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Article Contents

1. introduction, 2. analytical framework, 3. literature search, 5. discussion, 6. conclusion, acknowledgement.

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Research impact assessment in agriculture—A review of approaches and impact areas

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Peter Weißhuhn, Katharina Helming, Johanna Ferretti, Research impact assessment in agriculture—A review of approaches and impact areas, Research Evaluation , Volume 27, Issue 1, January 2018, Pages 36–42, https://doi.org/10.1093/reseval/rvx034

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Research has a role to play in society’s endeavour for sustainable development. This is particularly true for agricultural research, since agriculture is at the nexus between numerous sustainable development goals. Yet, generally accepted methods for linking research outcomes to sustainability impacts are missing. We conducted a review of scientific literature to analyse how impacts of agricultural research were assessed and what types of impacts were covered. A total of 171 papers published between 2008 and 2016 were reviewed. Our analytical framework covered three categories: (1) the assessment level of research (policy, programme, organization, project, technology, or other); (2) the type of assessment method (conceptual, qualitative, or quantitative); and (3) the impact areas (economic, social, environmental, or sustainability). The analysis revealed that most papers (56%) addressed economic impacts, such as cost-effectiveness of research funding or macroeconomic effects. In total, 42% analysed social impacts, like food security or aspects of equity. Very few papers (2%) examined environmental impacts, such as climate effects or ecosystem change. Only one paper considered all three sustainability dimensions. We found a majority of papers assessing research impacts at the level of technologies, particularly for economic impacts. There was a tendency of preferring quantitative methods for economic impacts, and qualitative methods for social impacts. The most striking finding was the ‘blind eye’ towards environmental and sustainability implications in research impact assessments. Efforts have to be made to close this gap and to develop integrated research assessment approaches, such as those available for policy impact assessments.

Research has multiple impacts on society. In the light of the international discourse on grand societal challenges and sustainable development, the debate is reinforced about the role of research on economic growth, societal well-being, and environmental integrity ( 1 ). Research impact assessment (RIA) is a key instrument to exploring this role ( 2 ).

A number of countries have begun using RIA to base decisions for allocation of funding on it, and to justify the value of investments in research to taxpayers ( 3 ). The so-called scientometric assessments with a focus on bibliometric and exploitable results such as patents are the main basis for current RIA practices ( 4–6 ). However, neither academic values of science, based on the assumption of ‘knowledge as progress’, nor market values frameworks (‘profit as progress’) seem adequate for achieving and assessing broader public values ( 7 ). Those approaches do not explicitly acknowledge the contribution of research to solving societal challenges, although they are sufficient to measure scientific excellence ( 8 ) or academic impact.

RIA may however represent a vital element for designing socially responsible research processes with orientation towards responsibility for a sustainable development ( 9 , 10 ). In the past, RIAs occurred to focus on output indicators and on links between science and productivity while hardly exploring the wider societal impacts of science ( 11 ). RIA should entail the consideration of intended and non-intended, positive and negative, and long- and short-term impacts of research ( 12 ). Indeed, there has been a broadening of impact assessments to include, for example, cultural and social returns to society ( 13 ). RIA is conceptually and methodologically not yet sufficiently equipped to capture wider societal implications, though ( 14 ). This is due to the specific challenges associated with RIA, including inter alia unknown time lags between research processes and their impacts ( 15–17 ). Independent from their orientation, RIAs are likely to influence research policies for years to come ( 18 ).

Research on RIA and its potential to cover wider societal impacts has examined assessment methods and approaches in specific fields of research, and in specific research organizations. The European Science Foundation ( 19 ) and Guthrie et al. ( 20 ) provided overviews of a range of methods usable in assessment exercises. They discuss generic methods (e.g. economic analyses, surveys, and case studies) with view to their selection for RIAs. Methods need to fit the objectives of the assessment and the characteristics of the disciplines examined. Econometric methods consider the rate of return over investment ( 21 ), indicators for ‘productive interactions’ between the stakeholders try to capture the social impact of research ( 22 ), and case study-based approaches map the ‘public values’ of research programmes ( 8 , 23 ). No approach is generally favourable over another, while challenges exist in understanding which impact areas are relevant in what contexts. Penfield et al. ( 6 ) looked at the different methods and frameworks employed in assessment approaches worldwide, with a focus on the UK Research Excellence Framework. They argue that there is a need for RIA approaches based on types of impact rather than research discipline. They point to the need for tools and systems to assist in RIAs and highlight different types of information needed along the output-outcome-impact-chain to provide for a comprehensive assessment. In the field of public health research, a minority of RIAs exhibit a wider scope on impacts, and these studies highlight the relevance of case studies ( 24 ). However, case studies often rely on principal investigator interviews and/or peer review, not taking into account the views of end users. Evaluation practices in environment-related research organizations tend to focus on research uptake and management processes, but partially show a broader scope and longer-term outcomes. Establishing attribution of environmental research to different types of impacts was identified to be a key challenge ( 25 ). Other authors tested impact frameworks or impact patterns in disciplinary public research organizations. For example, Gaunand et al. ( 26 ) analysed an internal database of the French Agricultural research organization INRA with 1,048 entries to identify seven impact areas, with five going beyond traditional types of impacts (e.g. conservation of natural resources or scientific advice). Besides, for the case of agricultural research, no systematic review of RIA methods exists in the academic literature that would allow for an overview of available approaches covering different impact areas of research.

Against this background, the objective of this study was to review in how far RIAs of agricultural research capture wider societal implications. We understand agricultural research as being a prime example for the consideration of wider research impacts. This is because agriculture is a sector which has direct and severe implications for a range of the UN Sustainable Development Goals. It has a strong practice orientation and is just beginning to develop a common understanding of innovation processes ( 27 ).

The analysis of the identified literature on agricultural RIA (for details, see next section ‘Literature search’) built on a framework from a preliminary study presented at the ImpAR Conference 2015 ( 28 ). It was based on three categories to explore the impact areas that were addressed and the design of RIA. In particular, the analytical framework consisted of: ( 1 ) the assessment level of research; ( 2 ) the type of assessment method; and ( 3 ) the impact areas covered. On the side, we additionally explored the time dimension of RIA, i.e. whether the assessment was done ex ante or ex post (see Fig. 1 ).

Analytical framework for the review of non-scientometric impact assessment literature of agricultural research.

Agricultural research and the ramifications following from that refer to different levels of assessment (or levels of evaluation, ( 29 )). We defined six assessment levels that can be the subject of a RIA: policy, programme, organization, project, technology, and other. The assessment level of the RIA is a relevant category, since it shapes the approach to the RIA (e.g. the impact chain of a research project differs to that at policy level). The assessment level was clearly stated in all of the analysed papers and in no case more than one assessment level was addressed. Articles were assigned to the policy level, if a certain public technology policy ( 30 ) or science policy, implemented by governments to directly or indirectly affect the conduct of science, was considered. Exemplary topics are research funding, transfer of research results to application, or contribution to economic development. Research programmes were understood as instruments that are adopted by government departments, or other organizational entities to implement research policies and fund research activities in a specific research field (e.g. programmes to promote research on a certain crop or cultivation technique). Articles dealing with the organizational level assess the impact of research activities of a specific research organization. The term research organization comprises public or private research institutes, associations, networks, or partnerships (e.g. the Consultative Group on International Agricultural Research (CGIAR) and its research centres). A research project is the level at which research is actually carried out, e.g. as part of a research programme. The assessment of a research project would consider the impacts of the whole project, from planning through implementation to evaluation instead of focusing on a specific project output, like a certain agricultural innovation. The technology level was considered to be complementary to the other assessment levels of research and comprises studies with a strong focus on specific agricultural machinery or other agricultural innovation such as new crops or crop rotations, fertilizer applications, pest control, or tillage practices, irrespective of the agricultural system (e.g. smallholder or high-technology farming, or organic, integrated, or conventional farming). The category ‘other’ included one article addressing RIA at the level of individual researchers (see ( 31 )).

We categorized the impact areas along the three dimensions of sustainable development by drawing upon the European Commission’s impact assessment guidelines (cf. ( 32 )). The guidelines entail a list of 7 environmental impacts, such as natural resource use, climate change, or aspects of nature conservation; 12 social impacts, such as employment and working conditions, security, education, or aspects of equity; and 10 economic impacts, including business competitiveness, increased trade, and several macroeconomic aspects. The European Commission’s impact assessment guidelines were used as a classification framework because it is one of the most advanced impact assessment frameworks established until to date ( 33 ). In addition, we opened a separate category for those articles exploring joint impacts on the three sustainability dimensions. Few articles addressed impacts in two sustainability dimensions which we assigned to the dominating impact area.

To categorize the type of RIA method, we distinguished between conceptual, qualitative, and quantitative. Conceptual analyses include the development of frameworks or concepts for measuring impacts of agricultural research (e.g. tracking of innovation pathways or the identification of barriers and supporting factors for impact generation). Qualitative and quantitative methods were identified by the use of qualitative data or quantitative data, respectively (cf. ( 34–36 )). Qualitative data can be scaled nominally or ordinally. It is generated by interviews, questionnaires, surveys or choice experiments to gauge stakeholder attitudes to new technologies, their willingness to pay, and their preference for adoption measures. The generation of quantitative data involves a numeric measurement in a standardized way. Such data are on a metric scale and are often used for modelling. The used categorization is rather simple. We assigned approaches which employed mixed-method approaches according to their dominant method. We preferred this over more sophisticated typologies to achieve a high level of abstraction and because the focus of our analysis was on impact areas rather than methods. However, to show consistencies with existing typologies of impact assessment methods ( 19 , 37 ), we provide an overview of the categorization chosen and give examples of the most relevant types of methods.

To additionally explore the approach of the assessment ( 38 ), the dimensions ex ante and ex post were identified. The two approaches are complementary: whereas ex ante impact assessments are usually conducted for strategic and planning purposes to set priorities, ex post impact assessments serve as accountability validation and control against a baseline. The studies in our sample that employed an ex ante approach to RIA usually made this explicit, while in the majority of ex post impact assessments, this was indicated rather implicitly.

