ridaura . Soil and water conservation on Central American hillsides: if more technologies is the answer, what is the question?. AIMS Agriculture and Food, 2016, 1(2): 194-207. doi: 10.3934/agrfood.2016.2.194
    [10] Emilio J. González-Sánchez, Amir Kassam, Gottlieb Basch, Bernhard Streit, Antonio Holgado-Cabrera, Paula Triviño-Tarradas . Conservation Agriculture and its contribution to the achievement of agri-environmental and economic challenges in Europe. AIMS Agriculture and Food, 2016, 1(4): 387-408. doi: 10.3934/agrfood.2016.4.387
  • Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws and global agreements, have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers and practitioners. The main objective of this study is to provide a synthesis of the key elements of the quantitative model offerings that use sustainability indicators in the design and management of forward supply chains. To achieve this objective, we developed a systematic literature review that includes seventy articles published during the last decade in peer-reviewed journals in English language. In addition a 4 W's analysis (When, Who, What, and Where) is applied and three structural dimensions are defined and grouped by categories: Supply chain management, modeling and sustainability. As part of the results we evidenced a continuous growth in the scientific production of this type of articles, with a predominance of deterministic mathematical programming models with an environmental economic perspective. Finally, we identified research gaps, highlighting the lack of integral inclusion of a life cycle analysis in the design of supply chain networks.



    1. Introduction

    Central Asia comprises the five independent republics Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. Six of the agro-climatic zones in this vast region occupy about 90% of the entire region. Two of the agro-climatic zones (Semi-arid-cold winter-warm summer, and Arid-cold winter-warm summer) alone occupy up to 60% (Table 1). The climate in Central Asia is mostly arid and semi-arid, and strongly continental, with long, hot summers and short, cold winters. Average annual precipitation, which is concentrated in winter and spring, is about 270 mm, but varies from 80-150 mm in the arid regions to 600 to 800 mm in the semi-arid mountainous zones. The land area of the five countries covers about 393 M ha (Table 2).

    Table 1. Main agro-climatic zones and extent of land area under CA. (Adapted from [4])
    Agro-climatic zones§Total area, haArea planted with elements of CA, haDescription of the elementsSource
    Semi-arid, cold winter, warm summer151,387,76013,700,000§Including 2,100,000 ha of no-tillage, i.e. direct seeding of spring wheat and barleyMinistry of Agriculture of Kazakhstan
    Semi-arid, cold winter26,419,800
    Sub-humid, cold winter23,617,700
    Arid, cold winter, warm summer123,027,520< 700,000§Conservation tillage, sowing of winter wheat into standing cotton Ministry of Agriculture and Water Resources of Uzbekistan, Ministry of Agriculture of Tajikistan and Ministry of Agriculture and Melioration of the Kyrgyz Republic
    Arid, cool winter, warm summer19,572,560
    Semi-arid, cool winter, warm summer5,991,600
    §for detailed description of the agro-climatic zones, see [4] and [5].
     | Show Table
    DownLoad: CSV
    Table 2. Land resources, population and various agricultural indicators of five Central Asian countries.
    CountryTotal territory (M ha)Land area (M ha)Cropland (M ha)Irrigated land (M ha) Rainfed land (M ha) Population (M)% rural populationPer capita cropland (ha)% Agric. GDP
    Kazakhstan272.5269.7241.622.117.0142.81.415.3
    Kyrgyzstan20.019.21.41.10.35.9363.40.2325.8
    Tajikistan14.214.00.90.70.28.6171.40.1019.8
    Turkmenistan48.847.01.81.80.05.4172.00.3322.1
    Uzbekistan44.742.54.94.30.531.0063.50.1519.4
    Total 400.3392.7339.523.168.05
    Mean62.60.489.9
    Source: [6,7,8,9,10]
     | Show Table
    DownLoad: CSV

    Mikhalev and Reimov postulated that Central Asia’s drylands are to be regarded as dry steppe, semi-desert, desert, and salt marshes, which are known to be vulnerable to different kinds of degradation including soil degradation (here defined as a loss of fertility, or increase in salinization or waterlogging), degradation of pastures (due to overgrazing and excessive agricultural and firewood harvest), degradation of forests (due to illegal logging, fires, grazing, erosion), and erosion, landslides, and mudflows [1]. Extensive and intensive land use during the Soviet Union period (1924-1990) and disorganized land management systems introduced after the collapse of the Soviet Union (1991) worsened land degradation in Central Asia. Mono-cropping and a production strategy aimed at increasing the production of agricultural commodities without considering environmental consequences have been listed as major causes of past and on-going land degradation [2,3].

    Gupta et al. argued that during the post-Soviet period, the three primary causes of land degradation included the (i) mismanagement and over-use of natural resources, (ii) insufficiency of economic infrastructures and market mechanisms, and (iii) insufficient development of capacity and weak inter-sector coordination [11]. Despite the control of areas by governmental agencies during Soviet reign, the on-going land degradation could not be stopped and therefore remained high on the political agendas of the countries in Central Asia after independence.

    Areas under land degradation are wide spread in the arid and semi-arid zones of the Central Asian countries, and comprising over 80% of the agricultural area [12]. Some 68% of the agricultural land in the region is degraded due to erosion and increased salinity [3] (Table 3). The degradation of agricultural land in Kazakhstan and Uzbekistan has amounted to 73% and 44%, respectively, mainly caused by increased soil salinity, erosion and loss of vegetation cover. Most of the land resources in Kyrgyzstan and Tajikistan are prone to erosion due to the high proportion (above 90%) of mountainous areas in these two countries (Table 3). The types of degradation within a country vary according to land use type. However, the largest portion of the degraded land is in response to improper farming practices [3]. In particular, the numerous soil tillage practices, which invert the soil using heavy machinery with high ground pressures, agricultural practices that neglect to protect the soil surface, and the insufficient supply of organic material to the soil has resulted in increased soil erosion, decrease of topsoil depth and increase in salinity, with consequent losses in soil fertility and land value. In addition, poor irrigation management have resulted in soil degradation due to waterlogging (Table 3).

    Table 3. Soil degradation in Central Asia, %.
    Type of soil degradationPercent of Agricultural land
    KazakhstanKyrgyzstanTajikistanTurkmenistanUzbekistan
    Erosion17.2*85.0*75.0*8.9*11.6*
    Waterlogging8.7**1.0**3.6**3.2**8.7**
    Salinity47.7**0.5**5.0**19.1**24.4**
    Total73.6**86.5**83.6**31.2**44.7**
    *[13] **[3]
     | Show Table
    DownLoad: CSV

    The on-going degradation of soil resources in Central Asia is not only widespread, but represents a direct threat to the productive capacity and sustainability of the agricultural production base in the region. Substantial improvements in soil management are therefore direly needed to counter these threats.

    Conservation Agriculture (CA) has the potential to provide various tools to combat soil degradation as well as raise productivity and resilience, and reduce production costs [5,14,15,16]. Conservation Agriculture rests on three interlinked principles: (i) no or minimal mechanical disturbance of the soil through no-till direct seeding to maintain the quality and productivity of the soil, which is at the base of all CA-based farming practices and systems; (ii) maintenance of a permanent soil mulch cover with for instance plant residues including stubbles or cover crops to improve infiltration, reduce water loss and erosion, protect the soil from harsh climate extremes, and serve as a substrate for soil microorganisms and fauna; and (iii) diversified cropping systems over time (rotations, sequences) and space (associations) to further strengthen the systems’ resilience against biotic and abiotic threats [16,17,18]. In this way, CA practices provide important benefits to the environment and the land user alike [18,19,20].

    2. Combating soil degradation with CA

    Originally, CA practices were promoted to combat soil degradation and erosion resulting from tillage that caused the destruction of soil structure and aggregate stability, deplete soil organic matter and soil biological health. In later years, CA helped to reduce production costs, and raise productivity (yield and efficiency) [17]. During the past 20 years, CA has spread across all continents and most agro-ecological zones, particularly in North and South America and in Australia, but more recently also in Asia, Africa and Europe. In 2013, CA was used globally on 155 M ha of annual cropland, corresponding to about 11% of global annual cropland [21,22]. About 50% of the CA area is located in developing countries.

    In Central Asia, the research on crop residue management under no-till and its effect on soil erosion is still in its infancy. Yet, a review of a wealth of literature from outside Central Asia, illustrates numerous benefits. Hence, CA has been shown to be an innovative approach that helps in reducing soil erosion, improving water use efficiency as well as soil quality and helps in increasing soil organic matter, decreasing energy use and above all improving crop and land productivity and in turn the income of (resource poor) farmers [22,24]. For instance, soil erosion in Brazil decreased from 3.4-8.0 t ha−1under conventional tillage to 0.4 t ha−1under CA, whilewater loss decreasedfrom approximately 990 to 170 t ha−1 [25,26]. The reduction in soil erosion led to enhanced surface and ground water quality whilst crop residues retention on the surface helps in holding soil particles in place and keeping any applied plant nutrients and pesticides on the field.

    The overarching experimental evidence from the many different production environments worldwide demonstrate that CA-based management can have both immediate (e.g. reduced production costs, reduced erosion, stabilized crop yield, and improved water productivity) and long-term benefits (e.g. higher soil organic matter contents and improved soil structure), although the magnitude of these benefits tends to be site and year specific depending on the nature of the initial status of land degradation and the prevailing yield level [27,28,29,30].

    Increasingly, CA is considered to be climate-smart also, because of its better adaptability to climate change, and as a means to reach a sustainable intensification of agricultural production with minimum negative impacts on the environment [31,32]. As such, CA is a means for the integration of ecological management with modern, scientific, agricultural production practices. This holistic embrace of knowledge, as well as the capacity of farmers to apply this knowledge, innovate and adjust to evolving local conditions, ensures the sustainability of those who practice CA. A major strength of CA is furthermore the option of a step-wise implementation by farmers of complementary, synergetic soil husbandry practices that build to a robust, cheaper, more productive and environmentally friendly farming system. Therefore, CA practices have an important role to fulfill in the production systems of Central Asia.

    3. Effect of CA on soil quality and land degradation problems

    Soil organic matter (SOM) dynamics: Worldwide evidence has also shown the main benefits of no-till or low soil disturbance tillage on soil organic matter and soil carbon (C) interactions. The maintenance of these important parameters for soil quality depends on a permanent soil mulch cover developed through crop residue retention or cover crops, which is one of the three main principles of CA. In CA in Central Asia, the use of cover crops is not fully developed yet, but crop residues including stubble are retained on the soil surface after harvest where they benefit soil properties and crops, as shown by numerous field investigations [33,34]. Keeping crop residues on the soil surface reduces soil losses, protects the soil from water and wind erosion, and adds organic matter to the soil both in the rainfed and irrigated agriculture conditions worldwide [35,36].

    In CA, no-till, direct seed drilling is the only mechanical operation causing disturbance to the soil surface. All other operations that are normally employed under “conventional tillage agriculture” in the rainfed areas of Kazakhstan such as sweep tillage, disking and harrowing, are thus not included in CA [2]. Intensive experiences with CA practices in Central Asia date from the year 2000 onward. The concept of CA within the irrigated areas of CA has taken some time to become accepted, which has delayed the experimentation and the documentation thereof.

    Organic matter is one of the major indicators of soil quality and biological health, which affects, among other factors, crop yield and the ability of soils to resist erosion. A number of researchers have investigated the impact of different tillage systems on soil organic matter (SOM). There is general agreement that no-till can increase SOM as shown in arid and semi-arid regions in and outside the Central Asia region. Hernanz et al. [37], for instance, conducted a long term experiment in a semi-arid area of Spain using different tillage methods and reported that under no-till with mulch cover, the SOM at a depth of 0-10 and 20 cm had higher organic contents compared to conventional tillage [37]. Numerous results from the irrigated areas of Central Asia showed that crop residue retention improves SOM and soil N content [38,39,40]. The CA practices examined in Central Asia increased SOM significantly with corresponding improvements in soil structure and greater soil moisture holding capacities [38,39].

    Most beneficial effects on soil physical properties reported due to plant residue retention were the positive influences on soil quality, decreasing soil bulk density, increasing soil moisture retention, and increasing biological activity of the soil. That is why a general preservation of crop residues, irrespective of its make-up, improved physiological and biological properties of the soil, which in turn significantly increased soil fertility [41]. More recently, the positive impact of no tillage and crop residue management on properties of a silty loam soil under irrigation in Uzbekistan was reported for a rotation of winter wheat and maize for two years followed by cotton for another two years [42].

    Soil salinization: The on-going soil salinization in the irrigated areas of Central Asia is predominantly caused by the capillary rise of the ground water. This is the major cause of the on-going cropland degradation, especially in the Aral Sea basin [43,44]. A mulching experiment with crop residues decreased soil salinity under the irrigated conditions of Uzbekistan [45]. Pulatov et al. [39] reported that after four years, a no-till CA system had the lowest soil salinity level of all practices tested, i.e., no tillage and residue retention, which influenced also the location and accumulation of salts by reducing evaporation and the upward salt transport in the soil [39].