This study was performed as a literature review based on Thomson Reuters Web of Science TM Core Collection, indexed in the Science Citation Index Expanded (SCI-Exp) and the Social Sciences Citation Index (SSCI). The motivation for restricting the analysis to articles from ISI-listed journals was to stay within the boundaries of internationally accepted scientific quality management and worldwide access. The advantages of a search based on Elsevier’s Scopus ® (more journals and alternative publications, and more articles from social and health science covered) would not apply for this literature review, with regard to the drawbacks of an index system based on abstracts instead of citation indexes, which is not as transparent as the Core Collection regarding the database definable by the user. We selected the years of 2008 to mid-2016 for the analysis (numbers last updated on 2 June 2016) . First, because most performance-based funding systems have been introduced since 2000, allowing sufficient time for the RIA approaches to evolve and literature to be published. Secondly, in 2008 two key publications on RIA of agricultural research triggered the topic: Kelley, et al. ( 38 ) published the lessons learned from the Standing Panel on Impact Assessment of CGIAR; Watts, et al. ( 39 ) summarized several central pitfalls of impact assessment concerning agricultural research. We took these publications as a starting point for the literature search. We searched in TOPIC and therefore, the terms had to appear in the title, abstract, author keywords, or keywords plus ® . The search query 1 filtered for agricultural research in relation to research impact. To cover similar expressions, we used science, ‘R&D’, and innovation interchangeably with research, and we searched for assessment, evaluation, criteria, benefit, adoption, or adaptation of research.

We combined the TOPIC search with a less strict search query 2 in TITLE using the same groups of terms, as these searches contained approximately two-thirds non-overlapping papers. Together they consisted of 315 papers. Of these, we reviewed 282 after excluding all document types other than articles and reviews (19 papers were not peer-reviewed journal articles) and all papers not written in English language (14 papers). After going through them, 171 proved to be topic-relevant and were included in the analysis.

Analysis matrix showing the number of reviewed articles, each categorized to an assessment level and an impact area (social, economic, environmental, or all three (sustainability)). Additionally, the type of analytical method (conceptual, quantitative, and qualitative) is itemized

In the agricultural RIA, the core assessment level of the reviewed articles was technology (39%), while the other levels were almost equally represented (with the exception of ‘other’). Generally, most papers (56%) addressed economic research impacts, closely followed by social research impacts (42%); however, only three papers (2%) addressed environmental research impacts and only 1 of 171 papers addressed all three dimensions of sustainable development. Assessments at the level of research policy slightly emphasized social impacts over economic impacts (18 papers, or 58%), whereas assessments at the level of technology clearly focused primarily on economic impacts (46 papers, or 68%).

The methods used for agricultural RIA showed no preference for one method type (see Table 1 ). Approximately 31% of the papers assessed research impacts quantitatively, whereas 37% used qualitative methods. Conceptual considerations on research impact were applied by 32% of the studies. A noticeable high number of qualitative studies were conducted to assess social impacts. At the evaluation level of research policy and research programmes, we found a focus on quantitative methods, if economic impacts were assessed.

Overview on type of methods used for agricultural RIA

a Mix of conceptual and qualitative methods.

b Mix of conceptual, qualitative, and quantitative methods.

Additionally, 37 ex ante studies, compared to 134 ex post studies, revealed that the latter clearly dominated, but no robust relation to any other investigated characteristic was found. Of the three environmental impact studies, none assessed ex ante , while the one study exploring sustainability impacts did. The share of ex ante assessments regarding social impacts was very similar to those regarding economic impacts. Within the assessment levels of research (excluding ‘others’ with only one paper), no notable difference between the shares of ex ante assessments occurred as they ranged between 13 and 28%.

The most relevant outcome of the review analysis was that only 3 of the 171 papers focus on the environmental impacts of agricultural research. This seems surprising because agriculture is dependent on an intact environment. However, this finding is supported by two recent reviews: one from Bennett, et al. ( 40 ) and one from Maredia and Raitzer ( 41 ). Both note that not only international agricultural research in general but also research on natural resource management shows a lack regarding large-scale assessments of environmental impacts. The CGIAR also recognized the necessity to deepen the understanding of the environmental impacts of its work because RIAs had largely ignored environmental benefits ( 42 ).

A few papers explicitly include environmental impacts of research in addition to their main focus. Raitzer and Maredia ( 43 ) address water depletion, greenhouse gas emissions, and landscape effects; however, their overall focus is on poverty reduction. Ajayi et al. ( 44 ) report the improvement of soil physical properties and soil biodiversity from introducing fertilizer trees but predominantly measure economic and social effects. Cavallo, et al. ( 45 ) investigate users’ attitudes towards the environmental impact of agricultural tractors (considered as technological innovation) but do not measure the environmental impact. Briones, et al. ( 46 ) configure an environmental ‘modification’ of economic surplus analysis, but they do not prioritize environmental impacts.

Of course, the environmental impacts of agricultural practices were the topic of many studies in recent decades, such as Kyllmar, et al. ( 47 ), Skinner, et al. ( 48 ), Van der Werf and Petit ( 49 ), among many others. However, we found very little evidence for the impact of agricultural research on the environment. A study on environmental management systems that examined technology adoption rates though not the environmental impacts is exemplarily for this ( 50 ). One possible explanation is based on the observation made by Morris, et al. ( 51 ) and Watts, et al. ( 39 ). They see impact assessments tending to accentuate the success stories because studies are often commissioned strategically as to demonstrate a certain outcome. This would mean to avoid carving out negative environmental impacts that conflict with, when indicated, the positive economic or societal impacts of the assessed research activity. In analogy to policy impact assessments, this points to the need of incentives to equally explore intended and unintended, expected and non-expected impacts from scratch ( 52 ). From those tasked with an RIA, this again requires an open attitude in ‘doing RIA’ and towards the findings of their RIA.

Another possible explanation was given by Bennett, et al. ( 40 ): a lack of skills in ecology or environmental economics to cope with the technically complex and data-intensive integration of environmental impacts. Although such a lack of skills or data could also apply to social and economic impacts, continuous monitoring of environmental data related to agricultural practices is particularly scarce. A third possible explanation is a conceptual oversight, as environmental impacts may be thought to be covered by the plenty of environmental impact assessments of agricultural activities itself.

The impression of a ‘blind eye’ on the environment in agricultural RIA may change when publications beyond Web of Science TM Core Collection are considered ( 53 ) or sources other than peer-reviewed journal articles are analysed (e.g. reports; conference proceedings). See, for example, Kelley, et al. ( 38 ), Maredia and Pingali ( 54 ), or FAO ( 55 ). Additionally, scientific publications of the highest quality standard (indicated by reviews and articles being listed in the Web of Science TM Core Collection) seem to not yet reflect experiences and advancements from assessment applications on research and innovation policy that usually include the environmental impact ( 56 ).

Since their beginnings, RIAs have begun to move away from narrow exercises concerned with economic impacts ( 11 ) and expanded their scope to social impacts. However, we only found one sustainability approach in our review that would cover all three impact areas of agricultural research (see ( 57 )). In contrast, progressive approaches to policy impact assessment largely attempt to cover the full range of environmental, social, and economic impacts of policy ( 33 , 58 ). RIAs may learn from them.

Additionally, the focus of agricultural research on technological innovation seems evident. Although the word innovation is sometimes still used for new technology (as in ‘diffusion of innovations’), it is increasingly used for the process of technical and institutional change at the farm level and higher levels of impact. Technology production increasingly is embedded in innovation systems ( 59 ).

The review revealed a diversity of methods (see Table 2 ) applied in impact assessments of agricultural research. In the early phases of RIA, the methods drawn from agricultural economics were considered as good standard for an impact assessment of international agricultural research ( 39 ). However, quantitative methods most often address economic impacts. In addition, the reliability of assessments based on econometric models is often disputed because of strong relationships between modelling assumptions and respective results.

Regarding environmental (or sustainability) impacts of agricultural research, the portfolio of assessment methods could be extended by learning from RIAs in other impact areas. In our literature sample, only review, framework development (e.g. key barrier typologies, environmental costing, or payments for ecosystem services), life-cycle assessment, and semi-structured interviews were used for environmental impacts of agricultural research.

In total, 42 of the 171 analysed papers assessed the impact of participatory research. A co-management of public research acknowledges the influence of the surrounding ecological, social, and political system and allows different types of stakeholder knowledge to shape innovation ( 60 ). Schut, et al. ( 36 ) conceptualize an agricultural innovation support system, which considers multi-stakeholder dynamics next to multilevel interactions within the agricultural system and multiple dimensions of the agricultural problem. Another type of participation in RIAs is the involvement of stakeholders to the evaluation process. A comparatively low number of six papers considered participatory evaluation of research impact, of them three in combination with impact assessment of participatory research.

Approximately 22% of the articles in our sample on agricultural research reported that they conducted their assessments ex ante , but most studies were ex post assessments. Watts, et al. ( 39 ) considered ex ante impact assessment to be more instructive than ex post assessment because it can directly guide the design of research towards maximizing beneficial impacts. This is particularly true when an ex ante assessment is conducted as a comparative assessment comprising a set of alternative options ( 61 ).

Many authors of the studies analysed were not explicit about the time frames considered in their ex post studies. The potential latency of impacts from research points to the need for ex post (and ex ante) studies to account for and analyse longer time periods, either considering ‘decades’ ( 62 , 63 ) or a lag distribution covering up to 50 years, with a peak approximately in the middle of the impact period ( 64 ). This finding is in line with the perspective of impact assessments as an ongoing process throughout a project’s life cycle and not as a one-off process at the end ( 51 ). Nevertheless, ex post assessments are an important component of a comprehensive evaluation package, which includes ex ante impact assessment, impact pathway analysis, programme peer reviews, performance monitoring and evaluation, and process evaluations, among others ( 38 ).

RIA is conceptually and methodologically not yet sufficiently equipped to capture wider societal implications, though ( 14 ). This is due to the specific challenges associated with RIA, including inter alia unknown time lags between research processes and their impacts ( 15–17 ). Independent from their orientation, RIAs are likely to influence research policies for years to come ( 18 ).

However, in the cases in which a RIA is carried out, an increase in the positive impacts (or avoidance of negative impacts) of agricultural research does not follow automatically. Lilja and Dixon ( 65 ) state the following methodological reasons for the missing impact of impact studies: no accountability with internal learning, no developed scaling out, the overlap of monitoring and evaluation and impact assessment, the intrinsic nature of functional and empowering farmer participation, the persistent lack of widespread attention to gender, and the operational and political complexity of multi-stakeholder impact assessment. In contrast, a desired impact of research could be reached or boosted by specific measures without making an impact assessment at all. Kristjanson, et al. ( 66 ), for example, proposed seven framework conditions for agricultural research to bridge the gap between scientific knowledge and action towards sustainable development. RIA should develop into process-oriented evaluations, in contrast to outcome-oriented evaluation ( 67 ), for addressing the intended kind of impacts, the scope of assessment, and for choosing the appropriate assessment method ( 19 ).