    Soil erosion:Increasing the SOM content and maintaining crop residues on the soil surface also reduced wind erosion [47]. Depending on the amount of crop residues retained on the soil surface, soil erosion could be reduced to insignificant levels compared to the unprotected, intensively tilled exposed fields [48] and this benefit can be harnessed in Central Asia as well. Water erosion too enhances soil degradation in Central Asia, especially on hilly areas and under irrigated conditions. The effective CA practices showed for many years to constitute a promising set of improved and financially feasible methods of crop production, which concurrently reduced wind and water erosion [49]. The regularly occurring wet springs in much of northern Kazakhstan resulted in severe soil erosion of exposed soil surface in fallowed fields [50]. Although information about the effect of slopes is lacking, in general where they are considered to be long, they resulted in water accumulation in the lower parts and in increasing the velocities of runoff water. However, with crop residue retention, soil erosion could be reduced drastically on the cropped areas [50]. Nevertheless, water and wind erosion studies remain rare in Central Asia despite having been acknowledged as being a core reason for on-going soil degradation (Table 3). Based on the research findings and lessons learned from different agro-climatic regions, several remedies could be examined for adoption in the region.

    4. Crop yield under Conservation Agriculture practices

    Early research from similar semi-arid environments showed the yield enhancing effects on barley of reduced and zero tillage systems compared to conventional practices [51]. Crop yields after four years of permanent bed planting in North-western Uzbekistan was 20% higher with zero tillage system compared to the conventional tillage methods [52]. Although results from numerous findings of CA practices on crop yields have been mixed, in the end crop yield is a critical assessment criterion for farmers. Hence, more research needs to be directed towards yield and its parameters under irrigated agriculture. Similarly, for the rainfed areas, results have been promising, but still are sparse. For example, from 1992 to 1995, minimum soil disturbance tillage techniques were introduced and tested particularly in the northern, rainfed parts of Kazakhstan. Excellent results were obtained throughout the areas cultivated with minimized soil disturbance, resulting in both economic savings and increased crop yields [53].

    Many research results from the irrigated areas in Central Asia indicated that bed planting practices improved wheat yields, increased fertilizer efficiency, reduced herbicide use, saved seeds, reduced water demands (on average 30%), and reduced production costs by 25-35% [54,55,56]. According to the Ministry of Agriculture of Kazakhstan, CA and conservation tillage practices were applied on some 11.7 M ha (Table 3), which is 70% of the total area sown to wheat in Kazakhstan [57] (Figure 1). Consequently, the country harvested a record gross output of grain of 20 M t, corresponding to a yield of 1.7 t ha−1 [57]. Hence, CA practices may have contributed to these increased yields and output, although the area under full CA in Kazakhstan is only 2.1 M ha. Results from Tajikistan and Kyrgyzstan showed 25-38% higher wheat yields under raised bed and no-till planting conditions compared to the traditional, tillage-driven planting [54,56].

    Figure 1. Rainfed, no-till winter wheat in Kazakhstan (2008). Photo by Aziz Nurbekov.

    In addition to yield increases, seeding rates under CA in Kyrgyzstan could be reduced by 50% while irrigation water requirement could be lowered by 27% [54]. Similar results were reported in the irrigated conditions of Tajikistan [56]. On the other hand, Nurbekov et al. reported that the application rate of N had no significant effect on winter wheat yields in no-till and conventional systems in Uzbekistan [40]. The yields with 120 kg N ha−1 rates turned out to be as good as with 140 kg N ha−1 under conventional practices using mouldboard ploughs, while with no-till practices a slight increase in grain yields was observed with the higher N rates. Nurbekov et al. reported that winter wheat yields increased with no-till compared to conventional tillage system [40]. Sanginov and Khalikov, carriying out research on the planting of winter wheat before the harvest of cotton in Yavan and Gozimalik districts of Tajikistan, reported that wheat growth and development under no-tillage system resulted in savings of seed quantity and in increased yield [58]. The adoption of CA methods could thus bring about significant productivity and environmental benefits [41].

    So far, only Kazakhstan has issued supportive policies to introduce and spread CA practices and this has increased the area under CA-based practices from virtually none in 2001 to 2.1 M ha in 2013. The other four countries in Central Asia are only gradually moving towards the adoption of supportive policies on CA and in general, a wide-spread adoption of CA is still pending and would need more extension and research support [60].

    Permanent raised bed planting in Uzbekistan consists of raised beds that have been prepared and used during a previous season and subsequently used for growing the next crop (Figure 2). Over the last 20 years, Uzbekistan has been researching different ways of introducing grain crops into the existing crop rotations, which included cotton and alfalfa mainly, albeit predominantly during the Soviet Union epoch. However, since 1990s, winter wheat, previously grown under rainfed conditions only in Central Asia, is being cultivated also under irrigation. Research findings showed that a timely, no-till planting of winter wheat in standing cotton is a promising relay cropping practice. As a consequence, the area under this cotton-winter wheat relay cropping has now reached some 600,000 ha annually [59]. Several development projects in Central Asia, supported by the international donor community, currently include the promotion of CA with permanent raised beds system as part of their priority activities, but according to many these efforts need to be intensified [60].

    Figure 2. Permanent bed planted winter wheat in Uzbekistan (2012). Photo by Aziz Nurbekov.

    Hence, despite the numerous positive research results, CA is still not widely practices among the farming population in the irrigated areas of Central Asia. This is partly due to a predetermined mindset but also due to the relative complexity of CA practices compared to conventional tillage agricultural practices.

    5. Conclusions

    Current research evidence from the rainfed areas of Central Asia, shows that CA practices are promising to combat a series of flaws in the existing cropping systems. However, much less research evidence exists for the irrigated areas even though such research has introduced in all five Central Asian countries and while covering the heterogeneous local conditions. These preliminary research results, albeit limited to a few locations, show the potential for achieving similar, or even higher crop yields over time. The CA practices favoured, such as permanent no-till beds, showed their effectiveness in lowering the rate of land degradation caused by soil salinization. Research on CA practices and its role in combating the on-going water and wind erosion have not been placed high on the research agendas yet. However, the maintenance of a soil coverage by residues reduces wind and water erosion, increases water infiltration and storage capacity, which helps reducing crop water stress, improves soil quality and increases organic matter. These benefits are promising to the scientists in the first place, but not yet to farmers! The findings underscored furthermore that CA is not a single, uniformly applicable technology that can be immediately applied anywhere and in a standard manner. Rather, it represents a set of principles that encourage the formulation of locally adapted practices, approaches and methods, which need to be tested, evaluated and then adopted or implemented not only under various climatic setting but also while considering the socio-economic conditions. Hence, also socio-economic research has to be promoted for instance when addressing the residue management component since this needs to be packaged into an easily adoptable technology, acceptable to farmers. Finally, as is evidenced in Kazakhstan, encouraging policies are needed as well as an effective and functioning agricultural extension system, which only is in its infancy in most Central Asia countries.

    Further research is needed across the agro-climatic zones that should address in detail the effects of various types of CA crop rotations and mulch covers on weed management, on nutrients, pests and water management, on residue levels, sowing depths, dates and density, and on fertilizer and irrigation rates. Needless to repeat the importance of an impact assessment on livelihoods and environmental conditions including the potential of integrating trees and timber production, pastures and livestock into CA farming systems particularly with small-scale farmers.

    Conflict of interest

    All authors declare no conflict of interest.