This review aimed at providing an overview of impact assessment activities reported in academic agricultural literature with regard to their coverage of impact areas and type of assessment method used. We found a remarkable body of non-scientometric RIA at all evaluation levels of agricultural research but a major interest in economic impacts of new agricultural technologies. These are closely followed by an interest in social impacts at multiple assessments levels that usually focus on food security and poverty reduction and rely slightly more on qualitative assessment methods. In contrast, the assessment of the environmental impacts of agricultural research or comprehensive sustainability assessments was exceptionally limited. They may have been systematically overlooked in the past, for the reason of expected negative results, thought to be covered by other impact studies or methodological challenges. RIA could learn from user-oriented policy impact assessments that usually include environmental impacts. Frameworks for RIA should avoid narrowing the assessment focus and instead considering intended and unintended impacts in several impact areas equally. It seems fruitful to invest in assessment teams’ environmental analytic skills and to expand several of the already developed methods for economic or social impact to the environmental impacts. Only then, the complex and comprehensive contribution of agricultural research to sustainable development can be revealed.

The authors would like to thank Jana Rumler and Claus Dalchow for their support in the Web of Science analysis and Melanie Gutschker for her support in the quantitative literature analysis.

This work was supported by the project LIAISE (Linking Impact Assessment to Sustainability Expertise, www.liaisenoe.eu ), which was funded by Framework Programme 7 of the European Commission and co-funded by the Leibniz-Centre for Agricultural Landscape Research. The research was further inspired and supported by funding from the ‘Guidelines for Sustainability Management’ project for non-university research institutes in Germany (‘Leitfaden Nachhaltigkeitsmanagement’, BMBF grant 311 number 13NKE003A).

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Zero Hunger pp 71–79 Cite as

Agricultural Research: Applications and Future Orientations

• Naser Valizadeh Ph.D. Student 6 &
• Masoud Bijani Assistant Professor 7  
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Part of the book series: Encyclopedia of the UN Sustainable Development Goals ((ENUNSDG))

Agricultural research methodology

Agricultural research can be broadly defined as any research activity aimed at improving productivity and quality of crops by their genetic improvement, better plant protection, irrigation, storage methods, farm mechanization, efficient marketing, and a better management of resources (Loebenstein and Thottappilly 2007 ).

Introduction

The objective of this document is to provide a tool to understand aspects and future orientations of agricultural research. It begins with an overview of the concept and/or definition of agricultural research. It then focuses on the role of agricultural research in achieving the goals of 2030 Agenda, different types of agricultural researched, systemic research methodology in agriculture, and finally different kinds of use for agricultural research.

The Concept and Definition of Agricultural Research

Finding answers for questions about unknown phenomena in the agricultural area is the key to agricultural...

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Valizadeh, N., Bijani, M. (2020). Agricultural Research: Applications and Future Orientations. In: Leal Filho, W., Azul, A.M., Brandli, L., Özuyar, P.G., Wall, T. (eds) Zero Hunger. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://doi.org/10.1007/978-3-319-95675-6_5

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• Published: 28 February 2022

Effect of COVID-19 on agricultural production and food security: A scientometric analysis

• Collins C. Okolie   ORCID: orcid.org/0000-0002-6633-6717 1 &
• Abiodun A. Ogundeji   ORCID: orcid.org/0000-0001-7356-5668 1  

Humanities and Social Sciences Communications volume  9 , Article number:  64 ( 2022 ) Cite this article

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Coronavirus disease has created an unexpected negative situation globally, impacting the agricultural sector, economy, human health, and food security. This study examined research on COVID-19 in relation to agricultural production and food security. Research articles published in Web of Science and Scopus were sourced, considering critical situations and circumstance posed by COVID-19 pandemic with regards to the shortage of agricultural production activities and threat to food security systems. In total, 174 published papers in BibTeX format were downloaded for further study. To assess the relevant documents, authors used “effects of COVID-19 on agricultural production and food security (ECAP-FS) as a search keyword for research published between 2016 and April 2021 utilising bibliometric innovative methods. The findings indicated an annual growth rate of about 56.64%, indicating that research on ECAP-FS increased over time within the study period. Nevertheless, the research output on ECAP-FS varied with 2020 accounting for 38.5%, followed by 2021 with 37.9% as at April 2021. The proposed four stage processes for merging two databases for bibliometric analyses clearly showed that one can run collaboration network analyses, authors coupling among other analyses by following our procedure and finally using net2VOSviewer, which is embedded in Rstudio software package. The study concluded that interruptions in agricultural food supply as a result of the pandemic impacted supply and demand shocks with negative impacts on all the four pillars of food security.

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Introduction.

The coronavirus disease (COVID-19) has created an unusual situation globally (Alam and Khatun, 2021 ). Barely a year ago early in the year 2020, the unusual nature of coronavirus caused most governments to implement stringent steps in their countries to restrain the virus’s spread. The novel coronavirus (SARS-CoV-2) disease impacted economies throughout the world, disproportionately impacting individuals who were already susceptible to poverty and hunger (Laborde et al., 2020a ; Ceballos et al., 2020 ). In late December 2019, the virus was discovered in Wuhan City, Hubei Province, China. The pandemic caused by COVID-19 presented a major danger to human health, the economy, and food security in both industrialised and emerging nations (Mottaleb et al., 2020 ; Carroll et al., 2020 ; Alam and Khatun, 2021 ). Lessons learned from China revealed that various COVID-19 countermeasures such as lockdown in the country hampered production. This poses a significant risk to the long-term food supply (FAO, 2020 ), and has a negative impact on the economy, resulting in economic decline and crisis (Bai, 2020 ). It is important to understand that certain precautional and control efforts compromise agricultural production (Singh et al., 2021 ).

The virus wreaked havoc on the agricultural production sector, which is at the heart of the food chain (Pu and Zhong, 2020 ). The global spread of coronavirus resulted in the greatest economic downturn since World War Two (Hanna et al., 2020 ; Xu et al., 2021 ). The epidemic’s major impact on agricultural labour input was the restriction of labour mobility. Farmers were not permitted to just go out and gather in any way except to purchase essentials. This resulted in a manpower scarcity and reduced mass production efficiency. For instance, due to a scarcity of migrant experts, producers from Sichuan, Hunan, and Hubei in the grain-producing districts in China (south-eastern coastal district) were not able to sow their crops in good time (Pu and Zhong, 2020 ). Furthermore, wheat and pulse harvesting in northwest India was hampered due to a lack of migrant labour (Dev, 2020 ). Vegetable farmers in Ethiopia incurred not just financial loss as a result of overstocked items, but also from a lack of vital inputs (Tamru et al., 2020 ). Before the pandemic, suppliers may have planted six hectares in a single day, but due to the difficulties in finding tractor drivers during the pandemic, they were only able to cover three hectares a day (Pu and Zhong, 2020 ). Any interruptions in agricultural food supply will indeed result in supply and demand shocks, which will have an immediate effect on the agricultural sector of the economy with long-term economic performance and food security implications (Gregorio and Ancog, 2020 ).

Food security refers to a situation where all individuals at all time have continuous physical and economic access to sufficient, safe, and nutritious food to fulfil their dietary needs and food choices for an active and healthy lifestyle (Elsahoryi et al., 2020 ). Food security has been jeopardised both directly and indirectly as a result of the virus’s destabilisation of food systems and the effects of lockdowns on family revenue and physical access to food (Devereux et al., 2020 ). The presence of coronavirus disease has a negative impact on all the four pillars of food security, viz. availability of food, accessibility of food, utilisation of food, and stability of food (Nechifor et al., 2021 ; Laborde et al., 2020b ). According to Genkin and Mikheev ( 2020 ), the report by the Food and Agriculture Organization (FAO), World Trade Organization, and World Health Organization (WHO) note the threat of a food catastrophe triggered by the current coronavirus pandemic, with a risk of a global “food shortage” owing to interruptions in the trade industry’s supply chain. According to the report, global commerce contracted by roughly 20% in 2020, with 90–120 million human beings falling into severe destitution and over 300 million facing food security issues in emerging nations. To combat the COVID-19 pandemic, world leaders implemented steps to decrease the number of commodities carried by sea, air and land, as well as labour migration at national and global levels. These variables contributed to a widespread disturbance in agricultural output and food distribution systems, posing challenges to the transportation of food and agricultural resources (Genkin and Mikheev, 2020 ).

Present literature centred on the effect of coronavirus on food security or effect of coronavirus on agricultural production (Elsahoryi et al., 2020 ; Nchanji and Lutomia, 2021 ). Despite the growing body of research on coronavirus, agricultural production, and food security, few studies have attempted to conduct a thorough assessment of the literature and map the present level of scientific knowledge on the effect of coronavirus on agricultural production and food security (ECAP-FS). Hence, the goal of this research was to examine the effect of coronavirus on agricultural production and food security by employing bibliometric analyses techniques to recognise keywords in connection to two core aspects, namely the most prolific or productive writers and the most collaborative nations, and then to examine the strength of their association over the study period. The study characterised intellectual processes further by visualising and recognising the advancement of the co-citation network, cooperation network, and trends in ECAP-FS research. This research will not only aid in the identification of present research on ECAP-FS, but also contributes to an improved comprehension of the scientific knowledge of coronavirus and its impact on agricultural production, food security, and the investigation of its evolution via published papers included in the Web of Science (WoS) and Scopus databases. Because one database is unlikely to provide a comprehensive picture of knowledge and trends in a field, the authors recommend a four stage processes to achieve a merged database that integrates WoS and Scopus and then deletes identical publications using RStudio or R-package to perform author coupling, keywords co-occurrence network visualisation, university collaboration networks, and others using net2VOSviewer. This study will be among the few that explains how to integrate two datasets and utilise them to conduct different network associations in bibliometrix R-package (RStudio v.4.0.3 software).

Method and data collection

The scientometric technique was used to retrieve articles relating to the effect of coronavirus on agricultural production and food security. This method used resources from two different databases, WoS and Scopus, for the systematic reviews. Table 1 shows the eligibility and exclusion criteria that was used to access the relevant documents. The various steps employed in the review process were (databases, identification, screening, eligibility, merging, duplicate removal and included documents) (see Fig. 1 ). Processing and analysis of the data were then applied to the remaining documents. Scientometrics is defined as the research approach utilised in analysing and assessing science, innovation, and technology by applying statistics and quantitative analysis to explain the distribution and visualisation patterns of research within a specific nation, issue, field or institution (Orimoloye and Ololade, 2021 ). Scientometric evaluations have been used to analyse scientific trends and outputs, as well as the evolution of research, author productivity, journals, and nations, as well as to discover and measure international collaboration (Orimoloye and Ololade, 2021 ).

WoS: Web of Science.

WoS and Scopus were the two-database used for this study. WoS is a database collection administered by Thomson Reuters Institute of Scientific Information (ISI) that contains databases on humanities, social sciences, biology (i.e., Biosis), science (i.e., Core Collection) and computers (i.e., Inspec). WoS was previously the only and biggest accessible database for bibliometric analysis. However, Scopus that was launched by Elsevier, with ease of use in universities throughout the globe emerged as a key rival for doing such studies (Echchakoui, 2020 ). Scopus has the largest abstract and citation databases with over 22,800 journals from 5000 publishers worldwide was used in the review (Shaffril et al., 2018 ). Moreover, It is the most comprehensive interdisciplinary database of peer-reviewed literature in the social sciences, and is generally acknowledged and utilised for quantitative analyses (Guerrero-Baena et al., 2014 ).

Criteria for eligibility and exclusion

Various qualifying and exclusion criteria were considered. Title-based search for rapid visibility and retrieval was used. According to Ekundayo and Okoh ( 2018 ), a title-specific search offers the advantages of low loss, considerable retrieval, and sensitivity when compared to other types of searches such as a topic, field, or author search. First, concerning literature type, only journals and final articles were selected, which meant Article in Press, etc., were excluded. Secondly, non-English articles were excluded. Thirdly, a period of 6 years was used followed by the subject area, which focused on Environmental, Social, Agricultural, and Biological Sciences (Table 1 ) (Shaffril et al., 2018 ).

Systematic review process

To explore the current literature on ECAP-FS, we conducted a comprehensive literature review according to the rules provided by Tranfield et al. ( 2003 ). The systematic review process for this study involved four stages. The review process was performed in April 2021. The first stage was the selection of databases (WoS and Scopus). The second stage pinpointed keywords utilised for the search process. Based on prior research, keywords similar and related to the effect of COVID-19 on agricultural output and food security were used with a total of ( n  = 9, 421) published records found on WoS and Scopus, respectively (Table 2 ). The third stage was screening. Out of ( n  = 9, 421) papers eligible for evaluation at this stage, a total of ( n  = 7, 203) papers were excluded. The fourth stage was eligibility where the complete articles were accessible. Following a thorough review, a total of ( n  = 1, 46) publications were eliminated since some did not focus on the effect of coronavirus on agricultural production and food security. The fifth stage was merging the two documents ( n  = 6, 172 = 178). The sixth stage was the removal of duplicates ( n  = 4). The last round of evaluation yielded a total of ( n  = 174) papers for qualitative analysis (Fig. 1 ).

Processing and analysis of data

The research assessed data obtained for scientometric investigation utilising RStudio v.4.0.3 software with bibliometrix R-package and net2VOSviewer after reading the articles relevant to the study. The data were imported into RStudio, transformed to a bibliographic data frame, and normalised for duplicate matches (Aria and Cuccurullo, 2017 ; Ekundayo and Okoh, 2018 ). Net2VOSviewer (net,vos.path = NULL) embedded in RStudio v.4.0.3 software were used for visualisation. The VOSviewer programme created by Van Eck and Waltman ( 2009 ) is often used to visualise and evaluate a bibliometric network. Hamidah et al. ( 2021 ) and Zhang and Yuan ( 2019 ) made use of VOSviewer to analyse a bibliographic map on energy performance. Park and Nagy ( 2018 ) used VOSviewer to examine building control bibliographic data, and Van Eck and Waltman ( 2017 ) analysed citation-based clustering in the field of astronomy and astrophysics using VOSviewer. The research made use of Net2VOSviewer embedded in R studio to make visualisation maps, such as authors coupling, keyword co-occurrence network, and university collaboration network, based on bibliographic data. Each circle on the VOSviewer visual map represents a word. The term activity is represented by the circle and text size. The big circle and text show the chosen terms in a field. The distance between the two words reflects the degree of their association. In this case, the relationship between two words will be greater if the distance between them is small (Hamidah et al., 2021 ).

Web of Science and Scopus database merging for bibliometric analysis

The authors suggest the following four stage approach to combine the two databases shown in Fig. 1 and Table 3 .

As soon as required articles were sourced, we downloaded the documents separately from WoS and Scopus databases. For WoS, we clicked on export, which redirected us to another window where we selected “other file formats” under record content, and “BiTeX” under file format before we clicked export. For Scopus, we went to export document setting where we ticked all relevant boxes including “BibTeX” before clicking export. The second step was to transform (WoS.bib and Scopus.bib) to “bibtex” files. Here we used R or Rstudio software by loading the bibliometrix package “install.packages” (“bibliometrix”), and “library(bibliometrix)”, After that we specified the pathway using the command file1<- “path/savedrecs.bib” and file2 < - “path/scopus.bib” for WoS and Scopus files, respectively. After that we converted file (1&2) using command “f1<-convert2df(file1, dbsource = “isi”, format = “bibtex”)” and “f2<-convert2df(file2, dbsource = “scopus”, format = “bibtex”)” for WoS and Scopus respectively. We merged the two databases in R/Rstudio. For this operation to be successful, we used the command “j <-mergeDbSources(f1, f2, remove.duplicated = FALSE)”. Finally, the duplicate documents were removed using the command “M < -duplicatedMatching(j, Field = “TI”,tol = 0.95)”. We performed a bibliometric analysis for bibtex file in Rstudio, using Aria and Cuccurullo’s ( 2017 ) techniques and scripts in R, and utilising the net2VOSviewer for keywords co-occurrence network, collaboration networks of universities, authors coupling, amongst others.

Bibliometric analyses results

During the survey period, 174 papers were published on ECAP-FS; their characteristics are shown in Table 4 . The research had 851 authors, with a cooperation index of 5.1 and a document/author ratio of 0.20 (4.89 authors/document). Except for nine authors who published alone, all 842 authors were part of multi-author publications.

During the research period, an average of 6.0 citations per document were recorded. Lotka’s law scientific output for ECAP-FS study revealed a constant of 0.70 and beta coefficient of 3.88, with a Kolmogorov–Smirnoff goodness-of-fit of 0.94. Table 5 and Fig. 2 displays published research on ECAP-FS from 2016 to April 2021 in conjunction with the total citation of papers on average by year. The yearly pace of development was 56.64, with a mean overall of 12 ± 6, indicating that ECAP-FS research increased over time. This outcome agrees with the work of El Mohadebe et al. ( 2020 ) who stated that the number of published articles increased exponentially since the start of the COVID-19 pandemic. The rise in COVID-19 research reflects that it is a major danger to human health, the economy, and food security in industrialised and emerging nations (Carroll et al., 2020 ; Mottaleb et al., 2020 ; Alam and Khatun, 2021 ).

ATC/Y average total citations of articles published per year. NB: The yearly percentage rate of increase was 56.64.

During the survey period, research production varied, peaking in 2020 with 38.5% (67/174) of the total research output, followed by 2021 with 66 research articles accounting for 37.9% (66/174) during the same time. This result is liable to change when additional papers pertaining to ECAP-FS are published in 2021. The average total number of citations for published papers changed over time, peaking in 2016 (average = 11.8). Furthermore, the findings of this analysis identified the top 20 most prolific authors from 2016 to April 2021. Table 6 shows Gong B as the most productive author over the time, with six papers accounting for 3.45% of the total research publications on ECAP-FS. The following were placed second on the list: Baudron F, Peng W, and Zhang S who published three research articles each accounting for 1.7% of the total published research articles within the study period. The rest of the 17 authors published two articles within the same year. The quantity of a researcher’s academic output demonstrates their efficacy and propensity for conducting quality research (Orimoloye et al., 2021a )

Citation analysis reveals how many times a specific research article has been cited in other scientific articles. More cited research articles are considered significantly more influential than articles with fewer citations (Mishra et al., 2017 ; Nyam et al., 2020 ). Table 7 shows the top 20 papers on ECAP-FS in terms of citations in the field throughout the time. The list was compiled using the publications with the most citations (Echchakoui, 2020 ). In this research on ECAP-FS, Foyer et al. 2016 “Nature Plants” placed first with a total of 244 citations. Hart et al. 2018 “Functional Ecology” took second place with 60 citations, followed by Smiraglia D. 2016 “Environmental Research” with 52 citations during the same time period. Millar NS 2016 “Oecologia” and Tesfahunegn GB 2016 “Applied Geography” rated fourth and fifth with 43 and 42 citations, respectively. With 39, 23 and 21 citations, respectively, KC et al. 2018 “Plos One,” Pu and Zhong, 2020 “Global Food Security,” and Provenza FD 2019 “Frontiers in Nutrition” placed sixth, seventh, and eighth. As shown in Table 8 , the leading active writers were connected with institutions in both emerging and developed countries, including China (28), the United States (19), the United Kingdom (12), Italy (9), Spain (8), Australia (5), India (5), and Mexico (5). With the exception of China, the majority of the articles were from developed countries. China, the United States of America, United Kingdom, Italy, and Spain, among other countries, contributed the most articles in ECAP-FS, which is line with the work of Mottaleb et al. ( 2020 ). According to Orimoloye et al. ( 2021b ), research funding and scholarships have had a significant impact on the research output of many countries. As a result, this study indicates that economic assistance could help in the advancement of research in the area of ECAP-FS. Furthermore, during the research period, the total citation of published papers on average by each nation differed from one nation to another. Table 9 shows the top 20 citations by nation for ECAP-FS research papers. The data indicated that the most mentioned nations were industrialised ones, while China, a developing country, placed second among the most often referenced nations. The exceptional success of China research suggests that the nation performs well in sponsoring field research, possibly because the coronavirus originated in Wuhan City of China (Mottalab et al., 2020). Italy leads the way with 112 total citations and an average article citation of 12.44 for research papers published during the study duration, China was second with 107 citations and an average article citation of 3.82. During the same time period, the United States, the United Kingdom, Ethiopia, and Canada were placed third, fourth, fifth, and sixth, with total number of citations (average article citations) of 81 (4.26), 76 (6.33), 47 (23.50), and 40 (13.33), respectively.