    [1] S. M. Mirzapour Al-E-Hashem, H. Malekly, M. B. Aryanezhad, A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty, Int. J. Prod. Econ., 134 (2011), 28–42. doi: 10.1016/j.ijpe.2011.01.027
    [2] A. Baghalian, S. Rezapour, R. Z. Farahani, Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case, Eur. J. Oper. Res., 227 (2013), 199–215. doi: 10.1016/j.ejor.2012.12.017
    [3] I. Moon, Y. Jeong, S. Saha, Fuzzy Bi-Objective Production-Distribution Planning Problem under the Carbon Emission Constraint, Sustainability, 8 (2016), 798–815. doi: 10.3390/su8080798
    [4] Z. Xu, A. Elomri, S. Pokharel, F. Mutlu, The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models, Sustainability, 11 (2019), 3094. doi: 10.3390/su11113094
    [5] World Commission on Environment and Development, Our Common Future, Oxford University Press.
    [6] C. P. Tautenhain, A. P. Barbosa-Povoa, M. C. Nascimento, A multi-objective matheuristic for designing and planning sustainable supply chains, Comput. Ind. Eng., 135 (2019), 1203–1223. doi: 10.1016/j.cie.2018.12.062
    [7] R. Daghigh, M. S. Pishvaee, S. A. Torabi, Sustainable Logistics Network Design under Uncertainty, Sustainable Logistics and Transportation, Springer, Cham, 2017.
    [8] A. Chaabane, A. Ramudhin, M. Paquet, Design of sustainable supply chains under the emission trading scheme, Int. J. Prod. Econ., 135 (2012), 37–49. doi: 10.1016/j.ijpe.2010.10.025
    [9] J. Elkington, Partnerships from cannibals with forks: The triple bottom line of 21st century business, Environ. Qual. Manage., 8 (1988), 37–51.
    [10] A. Rajeev, R. K. Pati, S. S. Padhi, K. Govindan, Evolution of sustainability in supply chain management: A literature review, J. Cleaner Prod., 162 (2017), 299–314. doi: 10.1016/j.jclepro.2017.05.026
    [11] H. Gilani, H. Sahebi, A multi-objective robust optimization model to design sustainable sugarcane-to-biofuel supply network: the case of study, Biomass Convers. Biorefin., 2020 (2020), 1–22.
    [12] H. Min, I. Kim, Green supply chain research: Past, present, and future, Logist. Res., 4 (2012), 39–47. doi: 10.1007/s12159-012-0071-3
    [13] C. L. Martins, M. V. Pato, Supply chain sustainability: A tertiary literature review, J. Cleaner Prod., 225 (2019), 995–1016. doi: 10.1016/j.jclepro.2019.03.250
    [14] H. G. Resat, B. Unsal, A novel multi-objective optimization approach for sustainable supply chain: A case study in packaging industry, Sustainable Prod. Consumption, 20 (2019), 29–39. doi: 10.1016/j.spc.2019.04.008
    [15] X. Bai, Y. Liu, Robust optimization of supply chain network design in fuzzy decision system, J. Intell. Manuf., 27 (2016), 1131–1149. doi: 10.1007/s10845-014-0939-y
    [16] K. Devika, A. Jafarian, V. Nourbakhsh, Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques, Eur. J. Oper. Res., 235 (2014), 594–615. doi: 10.1016/j.ejor.2013.12.032
    [17] Z. Zhang, A. Awasthi, Modelling customer and technical requirements for sustainable supply chain planning, Int. J. Prod. Res., 52 (2014), 5131–5154. doi: 10.1080/00207543.2014.899717
    [18] K. Govindan, H. Soleimani, D. Kannan, Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future, Eur. J. Oper. Res., 240 (2015), 603–626. doi: 10.1016/j.ejor.2014.07.012
    [19] D. M. Lambert, M. G. Enz, Issues in Supply Chain Management: Progress and potential, Ind. Mark. Manage., 62 (2017), 1–16. doi: 10.1016/j.indmarman.2016.12.002
    [20] C. J. C. Jabbour, A. B. L. de Sousa Jabbour, J. Sarkis, Unlocking effective multi-tier supply chain management for sustainability through quantitative modeling: Lessons learned and discoveries to be made, Int. J. Prod. Econ., 217 (2019), 11–30. doi: 10.1016/j.ijpe.2018.08.029
    [21] Q. Zhang, N. Shah, J. Wassick, R. Helling, P. Van Egerschot, Sustainable supply chain optimisation: An industrial case study, Comput. Ind. Eng., 74 (2014), 68–83. doi: 10.1016/j.cie.2014.05.002
    [22] B. Mota, M. I. Gomes, A. Carvalho, A. P. Barbosa-Povoa, Sustainable supply chains: An integrated modeling approach under uncertainty, Omega, 77 (2018), 32–57. doi: 10.1016/j.omega.2017.05.006
    [23] M. Pagell, A. Shevchenko, Why Research in Sustainable Supply Chain Management Should Have no Future, J. Supply Chain Manage., 50 (2014), 44–55.
    [24] S. Seuring, M. Müller, From a literature review to a conceptual framework for sustainable supply chain management, J. Cleaner Prod., 16 (2008), 1699–1710. doi: 10.1016/j.jclepro.2008.04.020
    [25] M. Brandenburg, K. Govindan, J. Sarkis, S. Seuring, Quantitative models for sustainable supply chain management:Developments and directions, Eur. J. Oper. Res., 233 (2014), 299–312. doi: 10.1016/j.ejor.2013.09.032
    [26] P. Ghadimi, C. Wang, M. K. Lim, Sustainable supply chain modeling and analysis: Past debate, present problems and future challenges, Resour. Conserv. Recycl., 140 (2019), 72–84. doi: 10.1016/j.resconrec.2018.09.005
    [27] A. Fink, Conducting Research Literature Reviews: From the Internet to Paper, Ucla edition, SAGE Publications, Inc., Los Angeles, 2014.
    [28] A. Cipriani, J. Geddes, Comparison of systematic and narrative reviews: The example of the atypical antipsychotics, Epidemiol. Psychiatr. Sci., 12 (2003), 146–153. doi: 10.1017/S1121189X00002918
    [29] J. Klewitz, E. G. Hansen, Sustainability-oriented innovation of SMEs: A systematic review, J. Cleaner Prod., 65 (2014), 57–75. doi: 10.1016/j.jclepro.2013.07.017
    [30] F. Jia, L. Zuluaga-Cardona, A. Bailey, X. Rueda, Sustainable supply chain management in developing countries: An analysis of the literature, J. Cleaner Prod., 189 (2018), 263–278. doi: 10.1016/j.jclepro.2018.03.248
    [31] R. U. Khalid, S. Seuring, P. Beske, A. Land, S. A. Yawar, R. Wagner, Putting sustainable supply chain management into base of the pyramid research, Supply Chain Manage., 20 (2015), 681–696. doi: 10.1108/SCM-06-2015-0214
    [32] R. Dubey, A. Gunasekaran, S. J. Childe, T. Papadopoulos, S. F. Wamba, World class sustainable supply chain management: Critical review and further research directions, Int. J. Logist. Manage., 28 (2017), 332–362. doi: 10.1108/IJLM-07-2015-0112
    [33] T. Rebs, M. Brandenburg, S. Seuring, System dynamics modeling for sustainable supply chain management: A literature review and systems thinking approach, J. Cleaner Prod., 208 (2019), 1265–1280. doi: 10.1016/j.jclepro.2018.10.100
    [34] F. Jia, T. Zhang, L. Chen, Sustainable supply chain Finance:Towards a research agenda, J. Cleaner Prod., 243 (2020), 118680. doi: 10.1016/j.jclepro.2019.118680
    [35] S. Seuring, S. Gold, Conducting content-analysis based literature reviews in supply chain management, Supply Chain Manage., 17 (2012), 544–555. doi: 10.1108/13598541211258609
    [36] Supply Chain Council, Supply Chain Operations Reference Model Revision 11.0, Technical report, 2012. Available from: www.supply-chain.org.
    [37] S. Validi, A. Bhattacharya, P. J. Byrne, A solution method for a two-layer sustainable supply chain distribution model, Comput. Oper. Res., 54 (2015), 204–217. doi: 10.1016/j.cor.2014.06.015