This analysis also uncovered the most relevant sources for published academic research on ECAP-FS between 2016 and April 2021, as shown in Table 10 . Sustainability (Switzerland) was first with a total of 23 scientific papers on ECAP-FS. Agricultural Systems and Journal of Cleaner Production were ranked second and third with a total of 13 and 10 articles respectively. Global Food Security and Science of The Total Environment were rated fourth with eight articles each. Land was ranked fifth with five articles while Food Security, International Journal of Environmental Research and Public Health, Plos One were ranked sixth with four published articles each. Environmental Research and Journal of Integrative Agriculture rated seventh with three published articles on ECAP-FS throughout the review period.

Concerns are growing about the influence of COVID-19 on agricultural production, which could pose a significant threat to long-term food security and food supply (Pu and Zhong, 2020 ). Table 11 summarises the top 20 academics’ most relevant terms. In addition, Table 11 displays the most important keywords linked to ECAP-FS research, including keywords-plus (ID) as well as author keywords (DE). COVID-19, Food Security, Agriculture, Climate Change, Sustainable Development, Agricultural Production, Biodiversity, China, and Sustainability were among the nine keywords shared by keywords-plus (ID) and author keywords (DE). Eleven keywords were peculiar to authors’ keywords (Resilience, Ecosystem Services, Food Systems, COVID-19 Pandemic, Food Supply Chain, India, Land Take, Life Cycle Assessment, Nutrition, Conservation, and Dietary Diversity), and nine keywords were unique to keywords-Plus (Food Supply, Human, Article, Food Production, Land Use, Agricultural Robots, Agricultural Land, Controlled Study, and Cultivation). The distinct author keywords explicitly defined what COVID-19 affected as well as the means or elements engaged in the process (Nutrition, Dietary Diversity, Ecosystem Services, Resilience, Conservation, Food Systems, and Food Supply Chain of People). COVID-19 ( n  = 27, 15.5%), Food Security ( n  = 25, 14.4%), Agriculture ( n  = 18, 10.3%), Climate Change ( n  = 9, 5.2%), Sustainable Development ( n  = 5, 2.9%), Agricultural Production ( n  = 4, 2.3%), Biodiversity ( n  = 4, 2.3%), China ( n  = 4, 2.3%), COVID-19 Pandemic ( n  = 4, 2.3%) were author keyword phrases related with the detection of ECAP-FS.

The keyword analysis identified Food Security in 35 (20.1%) and 25 (14.4%) published papers by keyword-plus and author keyword, respectively, while Agricultural was found in 28 (16.1%) and 18 (10.3%) published papers by keyword-plus and author keyword, respectively. By author keyword and keyword-plus, Agricultural Production was detected in 4 (2.3%) and 28 (16.1%) publications, respectively. In the ECAP-FS study field, Climate Change was detected in 26 (14.9%) and 9 (5.2%) papers by keyword-plus and author keyword, respectively. The review indicates that research on ECAP-FS emphasised these agricultural-related issues several times, implying that COVID-19 has an effect on agriculture, agricultural production, sustainable development, food security, and food supply of the general public, which is exacerbated by climate change, and is a major danger to food security, economy and human health (Mottaleb et al., 2020 ).

The connection between influential authors, keywords, journals, and trending topics was investigated using co-citation network analysis (Leydesdorff, 2009 ). Articles are said to be co-cited when they are cited and appear in other publications’ reference lists (Nyam et al., 2020 ). The top 20 authors coupling in Fig. 3 explains the authors coupling on ECAP-FS-related research. Every node in the network symbolises a distinct author who is linked to others. Connecting lines reflect author-to-author linking routes. The number of lines from each node correlates to the number of published papers that referenced the writer. The cluster of authors network, which comprises 20 nodes (authors), has no less than 18 interconnections. Other indicators of often expressed ideas and frameworks linked to ECAP-FS include nation collaboration (Fig. 4 ) and university collaboration network (Fig. 5 ).

The top 20 authors coupling on agricultural production and food security published articles. (Every node in the network symbolises a distinct author who is linked to others. Connecting lines reflect author-to author linking routes).

The top 27 nation collaboration networks on agricultural production and food security. (Each node represents a country, and the lines represent their collaboration).

The top 20 university collaboration networks on agricultural production and food security research.

Authors with multiple affiliations have made significant contributions to nation and university collaborative networks (Figs. 4 and 5 ). Our findings indicated that studies on ECAP-FS were conducted at institutions in both advanced and developing nations between 2016 and April 2021. The Wageningen University (Netherland), the China Agricultural University (China), the Zhejiang University (Asia), and University of Pretoria (South Africa) had the greatest collaboration network on ECAP-FS studies followed by the University of Western Australia (Australia), University of Leeds (UK), University of Alberta (Canada), University of Sydney (Australia), Case Western Reserve University (USA), Chinese University of Hong Kong (China) and the International Crop Research Institute. The University of Oxford was the only university that did not collaborate with any of the universities during the study period. Figure 4 depicts the networks of collaboration on ECAP-FS for 27 countries. The number of collaboration paths varied from one to 17. The number of partnerships was highest in the USA ( n  = 17), followed by China (n = 10), Australia ( n  = 8), the United Kingdom ( n  = 8), Canada ( n  = 5), the Netherlands ( n  = 4), Germany ( n  = 4), South Africa ( n  = 4), Uganda ( n  = 3), India ( n  = 3), Malaysia ( n  = 2), Denmark ( n  = 2), France ( n  = 2), Spain ( n  = 2), and New Zealand ( n  = 2). The remaining nations had one collaboration network. This outcome is consistent with El Mohadab et al. ( 2020 ) as the analysis of a nation’s collaboration is a vital type of analysis, because it allows for the visualisation of the most influential nations in a given field of research, revealing the level of scientific cooperation between the countries. The following network colour codes were prominent: light green for the USA network; light blue for the China network; purple for the Australia network; orange for the United Kingdom network; and brown for the Spain network.

Figure 6 depicts the top 30 keywords of co-occurrence network, the related visualisation and the association strength of ECAP-FS. The co-occurrence of author keywords was examined to illustrate the research hotspots in ECAP-FS. The threshold for keyword co-occurrence was set at 10, and 30 keywords out of 708 were categorised as visualisation elements. The distance between the components of each pairings indicated topic similarity and relative strength. Individual term clusters were allocated different colours of circles. The network in Fig. 6 depicts three different clusters, each reflecting a branch of research in the ECAP-FS literature. The number of publications in which the keywords co-occurred was shown by the connections between specific keywords. The main themes with the highest overall connection strength in the ECAP-FS literature were COVID-19, Food Security, Agriculture, and Climate Change.

The co-occurrence network visualisation of 30 keywords and their relationship strength of agricultural production and food security research.

The ECAP-FS scientific field has three subfields (clusters of author keywords), which are as follows:

The blue cluster includes terms such as COVID-19, Food Supply, Food Production, China, Food Security, and Agricultural Production.’

The red cluster grouped the keywords Agricultural Land, Catering Services, Environmental Protection, Humans, Meat, Human, Food Industry, Article, Female, Priority Journal, Procedures, Controlled Study, and Environmental Sustainability.

The green cluster grouped the keywords Economic and Social Effects, Agriculture, Agricultural Robots, Sustainable Development, Climate Change, Land Use, Greenhouse Gases, Ecosystem, and Biodiversity. The findings revealed a significant variation in the co-occurrence of author keywords in individual articles in the ECAP-FS literature. This demonstrated the scientific field’s multifaceted and multidimensional nature. This result is agreement with the work of Orimoloye et al. ( 2021b ).

Figure 7 depicts the frequency of word occurrence of the top 70 most utilised title keywords in ECAP-FS studies. During the research, a word cloud was generated using the titles of published articles that contained the most frequently used keywords in ECAP-FS research. This revealed the most commonly used word or phrase in ECAP-FS research. Within the word cloud on ECAP-FS research, various regions of connections and the most significant words used were determined. For example, COVID-19, food security, agriculture, climate change, ecosystem services, resilience, agricultural production, sustainable development, food system, and China were recognised as the most prevalent or prominent themes in ECAP-FS studies.

Word cloud or frequency of word occurrence of the top 70 most often used title keywords in agricultural production and food security research.

The COVID-19 pandemic has received significant recognition since the outbreak, and serious effort has been expended by researchers around the world in various fields. The present bibliometric analysis of COVID-19 examined the resulting effects on agricultural production and food security research trends from 2016 to April 2021 by means of data acquired from WoS and Scopus. According to our findings in ECAP-FS, there has been an exponential rise in research publications. This indicates that studies on ECAP-FS received increasing attention during last few years especially in 2020 and 2021, most likely due to COVID-19 pandemic related research by authors from different counties of the world like China, USA and the United Kingdom. Furthermore, most of the productive authors in ECAP-FS at the time of this research were from China, possibly because the pandemic was first discovered in Wuhan City.

The findings of this analysis revealed that few articles came from Africa. In terms of country and institution collaboration networks, few of the countries and institutions collaborated with the countries in Africa except for the University of Pretoria, which had a strong collaboration network on ECAP-FS research during the period of study. According to the word cloud analysis and frequency analysis of the frequently used keywords and keyword-plus demonstrated that the most topical issues in ECAP-FS are COVID-19, food security, agriculture, climate change, agricultural production, sustainable development, biodiversity and sustainability. These results demonstrated the most persistent issues related to ECAP-FS; this was buttressed by another conceptual framework indicator such as keyword co-occurrence networks.

The bibliometric survey performed in this study has some limitations, such as the use of two databases (Scopus and WoS), the strictness of the search keywords and search approach employed, as well as the exclusion of other document types (e.g., conference papers, books chapters, reviews, abstracts, meetings and notes, etc.) and published articles in languages other than English (French, Dutch, Chinese). Despite the limitations, this research seems to be the first bibliometric analysis on ECAP-FS-related studies, which adds to the evidence base and will drive further studies. Furthermore, WoS and Scopus have greater coverage than other databases, dependable indexing technology that reduces the “indexer effect,” and are highly regarded by scientific communities. Other databases, such as ScienceDirect, Education Resource Information Center (ERIC), and Directory of Open Access Journals (DOAJ), should be evaluated in future studies.

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All data analysed are contained in the paper.

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Okolie, C.C., Ogundeji, A.A. Effect of COVID-19 on agricultural production and food security: A scientometric analysis. Humanit Soc Sci Commun 9 , 64 (2022). https://doi.org/10.1057/s41599-022-01080-0

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Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China

Associated data.

Publicly available datasets were analyzed in this study. This data can be found here: China Statistical Yearbook ( http://www.stats.gov.cn/english/Statisticaldata/AnnualData/ , accessed on 17 July 2020), China Rural Statistical Yearbook ( https://data.cnki.net/trade/yearbook/single/n2019120190?z=z009 , accessed on 23 July 2020), China Meteorological Administration Data Network ( https://data.cma.cn/ , accessed on 28 July 2020).

Reducing drought vulnerability is a basis to achieve sustainable development in agriculture. The study focuses on agricultural drought vulnerability in China by selecting 12 indicators from two aspects: drought sensitivity and resilience to drought. In this study, the degree of agricultural drought vulnerability in China has been evaluated by entropy weight method and weighted comprehensive scoring method. The influencing factors have also been analyzed by a contribution model. The results show that: (1) From 1978 to 2018, agricultural drought vulnerability showed a decreasing trend in China with more less vulnerable to mildly vulnerable cities, and less highly vulnerable cities. At the same time, there is a trend where highly vulnerable cities have been converted to mildly vulnerable cities, whereas mildly vulnerable cities have been converted to less vulnerable cities. (2) This paper analyzes the influencing factors of agricultural drought vulnerability by dividing China into six geographic regions. It reveals that the contribution rate of resilience index is over 50% in the central, southern, and eastern parts of China, where agricultural drought vulnerability is relatively low. However, the contribution rate of sensitivity is 75% in the Southwest and Northwest region, where the agricultural drought vulnerability is relatively high. Among influencing factors, the multiple-crop index, the proportion of the rural population and the forest coverage rate have higher contribution rate. This study carries reference significance for understanding the vulnerability of agricultural drought in China and it provides measures for drought prevention and mitigation.

1. Introduction

Drought occurs frequently in China and there has been a long history of these occurrences. From 206 BC to 1949, 1056 droughts occurred in China [ 1 ]. From 1971 to 2016, the average annual disaster rates of droughts in Heilongjiang, Jilin, Liaoning, and Inner Mongolia Autonomous Region were 19.4%, 23.6%, 25.4% and 29.8%, respectively. The average annual disaster rates of droughts in Anhui, Hebei, Henan, Jiangsu, and Shandong provinces were 11.5%, 20.4%, 16.2%, 8.9%, and 18.3%, respectively [ 2 ]. The Ministry of Emergency Management of the People’s Republic of China has notified that from July to November of 2019, droughts had affected a total of 1174 thousand hectares of crops in Jiangxi and Anhui provinces, resulting in a direct economic loss of 8.8 billion yuan [ 3 ]. From January to April of 2020, 2.433 million people had been affected in 81 counties of 16 cities (prefectures) in Yunnan Province. A total of 662 thousand people had requested for life assistance due to droughts, and 534 thousand hectares of crops were affected, leading to direct economic loss of 1.41 billion yuan [ 4 ].

Drought is considered as a slow-moving natural disaster that causes severe damage to water resources and to agriculture [ 5 ]. The characteristics of drought include, but are not limited to, high frequency, long duration, and large area being influenced [ 6 ]. Agricultural drought is a crucial part of drought and it refers to the situation where agricultural production is sensitive and vulnerable to drought stress [ 7 ]. Agriculture utilizes natural resources directly and it is also a national anchoring industry. Agriculture is less capable of resisting and dealing with disasters. The resistance and handling capacity of agriculture to disasters is low so the adverse impact on agricultural production is most severe when drought occurs. In the same way, droughts can be intensified by poor land management [ 8 ]. Therefore, the situation of agriculture and the extent of drought affect each other. According to the Assessment Report of the AR5 Climate Change 2014: Impacts, Adaptation, and Vulnerability: Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt [ 9 ]. Taking the initiative via human activity is an effective way to alleviate the loss caused by a drought disaster [ 5 ]. So, measuring agricultural drought vulnerability is a prerequisite for targeting interventions to improve and sustain the agricultural performance of both irrigated and rain-fed agriculture [ 10 ].

Climate change has an increasing impact on production and people’s lives. In recent years, the topic of vulnerability to agricultural drought has gradually become the focus and research hotspot of scholars around the world.

Yi (2010) evaluated the agricultural droughts in Dalian, China. Ten evaluation indexes such as irrigation index, population density and proportion of paddy areas were selected [ 11 ]. Yuan (2016) proposed a comprehensive index of regional drought vulnerability that includes exposure, sensitivity, and adaptability [ 12 ]. The establishment of evaluation indicators cannot be applied to all since it is highly subjective to regional characteristics. However, different indexing systems provide more research possibilities in the field of drought vulnerability.

Yan (2012), Pang et al. (2013), Farhangfar et al. (2015), Liu et al. (2015), and others conducted quantitative evaluation on drought vulnerability of maize and wheat and obtained the severity and spatial changes of crops at different growth stages [ 13 , 14 , 15 , 16 ]. Kim et al. (2018) used multivariate statistical analysis method to assess the agricultural vulnerability to droughts in South Korea and the results showed that the Chungchongnam-Do area was most vulnerable [ 17 ]. Lestari et al. (2018) used Arc GIS spatial overlay analysis to evaluate the agricultural drought vulnerability of Semarang Port City in India. The results showed that high vulnerability in six villages, medium vulnerability in seven villages, and low vulnerability in three villages [ 18 ]. Based on super sufficiency DEA, Huang et al. (2019) evaluated the agricultural drought vulnerability of Hetao Irrigation Area in Inner Mongolia and the results showed that the drought vulnerability in the eastern part of Hetao Irrigation Area was much higher than that in the western part [ 19 ]. Frischen et al. (2019) combined the result from spatial analysis of expert consultation and determined the drought vulnerability of Zimbabwe’s agricultural system. The results showed that the country’s drought vulnerability and the degree of impact vary greatly. The northern and southern part of Matabeleland, a province in southwestern part, have higher vulnerability level [ 20 ]. Das et al. (2019) used Savitzky and Golay filtering methods to study the agricultural drought situation and vulnerability in India from 1982 to 2015. Results showed that the vulnerability of drought will continue to decrease over time [ 21 ]. On the basis of selecting the research areas and constructing the evaluation index system, scholars have adopted different methods to evaluate the agricultural vulnerability to droughts. For example: Data envelopment analysis [ 22 , 23 ], analytic hierarchy process [ 24 , 25 , 26 , 27 , 28 ], principal component analysis [ 29 , 30 ], entropy weight method [ 31 , 32 , 33 ], etc. STATA [ 34 , 35 ], ArcGIS [ 36 , 37 , 38 ] and other software have also been used to construct an evaluation model for quantitative analysis.

Rojas et al. (2011) and Zhang et al. (2016) used remote sensing technology to monitor and predict agricultural drought [ 39 , 40 ]. Guo et al. (2016) proposed a new method (vulnerability surfaces) for assessing vulnerability quantitatively and continuously by including the environmental variable as an additional perspective on exposure and assessed global drought risk of maize based on these surfaces [ 41 ]. Chen et al. (2017) and Zeng et al. (2019) conducted drought risk assessment on Yunnan Province and Gansu Province respectively [ 42 , 43 ]. All the above studies have provided scientific methods for drought risk assessment and they have since enriched the assessment system for agricultural drought vulnerability.

Basing on a wide range of research areas and research methods, there exists the differences in the natural geographical environment, economic and social conditions, which has led to different influencing factors and various degrees of agricultural drought vulnerability. For example: Zarafshani et al. (2012) argued that the vulnerability of wheat farmers in the western part of Iran is mainly affected by economical, socio-cultural, psychological, technological, and infrastructural factors [ 44 ]. Wu et al. (2017) believed that the water shortage rate and irrigation level in the growing season were the main factors affecting the vulnerability level of regional agricultural drought [ 45 ]. Kamali et al. (2019) believed that the fertilization level is an important factor affecting the vulnerability of crop to drought in sub-Saharan Africa. Generally, countries with a higher food production index and better infrastructure perform better in terms of withstanding drought [ 46 ].

To sum up, there are two methods namely qualitative research and quantitative research on agricultural drought vulnerability. Existing research on agricultural drought vulnerability in China mainly focused on certain regions for quantitative research [ 7 , 14 , 32 , 37 , 45 , 47 , 48 , 49 , 50 , 51 ]. There were only a few studies on the overall assessment of agricultural drought vulnerability and among those the research objects, conclusions and countermeasures are limited.

Therefore, this paper focuses on the agricultural drought vulnerability in China. Based on literature review and relative theories, the paper first constructs the vulnerability evaluation index system of agricultural drought. Then the paper uses entropy weight method, weighted comprehensive scoring method as well as k-means clustering algorithm to evaluate and categorize the vulnerability of agricultural drought in China. Finally, using the contribution model to analyze the influencing factors and the degrees of agricultural drought vulnerability in China, this paper proposes countermeasures to reduce agricultural drought vulnerability in China. In one aspect, the paper carries theoretical value for enriching vulnerability research. It is also conducive to a better understanding of drought conditions and influencing factors in various regions of China. In another aspect, the empirical analysis provides the basis for the government to formulate corresponding policies, to reduce losses caused by disasters, and to promote the sustainable development of agriculture in China.

2. Materials and Methods

2.1. research area overview.

The People’s Republic of China is located in East Asia and to the west coast of the Pacific Ocean. Liberated on 1 October 1949, China’s capital city is Beijing and the provincial administrative divisions are divided into twenty-three provinces, five autonomous regions, four municipalities, and two special administrative regions. China’s land area is about 9.6 million square kilometers. China is the world’s second largest economy, the world’s largest industrial country, and the world’s largest agricultural country. At the end of 2019, the total population of mainland China was more than 1.4 billion.

The terrain is high in the West and low in the East. Mountains, plateaus, and hills account for estimated 67% of the land area, basins, and plains account for around 33% of the total land area. The climate condition is complex and diverse.