    [38] D. Broz, G. Durand, D. Rossit, F. Tohmé, M. Frutos, Strategic planning in a forest supply chain: a multigoal and multiproduct approach, Canadian J. For. Res., 47 (2017), 297–307. doi: 10.1139/cjfr-2016-0299
    [39] S. Coskun, L. Ozgur, O. Polat, A. Gungor, A model proposal for green supply chain network design based on consumer segmentation, J. Cleaner Prod., 110 (2016), 149–157. doi: 10.1016/j.jclepro.2015.02.063
    [40] N. Kafa, Y. Hani, A. El Mhamedi, Evaluating and selecting partners in sustainable supply chain network: a comparative analysis of combined fuzzy multi-criteria approaches, OPSEARCH, 55 (2018), 14–49. doi: 10.1007/s12597-017-0326-5
    [41] P. Ghadimi, F. Ghassemi Toosi, C. Heavey, A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain, Eur. J. Oper. Res., 269 (2018), 286–301. doi: 10.1016/j.ejor.2017.07.014
    [42] F. Niakan, A. Baboli, V. Botta-Genoulaz, R. Tavakkoli-Moghaddam, J. P. Camapgne, A multi-objective mathematical model for green supply chain reorganization, IFAC Proc. Vol., 46 (2013), 81–86.
    [43] A. T. Espinoza Pérez, P. C. Narváez Rincón, M. Camargo, M. D. Alfaro Marchant, Multiobjective optimization for the design of phase Ⅲ biorefinery sustainable supply chain, J. Cleaner Prod., 223 (2019), 189–213. doi: 10.1016/j.jclepro.2019.02.268
    [44] H. Ren, W. Zhou, M. Makowski, H. Yan, Y. Yu, T. Ma, Incorporation of life cycle emissions and carbon price uncertainty into the supply chain network management of PVC production, Ann. Oper. Res., 2019 (2019).
    [45] K. Govindan, A. Jafarian, V. Nourbakhsh, Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic, Comput. Oper. Res., 62 (2015), 112–130. doi: 10.1016/j.cor.2014.12.014
    [46] M. Soysal, J. M. Bloemhof-Ruwaard, J. G. Van Der Vorst, Modelling food logistics networks with emission considerations: The case of an international beef supply chain, Int. J. Prod. Econ., 152 (2014), 57–70. doi: 10.1016/j.ijpe.2013.12.012
    [47] Y. Huang, F. Xie, Multistage Optimization of Sustainable Supply Chain of Biofuels, Transp. Res. Rec., 2502 (2015), 89–98. doi: 10.3141/2502-11
    [48] T. Vafaeenezhad, R. Tavakkoli-Moghaddam, N. Cheikhrouhou, Multi-objective mathematical modeling for sustainable supply chain management in the paper industry, Comput. Ind. Eng., 135 (2019), 1092–1102. doi: 10.1016/j.cie.2019.05.027
    [49] K. Shaw, M. Irfan, R. Shankar, S. S. Yadav, Low carbon chance constrained supply chain network design problem: a Benders decomposition based approach, Comput. Ind. Eng., 98 (2016), 483–497. doi: 10.1016/j.cie.2016.06.011
    [50] A. Mohammed, Q. Wang, The fuzzy multi-objective distribution planner for a green meat supply chain, Int. J. Prod. Econ., 184 (2017), 47–58. doi: 10.1016/j.ijpe.2016.11.016
    [51] S. M. Mirzapour Al-E-Hashem, A. Baboli, Z. Sazvar, A stochastic aggregate production planning model in a green supply chain: Considering flexible lead times, nonlinear purchase and shortage cost functions, Eur. J. Oper. Res., 230 (2013), 26–41. doi: 10.1016/j.ejor.2013.03.033
    [52] L. E. Hombach, C. Büsing, G. Walther, Robust and sustainable supply chains under market uncertainties and different risk attitudes ȼ A case study of the German biodiesel market, Eur. J. Oper. Res., 269 (2018), 302–312. doi: 10.1016/j.ejor.2017.07.015
    [53] A. Rezaee, F. Dehghanian, B. Fahimnia, B. Beamon, Green supply chain network design with stochastic demand and carbon price, Ann. Oper. Res., 250 (2017), 463–485. doi: 10.1007/s10479-015-1936-z
    [54] C. W. Chen, Y. Fan, Bioethanol supply chain system planning under supply and demand uncertainties, Transp. Res. Part E, 48 (2012), 150–164. doi: 10.1016/j.tre.2011.08.004
    [55] Y. Tong, Model for evaluating the green supply chain performance under low-carbon agricultural economy environment with 2-tuple linguistic information, J. Intell. Fuzzy Syst., 32 (2017), 2717–2723. doi: 10.3233/JIFS-16802
    [56] F. Mohebalizadehgashti, H. Zolfagharinia, S. H. Amin, Designing a green meat supply chain network: A multi-objective approach, Int. J. Prod. Econ., 219 (2020), 312–327. doi: 10.1016/j.ijpe.2019.07.007
    [57] T. C. Kuo, M. L. Tseng, H. M. Chen, P. S. Chen, P. C. Chang, Design and Analysis of Supply Chain Networks with Low Carbon Emissions, Comput. Econ., 52 (2018), 1353–1374. doi: 10.1007/s10614-017-9675-7
    [58] E. Huang, X. Zhang, L. Rodriguez, M. Khanna, S. de Jong, K. C. Ting, et al., Multi-objective optimization for sustainable renewable jet fuel production: A case study of corn stover based supply chain system in Midwestern U.S., Renewable Sustainable Energy Rev., 115 (2019), 109403. doi: 10.1016/j.rser.2019.109403
    [59] R. Hosseinalizadeh, A. Arshadi Khamseh, M. M. Akhlaghi, A multi-objective and multi-period model to design a strategic development program for biodiesel fuels, Sustainable Energy Technol. Assess., 36 (2019), 100545. doi: 10.1016/j.seta.2019.100545
    [60] A. Tognetti, P. T. Grosse-Ruyken, S. M. Wagner, Green supply chain network optimization and the trade-off between environmental and economic objectives, Int. J. Prod. Econ., 170 (2015), 385–392. doi: 10.1016/j.ijpe.2015.05.012
    [61] F. You, L. Tao, D. J. Graziano, S. W. Snyder, Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input-output analysis, AIChE J., 58 (2012), 1157–1180. doi: 10.1002/aic.12637
    [62] R. Ortiz-Gutierrez, S. Giarola, F. Bezzo, Optimal design of ethanol supply chains considering carbon trading effects and multiple technologies for side-product exploitation, Environ. Technol., 34 (2013), 2189–2199. doi: 10.1080/09593330.2013.829111
    [63] Z. Ghelichi, M. Saidi-Mehrabad, M. S. Pishvaee, A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study, Energy, 156 (2018), 661–687. doi: 10.1016/j.energy.2018.05.103
    [64] Z. Sazvar, S. M. Mirzapour Al-E-Hashem, A. Baboli, M. R. Akbari Jokar, A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products, Int. J. Prod. Econ., 150 (2014), 140–154. doi: 10.1016/j.ijpe.2013.12.023
    [65] T. M. Choi, Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme, Comput. Oper. Res., 40 (2013), 2646–2655. doi: 10.1016/j.cor.2013.04.017
    [66] T. Yu-Chung, T. Vo-Van, L. Jye-Chyi, Y. Vincent, Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming, J. Cleaner Prod., 174 (2018), 1550–1565. doi: 10.1016/j.jclepro.2017.10.272
    [67] K. Boonsothonsatit, S. Kara, S. Ibbotson, B. Kayis, Development of a Generic decision support system based on multi-Objective Optimisation for Green supply chain network design (GOOG), J. Manuf. Technol. Manage., 26 (2015), 1069–1084. doi: 10.1108/JMTM-10-2012-0102
    [68] M. M. Saffar, G. Hamed Shakouri, J. Razmi, A new multi objective optimization model for designing a green supply chain network under uncertainty, Int. J. Ind. Eng. Comput., 6 (2015), 15–32.
    [69] S. Y. Balaman, H. Selim, A fuzzy multiobjective linear programming model for design and management of anaerobic digestion based bioenergy supply chains, Energy, 74 (2014), 928–940. doi: 10.1016/j.energy.2014.07.073