Looking at the situation and distribution of China’s agricultural natural resources as a whole, the light and heat conditions are superior. However, there is a great regional differences of dry and wet conditions. The total amount of river runoff is large; however, the coordination and distribution of soil and water is not even. The absolute amount of land resources is large; however, the land occupied per capita is small. Agriculture still serves as the basic industry of China’s national economy.

2.2. Establishment of Indicator System and Data Sources

The establishment of evaluation index system is the prerequisite for evaluating agricultural drought vulnerability. Vulnerability is the root cause of drought disasters, which results from the interaction of natural environment and social economy system as well as the interactions of sensitivity and resilience in a certain space. Therefore, following the principles of science, comprehensiveness, pertinence, quantification, and availability of data [ 47 ], we select two first-level indicators, namely, sensitivity and resilience and 12 second-level indicators to conduct an evaluation on 31 provincial administrative units (except for Hong Kong, Macao, and Taiwan) in China to establish an indicator system (as shown in Table 1 ). The larger the indicator, the larger the vulnerability of agricultural drought. Hence, it is a positive indicator. On the contrary, it would be a negative indicator.

Index system and source of China’s agricultural drought vulnerability assessment.

Sensitivity is the sum of all kinds of natural and social factors that would cause or aggravate drought and its impact on agricultural drought vulnerability is negative. That means the higher the sensitivity, the greater the vulnerability of agricultural drought. It includes agriculture in GDP proportion, multiple-crop index, rural population proportion, annual average temperature, annual sunshine duration, and annual precipitation.

Higher proportion of agriculture in GDP means that farmers rely heavily on agricultural income which is highly dependent on natural conditions. So the vulnerability of agricultural drought will increase. The higher the multiple-crop index, the more water the crop would need to grow. As a result, drought vulnerability will increase. The most severely impacted population at the time of drought is the agricultural population. Therefore, when the proportion of rural population increases, the degree of vulnerability will also increase. Moreover, higher the temperature and longer sunshine hours will lead to the increase of evaporation, and hence the agricultural drought vulnerability will increase together. Precipitation is the main factor affecting the growth of crops. The precipitation index can reflect the meteorological conditions of crops in this region and the impact of precipitation on vulnerability is negative.

Resilience refers to the ability of human society to prepare for, to respond to, and to recover from, disasters. It has a positive impact on agricultural drought vulnerability. That means the stronger the resilience, the lower the drought vulnerability. It includes forest coverage rate, net income per capita of rural residents, food production per capita, real GDP per capita, effective irrigation rate, and agricultural fertilizer per unit area.

The forest coverage rate reflects a country’s (or region) actual level of forest resources and forestry possession. Net income per capita of rural residents reflects the group of people’s economical ability to withstand and to resist drought. The higher the net income per capita of rural residents, the weaker the threats of agricultural drought. Food production per capita reflects the level of agricultural productivity. Real GDP per capita reflects the level of social and economic development. When the index is bigger, it means that the social and economic development level and the ability to withstand disasters is high. The effective irrigation rate reflects the degree of water conservancy and irrigation capacity. The increase of the amount of agricultural fertilizer per unit area is beneficial to enhance soil fertility, to improve soil structure and to increase the efficiency of land usage. The above indicators constitute the resilience of the agricultural system.

The agriculture in GDP proportion, the rural population proportion, the net income per capita of rural residents, the food production per capita, and the real GDP per capita affect the agricultural drought vulnerability from the economic and social perspectives. The multiple-crop index, the effective irrigation rate and the agricultural fertilizer per unit area affect the vulnerability of agricultural drought from the perspective of agricultural technology. The forest coverage rate, annual average temperature, annual sunshine duration, and precipitation affect the vulnerability of agriculture to drought from the perspective of natural conditions.

The indicator data in this paper comes from the website of the National Bureau of Statistics [ 52 ] and the China Meteorological Administration [ 53 ]. The annual precipitation, annual sunshine duration and annual average temperature are obtained from annual observations from 613 weather stations nationwide from China Meteorological Administration data network. In addition to the forest coverage rate, net income per capita of rural residents and real GDP (Gross Domestic Product) per capita can be directly obtained, other indicators need to be calculated. The descriptive statistical results of the complete sample are shown in Table 2 .

Descriptive statistical results of samples.

2.3. Data Processing

From Table 1 , each indicator has different dimensions; hence, direct comparison is not possible. Therefore, it is necessary to carry out the dimensionless standardization of each indicator. The positive and negative indicators have different influence directions on agricultural drought vulnerability so the treatment methods should be different.

Suppose there are k provinces, n years and m evaluation indicators; then X θ i j represents the j indicator value of province i in year θ . The normalized value after treatment is expressed as S θ i j (0 < S θ i j < 1). X min is the minimum value of the j indicator and X max is the maximum value of the j indicator.

Positive indicator:

Negative indicator:

2.4. Improved Entropy Weight Method

There are two methods to determine the weight: subjective weight method and objective weight method. This paper chooses the entropy weighting method (one of the objective weighting methods) for indicator weighting, which overcomes the subjective arbitrariness of the subjective weighting method and makes the weighting more scientific. The improved entropy weighting method has the following methods and steps [ 60 , 61 ]:

Build the matrix Y θ i j :

Calculate indicator information entropy e j :

Find indicator difference coefficient (redundancy) g j :

The weight of each indicator w j :

2.5. Vulnerability Assessment Model

This paper chooses the weighted comprehensive scoring method and uses V θ i to represent the degree of vulnerability. The improved vulnerability assessment model of agricultural drought in China is as follows:

2.6. K-Means Clustering Algorithm

According to the above steps, to calculate the degree of vulnerability of the target year of China’s agricultural drought in various regions and put them in ascending order. After that, to use k-means clustering algorithm in Stata to grade the vulnerability of China’s agricultural drought disaster [ 48 , 62 ].

Algorithms usually use Euclidean distance to calculate the distance between samples. The calculation formula is as follows:

Suppose the class center of the k category is c e n t e r k , then the formula of c e n t e r k is updated as follows:

The clustering algorithm requires continuous iteration to re-classify and update c e n t e r k value. Whenever the maximum number of iterations has been reached or the objective function is less than the threshold value, the iteration ends. The objective function is as follows:

2.7. Contribution Model

The main contributing factors of agricultural drought vulnerability in China are analyzed by contribution model. R j is the weight of the j criterion level indicator; C i j is the contribution degree of the j indicator factor to the vulnerability of the i evaluation object; U r represents the contribution of the first level indicators to vulnerability; F j is the weight of single indicator to total target; I i j is the indicator membership degree (that is to say the proportion of Single factor indicator accounts for in vulnerability results. In the obstacle degree model, the indicator deviation degree is the difference between the individual index factor evaluation value and 100%. Therefore, the factor membership in the contribution degree model is the single indicator factor evaluation value ratio 100%) [ 32 ].

3. Results and Discussion

3.1. agricultural drought vulnerability in china.

According to Formulas (1) and (2), after the data is being nondimensionalized and standardized, we use the calculation steps of the entropy weight method (Formulas (3)–(6)) to calculate the weight of each indicator, which is shown in Table 3 .

The weight of each indicator.

It can be seen that, for the two first-class indicators, sensitivity index weight is 0.594 and resilience index weight is 0.406. Among them, multiple-crop index, annual average temperature, the forest coverage rate, the effective irrigation rate, and agriculture in GDP proportion have higher weight of over 0.1. Since the weight of agricultural in GDP proportion is 0.099, which is very close to 0.1, we also put significant important over this index.

According to Formula (7), the agricultural drought vulnerability degree of each region in 1978, 1983, 1988, 1993, 1998, 2003, 2008, 2013, and 2018 have been calculated and shown in Table 4 .

Vulnerability of agricultural drought in different provinces.

It can be noticed that from 1978 to 2018, the vulnerability of agricultural drought in China has decreased year by year. Agricultural drought vulnerability in Gansu, Ningxia, Guizhou, and Tibet is relatively high with an average value of more than 0.648. Agricultural drought vulnerability in Shanghai, Beijing, and Zhejiang is low with the average value less than 0.47.

3.1.1. Classification of Agricultural Drought Vulnerability

In order to accurately classify China’s agricultural drought vulnerability, according to Formulas (8)–(10) by using k-means clustering algorithm in Stata, the China Agricultural Drought Vulnerability Index (ADVI) is divided into four ranges between 0 to 1 and they are shown in Table 5 .

Classification of agricultural drought vulnerability.

3.1.2. Spatial Distribution and Evolution

According to the classification of agricultural drought vulnerability in Table 5 , in order to express the results more clearly, this study uses ArcGIS to display the research results. The assessment results of agricultural drought vulnerability in China are shown in Figure 1 a–i.

Spatiotemporal evolution of agricultural drought vulnerability in China ( a – i ).

It can be seen that the vulnerability of agricultural drought in China has changed significantly over time: from 1978 to 2018, the number of provinces and cities in low and mild vulnerability state has been increasing.

From 1978 to 2018, the spatial distribution pattern of agricultural drought vulnerability in China was obvious:

• (1) The overall agricultural drought vulnerability in China is 0.569, which is at a moderate fragile level. This is in line with the characteristics of frequent droughts and serious losses in China [ 63 , 64 , 65 ].
• (2) Highly vulnerability level: (0.628 < ADVI < 1) Over time, the number of cities in highly vulnerable areas has decreased, which mainly included Xizang, Guizhou, Ningxia, Gansu, etc. Among them, Gansu, Ningxia have higher vulnerability to drought, which is consistent with the research results of other scholars [ 59 , 66 ]. Firstly, most of these areas have complex terrain conditions and less precipitation. Drought is their main natural feature. Secondly, the region is less developed compared with other regions and real GDP per capita is low while agriculture in GDP proportion and rural population proportion is high. It means that farmers are highly dependent on agricultural and natural conditions. With high sensitivity and weak resilience when drought occurs, the number of highly vulnerable provinces and cities are inevitably high.
• (3) Middle vulnerability level: (0.552 < ADVI < 0.628) The number of provinces and cities in this region is stable and it accounts for nearly half of the total number of provinces and cities in China and most of them are concentrated in Central China. It included Inner Mongolia, Sichuan, Hebei, Anhui, etc. Most of them are important grain production bases in China and major agricultural provinces. Agriculture in GDP proportion, multiple-crop index and rural population proportion are high. It reflects that the region has a strong dependence on agriculture with high land utilization rate and heavy water demand.
• (4) Low and mild vulnerability level: (0 < ADVI < 0.552) Although there has seen a small fluctuation in the number of slightly vulnerable provinces and cities, the overall trend shows a stable and marginal increase. This is consistent with the research results of some scholars [ 22 , 67 ]. The provinces and cities in this region such as Shanghai, Zhejiang, Beijing, and Tianjin have a high level of economic development. Their high real GDP per capita gives them better response ability and post disaster recovery ability when disasters occur. At the same time, those provinces and cities tend to have a small agricultural planting area multiple-crop index, agriculture in GDP proportion and rural population proportion are also low. When we turn to those provinces and cities in the Northeast China like Heilongjiang, Jilin, and Liaoning, their land is sparsely populated and the food production per capita is high. They also have high latitude, low average temperature, and less evaporation. The annual sunshine duration is long and the crops normally harvest once a year. With lower multiple-crop index, the water demand is lesser and the sensitivity of disaster is weak.

3.2. Analysis on the Influencing Factors of Agricultural Drought Vulnerability in China

3.2.1. factor contribution analysis of first level index.

According to the research results of agricultural drought vulnerability assessment in China, it can be noticed that the distribution of provinces and cities in different vulnerability levels has certain regional characteristics. Therefore, this paper studies the influencing factors of vulnerability in different regions. It adopts China’s six geographic regions: North China, Northeast China, Northwest China, East China, Central and Southern China, and Southwest China. According to the Formulas (11)–(14), the contribution of sensitivity and resilience is shown in Figure 2 and Figure 3 .

Changes in the contribution of sensitivity indicators.

Changes in the contribution of resilience indicators.

As shown in Figure 2 , looking at the trends as a whole, during the period of 1978–2013, the contribution of sensitivity indexes in different regions was relatively stable. From 2013 to 2018, except for North China, the contribution ratio of sensitivity indicators in other regions changed dramatically.

The contribution of sensitivity indicators in Northeast China, Northwest China, and Southwest China have declined. Possible reasons are as follows: agriculture in GDP proportion, multiple-crop index, and annual sunshine duration has decreased drastically while annual precipitation has increased significantly. Farmers in these regions have become less dependent on agricultural income. Lower land use has reduced water demand, and hence there is less evaporation.

The contribution of sensitivity indexes in East China and Central South have increased significantly. Possible reasons are as follows: agriculture in GDP proportion and multiple-crop index have increased while land use in the region has improved and water demand has increased.

As shown in Figure 3 , looking at the trends as a whole, during the period of 1978–2013, the contribution of resilience indexes in different regions was relatively stable. From 2013 to 2018, except for North China, the contribution ratio of resilience indicators in other regions changed dramatically.

The contribution of resilience indicators in Northeast China, Northwest China, and Southwest China have increased. I think the possible reasons are as follows: the contribution of forest coverage rate, food production per capita, and agricultural fertilizer per unit area have increased while net income per capita of rural residents has declined. The region is less dependent on agriculture, needs to improve its ability to conserve water, and is less able to cope with disasters and recover from disasters.

The contribution of resilience indicators in East China, Central China, and South China have decreased evidently. Possible reasons are as follows: the forest coverage rate, the effective irrigation rate and agricultural fertilizer per unit area have increased. The region’s ability to conserve water is better, so the recovery capacity after the disaster is improved.

On the whole, the contribution of resilience in East China, Central, and Southern China is more than 50%, which is greater than the contribution of sensitivity. Cities in the central and southern region: the Yellow River valley passes through Henan, and the Yangtze River valley passes through Hubei and Hunan. Guangxi, Guangdong, and Hainan are adjacent to the sea. Therefore, the relative abundance of water resources and the contribution of sensitivity indicators are less. East China includes Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, and Shandong. These several provinces and cities are adjacent to the sea, or within the territory of the river flow through, and the level of economic development is at the forefront of China. Therefore, the contribution degree of resilience index is relatively high.

Northwest China, Northeast China, North China, and Southwest China have higher sensitivity contributions than that of resilience. The sensitivity contribution of the Southwest China and Northwest China is as high as 75%. Desert is widespread in the northwest and annual precipitation about 200–400 mm. Deep inland and blocking the arrival of moist air. Northeast China is an important grain production base with a large area of cultivated land. Compared with the coastal areas, its economic development level is not high.

3.2.2. Factor Contribution Analysis of Secondary Index

Select the top four indicators of contribution among the 12 indicators and the calculated results are shown in Table 6 .

The top four indicators and contribution of agricultural drought vulnerability in different regions.

On the whole, referring to the calculation results above, it can be noticed that the contribution factors of agricultural drought vulnerability in China mainly focus on sensitivity. Among them, A2 (multiple-crop index) and A3 (rural population proportion) are more important. It shows that these two indicators have a greater impact on the vulnerability of agricultural drought. We should sustainably reduce the land utilization rate, reduce the water demand, strengthen the vocational skills training of rural residents, supervise and protect the legitimate rights and interests of migrant workers, and promote the transfer of rural population to cities. Hence, the proportion of rural population can be effectively reduced, and the vulnerability of agricultural drought can also be mitigated.

Among the indexes of resilience, B1 (the forest coverage rate) and B4 (real GDP per capita) are more important. According to the data from the Ninth National Forest Resources Inventory, China’s forest coverage rate is still lower than the world average level. Strengthening afforestation is highly effective for soil and water conservation, hence reducing water evaporation and improving the forest coverage rate. The adverse impact from drought can also be reduced significantly. Similarly, the higher the real GDP per capita, the easier the recovery would be after droughts. The GDP per capita China is still relatively low in the worldwide spectrum, although China’s total domestic GDP ranks No. 1 in the world.

The factor with the least contribution is B3 (Food production per capita). China has a large planting area of crops with high and stable grain yield, so it has little impact on agricultural drought vulnerability.

4. Conclusions, Limitations, and Future Research

The paper uses entropy weight method, weighted comprehensive scoring method as well as k-means clustering algorithm to calculate and classify the vulnerability degree of agricultural drought. ArcGIS was used to show the spatial and temporal changes of agricultural drought vulnerability in China, then, using the contribution model to analyze the influencing factors and the degrees of agricultural drought vulnerability in China, the results show that:

• (1) From 1978 to 2018, the vulnerability of agriculture to drought has been reduced and the numbers of China’s highly vulnerable cities have declined. During the same time, there has been a trend appeared that high vulnerability cities have converted to the middle-level vulnerability cities while middle-level vulnerability cities have converted to mild-level or low-level vulnerability cities. The vulnerability towards agricultural drought disasters in China was generally at the middle and mild level in most regions while the vulnerability in Northwest China and Southwest China were more severe.
• (2) China’s agricultural drought vulnerability is mainly affected by sensitivity factors, among which multiple-crop index and the proportion of rural population have a higher contribution compared with other indicators. For resilience index, forest coverage rate and real GDP per capita carry a more important role.

In the data collection process of this paper, partially due to the wide time span selected, there is a lack of data from early years. Therefore, those crucial indicators that can be easily obtained with clean data have been selected for evaluation. Imperfection still exists although these selected indicators can truly reflect the vulnerability characteristics of agricultural drought in China. In the future, we will do some comparative studies on different evaluation methods to further optimize the research results.

Author Contributions

Conceptualization, H.G. and C.P.; Data curation, C.P. and J.C.; Formal analysis, H.G.; Funding acquisition, H.G.; Investigation, C.P. and J.C.; Methodology, H.G. and J.C.; Project administration H.G.; Resources, H.G.; Software, H.G. and J.C.; Supervision, C.P.; Validation, H.G. and J.C.; Visualization, H.G.; Writing—original draft H.G. and J.C.; Writing—review and editing, J.C. and C.P. All authors have read and agreed to the published version of the manuscript.

Social Science Fund Project of Jilin Province, China, grant No.2018BS33; MOE (Ministry of Education in China) Project of Humanities and Social Sciences, grant/award No.18YJC630128; Social Science Fund Project of “the 13th Five-Year” of Education Department of Jilin Province, China, grant No. JJKH20190736SK; and Jilin Province Education Planning Project, China, grant No. JJKH20190243SK.

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HIGH SOLIDS LOADING AQUEOUS SLURRY FORMATION OFCORN STOVER BEFORE PRETREATMENT IN A FED-BATCH BIOREACTOR

Feedstock variability represents a challenge in the adoption of lignocellulosic biomass for biofuels and biochemicals production, due to the differences in critical chemical and physical properties like lignin content, and water absorption respectively. Thus, difficult continuous manufacturing processes in biorefineries, hinder the transition from liquid feedstocks to renewable materials that consisting of solid particles. Modeling of flow properties based on rheological measurements of treated biomass is a quantitative metric for identifying if different feedstocks form pumpable slurries. Additionally, the correlation of yield stress to physical and chemical properties gives a measure that accounts for the variability in the processing design. This research models rheological properties and relates these to compositional data from different non-pretreated fractions of corn stover biomass slurries. Slurries were formed with solids concentrations of 300 g/L in a 6 hours fed-batch process using the commercial enzymes Celluclast 1.5L or Ctec-2 at 1FPU/g or 3 FPU/g of dry solids, basis to enable the liquefaction (i.e., slurry-forming) mechanism. We found that insoluble lignin content of the different fractions was related to water absorption in pellets and free water on slurries and that free water was a good indicator of the potential for a material to form slurry. Higher flowability (lower yield stress) was found at higher content of lignin, particularly for materials containing 26% lignin where yield stress was reduced to 254Pa when compared with mixtures of 14% lignin that presented yield stresses of around 4000 Pa. We show that rheology modeling linked to compositional characteristics for biomass slurries can be used to predict material flow behavior in a biorefinery to optimize and achieve high solids loadings that enhance the production of ethanol for biofuels. This insight and the ability to form high concentration slurries before pretreatment holds the potential to develop new processing strategies that could help to foster a more efficient and sustainable bio-based industry.

DOE EE0008910

Purdue university college of agriculture idea challenge 2030, degree type.

• Doctor of Philosophy
• Agricultural and Biological Engineering

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, additional committee member 5, usage metrics.

• Bioprocessing, bioproduction and bioproducts
• Crop and pasture biomass and bioproducts
• Agricultural engineering