    [70] M. S. Pishvaee, J. Razmi, S. A. Torabi, An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain, Transp. Res. Part E, 67 (2014), 14–38. doi: 10.1016/j.tre.2014.04.001
    [71] H. Heidari-Fathian, S. H. R. Pasandideh, Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation, Comput. Ind. Eng., 122 (2018), 95–105. doi: 10.1016/j.cie.2018.05.051
    [72] H. Golpîra, E. Najafi, M. Zandieh, S. Sadi-Nezhad, Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk, Comput. Ind. Eng., 107 (2017), 301–312. doi: 10.1016/j.cie.2017.03.029
    [73] M. Jin, L. Song, Y. Wang, Y. Zeng, Longitudinal cooperative robust optimization model for sustainable supply chain management, Chaos Solitons Fractals, 116 (2018), 95–105. doi: 10.1016/j.chaos.2018.09.008
    [74] M. Sherafati, M. Bashiri, R. Tavakkoli-Moghaddam, M. S. Pishvaee, Supply chain network design considering sustainable development paradigm: A case study in cable industry, J. Cleaner Prod., 234 (2019), 366–380. doi: 10.1016/j.jclepro.2019.06.095
    [75] F. D. Mele, A. M. Kostin, G. Guillén-Gosálbez, L. Jiménez, Multiobjective model for more sustainable fuel supply chains. A case study of the sugar cane industry in argentina, Ind. Eng. Chem. Res., 50 (2011), 4939–4958. doi: 10.1021/ie101400g
    [76] Z. Chen, S. Andresen, A Multiobjective Optimization Model of Production-Sourcing for Sustainable Supply Chain with Consideration of Social, Environmental, and Economic Factors, Math. Probl. Eng., 2 (2014), 1–11.
    [77] S. Giarola, F. Bezzo, N. Shah, A risk management approach to the economic and environmental strategic design of ethanol supply chains, Biomass Bioenergy, 58 (2013), 31–51. doi: 10.1016/j.biombioe.2013.08.005
    [78] C. V. Valderrama, E. Santibanez-González, B. Pimentel, A. Candia-Véjar, L. Canales-Bustos, Designing an environmental supply chain network in the mining industry to reduce carbon emissions, J. Cleaner Prod., 254 (2020), 119688. doi: 10.1016/j.jclepro.2019.119688
    [79] S. D. Budiman, H. Rau, A mixed-integer model for the implementation of postponement strategies in the globalized green supply chain network, Comput. Ind. Eng., 137 (2019), 106054. doi: 10.1016/j.cie.2019.106054
    [80] M. Izadikhah, R. F. Saen, Assessing sustainability of supply chains by chance-constrained two-stage DEA model in the presence of undesirable factors, Comput. Oper. Res., 100 (2018), 343–367. doi: 10.1016/j.cor.2017.10.002
    [81] R. Das, K. Shaw, M. Irfan, Supply chain network design considering carbon footprint, water footprint, supplier's social risk, solid waste, and service level under the uncertain condition, Clean Technol. Environ. Policy, 22 (2020), 337–370. doi: 10.1007/s10098-019-01785-y
    [82] J. Jonkman, A. Kanellopoulos, J. M. Bloemhof, Designing an eco-efficient biomass-based supply chain using a multi-actor optimisation model, J. Cleaner Prod., 210 (2019), 1065–1075. doi: 10.1016/j.jclepro.2018.10.351
    [83] F. Barzinpour, P. Taki, A dual-channel network design model in a green supply chain considering pricing and transportation mode choice, J. Intell. Manuf., 29 (2018), 1465–1483. doi: 10.1007/s10845-015-1190-x
    [84] V. K. Manupati, S. J. Jedidah, S. Gupta, A. Bhandari, M. Ramkumar, Optimization of a multi-echelon sustainable production-distribution supply chain system with lead time consideration under carbon emission policies, Comput. Ind. Eng., 135 (2019), 1312–1323. doi: 10.1016/j.cie.2018.10.010
    [85] S. Elhedhli, R. Merrick, Green supply chain network design to reduce carbon emissions, Transp. Res. Part D, 17 (2012), 370–379. doi: 10.1016/j.trd.2012.02.002
    [86] R. Jamshidi, S. M. Fatemi Ghomi, B. Karimi, Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method, Sci. Iran., 19 (2012), 1876–1886. doi: 10.1016/j.scient.2012.07.002
    [87] K. Sari, A novel multi-criteria decision framework for evaluating green supply chain management practices, Comput. Ind. Eng., 105 (2017), 338–347. doi: 10.1016/j.cie.2017.01.016
    [88] M. Song, X. Cui, S. Wang, Simulation of land green supply chain based on system dynamics and policy optimization, Int. J. Prod. Econ., 217 (2019), 317–327. doi: 10.1016/j.ijpe.2018.08.021
    [89] G. Wang, A. Gunasekaran, Modeling and analysis of sustainable supply chain dynamics, Ann. Oper. Res., 250 (2017), 521–536. doi: 10.1007/s10479-015-1860-2
    [90] E. S. Nwe, A. Adhitya, I. Halim, R. Srinivasan, Green supply chain design and operation by integrating LCA and dynamic simulation, Comput. Aided Chem. Eng., 28 (2010), 109–114. doi: 10.1016/S1570-7946(10)28019-7
    [91] R. Das, K. Shaw, Uncertain supply chain network design considering carbon footprint and social factors using two-stage approach, Clean Technol. Environ. Policy, 19 (2017), 2491–2519. doi: 10.1007/s10098-017-1446-6
    [92] H. Kaur, S. P. Singh, R. Glardon, An Integer Linear Program for Integrated Supplier Selection: A Sustainable Flexible Framework, Global J. Flexible Syst. Manage., 17 (2016), 113–134. doi: 10.1007/s40171-015-0105-1
    [93] K. J. Wu, C. J. Liao, M. L. Tseng, K. K. S. Chiu, Multi-attribute approach to sustainable supply chain management under uncertainty, Ind. Manage. Data Syst., 116 (2016), 777–800. doi: 10.1108/IMDS-08-2015-0327
    [94] N. Ghani, G. Egilmez, M. Kucukvar, M. K. S. Bhutta, From green buildings to green supply chains: An integrated input-output life cycle assessment and optimization framework for carbon footprint reduction policy making, Manage. Environ. Qual., 28 (2017), 532–548. doi: 10.1108/MEQ-12-2015-0211
    [95] M. L. Tseng, M. K. Lim, K. J. Wu, Improving the benefits and costs on sustainable supply chain finance under uncertainty, Int. J. Prod. Econ., 218 (2019), 308–321. doi: 10.1016/j.ijpe.2019.06.017
    [96] A. Acquaye, T. Ibn-Mohammed, A. Genovese, G. A. Afrifa, F. A. Yamoah, E. Oppon, A quantitative model for environmentally sustainable supply chain performance measurement, Eur. J. Oper. Res., 269 (2018), 188–205. doi: 10.1016/j.ejor.2017.10.057
    [97] X. Ji, J. Wu, Q. Zhu, Eco-design of transportation in sustainable supply chain management: A DEA-like method, Transp. Res. Part D, 48 (2016), 451–459. doi: 10.1016/j.trd.2015.08.007
    [98] V. K. Sharma, P. Chandana, A. Bhardwaj, Critical factors analysis and its ranking for implementation of GSCM in Indian dairy industry, J. Manuf. Technol. Manage., 26 (2015), 911–922. doi: 10.1108/JMTM-03-2014-0023
    [99] B. He, Y. Liu, L. Zeng, S. Wang, D. Zhang, Q. Yu, Product carbon footprint across sustainable supply chain, J. Cleaner Prod., 241 (2019), 118320. doi: 10.1016/j.jclepro.2019.118320
    [100] O. Boutkhoum, M. Hanine, H. Boukhriss, T. Agouti, A. Tikniouine, Multi-criteria decision support framework for sustainable implementation of effective green supply chain management practices, SpringerPlus, 5 (2016), 664. doi: 10.1186/s40064-016-2233-2
    [101] H. Snyder, Literature review as a research methodology: An overview and guidelines, J. Bus. Res., 104 (2019), 333–339. doi: 10.1016/j.jbusres.2019.07.039
  • This article has been cited by:

    1. Baqir Lalani, Bassil Aleter, Shinan Kassam, Amyn Bapoo, Amir Kassam, Potential for Conservation Agriculture in the Dry Marginal Zone of Central Syria: A Preliminary Assessment, 2018, 10, 2071-1050, 518, 10.3390/su10020518
    2. Hasan Boboev, Utkur Djanibekov, Maksud Bekchanov, John P.A. Lamers, Kristina Toderich, Feasibility of conservation agriculture in the Amu Darya River Lowlands, Central Asia, 2019, 17, 1473-5903, 60, 10.1080/14735903.2018.1560123
    3. A. Kassam, T. Friedrich, R. Derpsch, Global spread of Conservation Agriculture, 2019, 76, 0020-7233, 29, 10.1080/00207233.2018.1494927
    4. Vadim Yapiyev, Zhanay Sagintayev, Vassilis Inglezakis, Kanat Samarkhanov, Anne Verhoef, Essentials of Endorheic Basins and Lakes: A Review in the Context of Current and Future Water Resource Management and Mitigation Activities in Central Asia, 2017, 9, 2073-4441, 798, 10.3390/w9100798
    5. Eldiiar Duulatov, Xi Chen, Gulnura Issanova, Rustam Orozbaev, Yerbolat Mukanov, Amobichukwu C. Amanambu, 2021, Chapter 2, 978-3-030-63508-4, 9, 10.1007/978-3-030-63509-1_2
    6. Yi Qin, Jiawen He, Miao Wei, Xixi Du, Challenges Threatening Agricultural Sustainability in Central Asia: Status and Prospect, 2022, 19, 1660-4601, 6200, 10.3390/ijerph19106200
    7. M Juliev, B Matyakubov, O Khakberdiev, X Abdurasulov, L Gafurova, O Ergasheva, U Panjiev, B Chorikulov, Influence of erosion on the mechanical composition and physical properties of serozems on rainfed soils, Tashkent province, Uzbekistan, 2022, 1068, 1755-1307, 012005, 10.1088/1755-1315/1068/1/012005
    8. Lazizakhon Gafurova, Mukhiddin Juliev, 2021, Chapter 5, 978-3-030-72223-4, 59, 10.1007/978-3-030-72224-1_5
    9. Yang Yu, Xi Chen, Ireneusz Malik, Malgorzata Wistuba, Yiguo Cao, Dongde Hou, Zhijie Ta, Jing He, Lingyun Zhang, Ruide Yu, Haiyan Zhang, Lingxiao Sun, Spatiotemporal changes in water, land use, and ecosystem services in Central Asia considering climate changes and human activities, 2021, 13, 1674-6767, 881, 10.1007/s40333-021-0084-3
    10. Mukhiddin Juliev, Lazizakhon Gafurova, Olimaxon Ergasheva, Makhsud Ashirov, Kamila Khoshjanova, Mirvasid Mirusmanov, 2022, Chapter 8, 978-3-031-12111-1, 163, 10.1007/978-3-031-12112-8_8
    11. Nelson Guilherme Machado Pinto, Vanessa Piovesan Rossato, Andressa Petry Müller, Daniel Arruda Coronel, Environmental degradation and agriculture: an approach in countries by middle of indexes, 2022, 52, 1678-4596, 10.1590/0103-8478cr20201067
    12. Mohummed Shofi Ullah Mazumder, Md. Sekender Ali, Mahbuba Moonmoon, Farzana Zannat Toshi, Effects of conservation farming practices on agro-ecosystem services for sustainable food security in Bangladesh, 2023, 1876-4517, 10.1007/s12571-023-01359-3
    13. Abdusame Tadjiev, Nodir Djanibekov, Thomas Herzfeld, Does zero tillage save or increase production costs? Evidence from smallholders in Kyrgyzstan, 2023, 21, 1473-5903, 10.1080/14735903.2023.2270191
    14. Ya. Z. Kaipov, Z. R. Sultangazin, R. L. Akchurin, The influence of biologized crop rotations on organic matter and agrophysical soil layers in the arid steppe of the Southern Urals, 2023, 2686-701X, 63, 10.32634/0869-8155-2023-372-7-63-68
    15. Christoph Raab, Michael Spies, Characterising cropland fragmentation in post-Soviet Central Asia, using Landsat remote-sensing time series data, 2023, 156, 01436228, 102968, 10.1016/j.apgeog.2023.102968
    16. Mukhiddin Juliev, Madinabonu Kholmurodova, Bekmurat Abdikairov, Jilili Abuduwaili, A comprehensive review of soil erosion research in Central Asian countries (1993-2022) based on the Scopus database, 2024, 18015395, 10.17221/82/2024-SWR
    17. Aziz Nurbekov, Muhammadjon Kosimov, Sokhib Islamov, Botir Khaitov, Dilrabo Qodirova, Zulfiya Yuldasheva, Jonibek Khudayqulov, Khafizakhon Ergasheva, Ruhangiz Nurbekova, No-till, crop residue management and winter wheat-based crop rotation strategies under rainfed environment, 2024, 6, 2673-3218, 10.3389/fagro.2024.1453976
    18. Zhumagali OSPANBAYEV, Ainur DOSZHANOVA, Yerlan ABDRAZAKOV, Rauan ZHAPAYEV, Aisada SEMBAYEVA, Araily ZAKİEVA, Zhainagul YERTAYEVA, Tillage system and cover crop effects on organic carbon and available nutrient contents in light chestnut soil, 2023, 12, 2147-4249, 238, 10.18393/ejss.1268176
    19. Muhammad Farooq, Ahmad Nawaz, Abdul Rehman, Aman Ullah, Abdul Wakeel, Hafeez ur Rehman, Ahmad Nawaz, Kadambot H.M. Siddique, Michael Frei, Conservation agriculture effects on ecosystem health and sustainability – A review of rice–wheat cropping system, 2024, 957, 00489697, 177535, 10.1016/j.scitotenv.2024.177535
    20. Ceren Uysal Oğuz, Arda Özkan, Sanem Özer, Socio-economic and Environmental Dimensions of the Aral Sea Disaster from the Sustainable Development Perspective, 2024, 1301-0549, 1, 10.12995/bilig.7670
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(8456) PDF downloads(862) Cited by(18)

Article outline

Figures and Tables

Figures(4)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog