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A Review of the Literature

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1. Introduction

Not to be confused with a book review, a  literature review  surveys scholarly articles, books and other sources (e.g. dissertations, conference proceedings, reports) relevant to a particular issue, area of research, or theory, providing a description, summary, and critical evaluation of each work. The purpose is to offer an overview of and background on significant literature published on a topic.

2. Components

Similar to primary research, development of the literature review requires four stages:

  • Literature search—finding materials relevant to the subject being explored
  • Data evaluation—determining which literature makes a significant contribution to the understanding of the topic
  • Analysis and interpretation—discussing the findings and conclusions of pertinent literature

Literature reviews should comprise the following elements:

  • An overview of the subject, issue or theory under consideration, along with the objectives of the literature review
  • Division of works under review into categories (e.g. those in support of a particular position, those against, and those offering alternative theses entirely)
  • Explanation of how each work is similar to and how it varies from the others
  • Conclusions as to which pieces are best considered in their argument, and make the greatest contribution to the understanding and development of their area of research

3. Definition and Use/Purpose

A literature review may constitute an essential chapter of a thesis or dissertation, or may be a self-contained review of writings on a subject. In either case, its purpose is to:

  • Place each work in the context of its contribution to the understanding of the subject under review
  • Describe the relationship of each work to the others under consideration
  • Identify new ways to interpret, and shed light on any gaps in, previous research
  • Resolve conflicts amongst seemingly contradictory previous studies
  • Identify areas of prior scholarship to prevent duplication of effort
  • Point the way forward for further research
  • Place one's original work (in the case of theses or dissertations) in the context of existing literature

The literature review itself, however, does not present new  primary  scholarship. 

Information adapted from UC Santa Cruz University Library .

  • Master's Theses Database of master's theses written by CSU, Chico students, from 2009 on. Many of these will contain published examples of literature reviews.
  • Proquest Dissertations and Theses: The Humanities and Social Sciences Collection Containes over 2 million dissertations and theses with abstracts, 24 page free previews, and full-text PDF, if available, for dissertations and theses dating back to 1637.
  • Sample APA Paper (lit. review begins page 3) Purdue University Online Writing Lab (OWL)
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What is a literature review?

Finding the gaps and advances in your area, original research vs review articles, annotated bibliography.

A literature review is a survey and critical analysis of what has been written on a particular topic, theory, question or method.

What's its purpose?

  • justify your research
  • provide context for your research
  • ensure that the research has not been done before
  • highlight flaws in previous research
  • identify new ways, to interpret and highlight gaps in previous research
  • signpost a way forward for further research
  • show where your research fits into the existing literature
  • Literature reviews guide The literature reviews guide contains detailed information on the process of searching for and producing literature reviews.
  • Annual reviews online Annual Reviews publishes analytic reviews in focused disciplines within the biomedical, physical, and social sciences. The reviews cover significant developments in the different fields.
  • Web of Science A collection of citation databases and citation analysis tools covering the sciences, social sciences, arts and humanities.
  • Scopus A multidisciplinary abstract and citation database of peer reviewed literature, book reviews and conference proceedings.
  • IEEE Xplore digital library Provides full-text access to IEEE and IEE transactions, journals, magazines and conference proceedings published since 1988 and current IEEE Standards.

Original research

Original research articles are based on an experiment or a study of a topic. They include a methodology section if an experiment was set up and conducted, a results or discussion section, and a conclusion.

Review articles

Review articles are written to bring together and summarise the results or conclusions from many original research articles or studies. They do not usually contain a methodology section, but they will contain very extensive bibliographies.

An annotated bibliography provides:

  • a list of references presenting a brief summary of the main arguments or ideas of each resource.
  • a critique or evaluation of the resource's usefulness, reliability, objectivity or bias
  • a reflection on how the resource fits into your research.
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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.

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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  • Literature Reviews: Analysis, Planning, and Query Techniques. by Wilhelm, William J. and Kaunelis, Davis. Delta Pi Epsilon Journal; Spring / Summer 2005, Vol. 47 Issue 2, p91-106, 16p more... less... This article presents proper techniques for planning, organizing and conducting a reliable literature search using electronic databases that index business education scholarly publications.
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  • Literature Review / Richard Race Source: The Sage Encyclopedia of Qualitative Research Methods / Lisa M. Given (2008). Database: Sage Research Methods

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  • Practical recommendations to help students bridge the research-implementation gap and promote conservation. Pietri, D. M., et al. (2013). Conservation Biology, 27(5), 958-967.

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A Systematic Review of IoT Solutions for Smart Farming

Emerson navarro.

1 School of Technology and Management, Computer Science and Communication Research Centre, Polytechnic Institute of Leiria, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal; tp.airielpi.ym@3320812 (E.N.); [email protected] (N.C.)

António Pereira

2 INOV INESC Inovação, Institute of New Technologies, Leiria Office, Campus 2, Morro do Lena—Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

1. Introduction

The challenge of food production in the 21st century is an increasingly relevant theme as population growth increases year after year. It is estimated that by 2050 the world will have between 9.4 and 10.1 billion people who depend on the world’s biodiversity to live, increasing the demand for dedicated food production areas—specifically for planting and livestock [ 1 ]. Environmental changes caused by human beings could potentially cause conditions in which the development of new crops is not possible. Likewise, the growing urbanization decreases labor in areas typically involved in food production, increases costs and reduces the productive capacity of the sector [ 2 ]. In view of this, smart farming is a new farm management concept that employs techniques and technologies at various levels and scales of agricultural production, enabling to overcome the challenges in food production demands and reduction in the workforce [ 3 , 4 ]. For example smart farming may use different types of sensors to collect data (e.g., temperature, humidity, light, pressure, presence, etc.), communication networks to send and receive data which is then managed and analyzed by management information systems and data analysis solutions [ 5 ]. This system of interconnected devices is commonly referred to as internet of things (IoT) [ 6 ]. The use of the data provided by smart farming helps boosting productivity and minimizing waste by allowing necessary actions to be carried out at the right time, quantity and place [ 7 ].

Moreover, recent technological developments in areas relevant to IoT facilitates an easier adoption and use of smart farming with IoT [ 8 ]. Such technological developments include, for example, network communications, reduction of hardware size, optimization of power consumption and devices cheapening. Furthermore, the World’s largest agricultural producers are promoting the usage of IoT in smart farming by creating incentive programs and public policies to fund research and training [ 9 , 10 ].

Several reviews have been published on IoT solutions for smart agriculture in recent years which denotes that this research field is being constantly receiving new contributions and constant improvement. Existing reviews usually focus on topics like network technologies, embedded system platforms, unmanned aerial vehicles (UAV) devices, network protocols and topologies and enabling cloud platforms. For instance [ 11 ] focuses on arable farming from year 2008 to 2018 and surveys communication technologies and protocols, the generation and analysis of data, IoT architectures and applications and highlights the challenges and future directions related with the application of IoT technologies on arable farming. Review [ 12 ] presents technologies used for communication and data collection within IoT solutions for smart farming as well as several cloud-based IoT platforms used for IoT solutions for smart farming. Additionally, authors present several use cases for the identified applications of IoT for smart farming. Review [ 13 ] presents a systematic review of papers published between 2006 and 2016 and classifies these papers in application domains, such as monitoring, controlling, logistic and prediction. Within these domains, authors also identified the data visualization strategies and the technologies used for communication and edge computing. Review [ 14 ] presents a review of papers published between 2010 and 2016. The authors rely on an IoT architecture with three layers (perception, network, application) to analyze the reviewed papers in terms of perception devices, network technologies and applications. With this, they identify embedded platforms and communication technologies used in IoT solutions as well as the application of such IoT solutions. Finally, [ 15 ] reviewed papers published between 2010 and 2015 and presents a state-of-the-art of IoT solutions for smart farming and smart agriculture. Authors relied on an IoT architecture with three layers (perception, network and application) to analyze the application of sensor and actuator devices and communication technologies within several farming domains, such as agriculture, food consumption, livestock farming, among others.

This study aims to identify how IoT is used with smart farming by (i) presenting a systematic review of the state of the art of the IoT adoption in smart agriculture and (ii) identifying the most commonly used hardware, platforms, network protocols and technologies and their applicability to the proposed solutions. To give a more up-to-date view to the reader about the smart agriculture research field, this review article surveys literature until year 2020 and evaluates the acceptance of (new) IT technologies, such as big data, computer vision, artificial intelligence, blockchain and fuzzy logic in the smart agriculture field. This review complements the analysis already carried out by the academy by performing a comprehensive review of the state-of-the-art of IoT in smart farming. To do so, we rely on an IoT architecture with 4 layers (perception, network, processing and application) to identify the technologies that enable IoT in smart farming. From this approach, the present work identifies an increasing use of modern techniques and technologies for processing the collected data in recent years, such as big data, machine learning, computer vision and blockchain. Moreover, this review contributes to the existent literature by reporting a change in the treatment of data in recent works: while previous work showed that the majority of decision support systems used simple processing mechanisms to handle data collected in real-time, more recent work showed an increasing number of management systems that use complementary technologies that rely on cloud and big data computing for processing large amounts of data. In terms of research domain, this article addresses the agriculture economic sector, including indoor and outdoor agriculture (greenhouse, hydroponics, crop beds, pots, orchards, permanent crops, and arable lands). However, the livestock farming (both indoors and outdoors) was left out because, due to its specificities like impact on nature, associated mobility, different species (mammals, birds, fishing) deserves a separated and specific review.

For this purpose, the remaining work is organized as follows: Section 2 presents the introductory concepts discussed in this study, Section 3 presents the methodology followed for the systematic review, Section 4 details the results obtained, Section 5 presents the considerations and Section 6 presents the conclusions.

2. Concepts

To better understand the questions related to the use of IoT in agriculture during the 21st century, it is important go deeper in some fundamental concepts.

2.1. Smart Farming

Smart farming can be defined as the application of supplementary technologies to agricultural production techniques to help minimize waste and boost productivity.

For such, intelligent farms use technological resources that help in various stages of the production process, such as monitoring of plantations, soil management, irrigation, pest control, delivery tracking, etc. [ 16 ]. Such resources include, among others, temperature, luminosity, humidity, pressure, ground chemical concentration, unmanned flying equipment, video cameras, agricultural information management systems, global positioning systems (GPS) and communication networks [ 17 ].

The integration of technological resources into the agricultural production process is a relevant issue. From an economic point of view, the precision agriculture market is expected to have a revenue of US$10 billion in 2023 [ 18 ] with opportunities for technology providers, agricultural equipment and machinery providers, producers and others involved in this business. In addition, smart farms are expected to be able to optimize food production by improving the application of nutrients to the soil, reducing the amount of pesticides and water consumption in irrigation [ 19 ].

2.2. Internet of Things

IoT can be understood as a network of interconnected intelligent devices capable of communicating with each other, generating relevant data about the environment in which they operate. Thus, virtually any device capable of establishing a connection to the Internet can be considered a “thing” within the context of IoT, such as household appliances, electronics, furniture, agricultural or industrial machinery and even people [ 6 ].

Although the idea of IoT is not new, its adoption has increased in recent years, mainly thanks to the development of technologies that support it, among which the improvement of hardware—with the consequent reduction in size and power consumption—improvements in connectivity with the Internet and between devices via wireless connection, cloud computing, artificial intelligence and big data. All these technological components help build a network of devices capable of sharing data and information, as well as acting actively based on network inputs [ 20 ].

According to [ 21 ], the architecture of IoT systems is similar to the architecture of other computer systems but it must take into account the particularities of this paradigm, such as the limited computing capabilities of the devices, identification, detection and control of remote objects.

2.3. Intelligent Agriculture

The IoT architecture proposed in [ 22 ] and [ 23 ], and shown in Figure 1 , presents four layers, considering the main components of an IoT solution: devices, network, services and application.

An external file that holds a picture, illustration, etc.
Object name is sensors-20-04231-g001.jpg

IoT solution architecture that includes 4 layers: perception, transport, processing and application, based on [ 22 , 23 ].

The perception layer relates to the physical devices in the solution and how they interact with each other and with the transport layer. These devices are responsible for collecting data, enabling the communication of the so-called “things”. This can be done by using commercial solutions—such as UAV devices [ 24 ], sensor nodes [ 25 ]—or new devices, developed with components like sensors and single-board computers (SBC)—such as Arduino or Raspberry Pi—to build sensor nodes and communication gateways. Sensor nodes, for example, are used to monitor plant diseases [ 26 ], control environmental variables in greenhouses [ 27 ] and external crops [ 28 , 29 , 30 ], among others. The interaction between the devices that belong to the perception layer and the services that belongs to the processing layer is intermediated by the transport layer and might occur in several ways, such as through the direct communication between sensor nodes and a data processing platform (such as FIWARE [ 31 ], SmartFarmNet [ 32 ] and Thinger.io [ 33 ]) or through a gateway that, besides intermediating the communication between sensor nodes and the internet, acts as a data hub and enables the communication between network protocols that are originally incompatible, such as ZigBee and the Internet [ 28 ].

The transport layer refers to the network and transport capabilities such as network and application protocols [ 22 ]. IoT solutions use network protocols to enable communication between the perception layer and the processing layer. These protocols are used to create the so-called wireless sensor networks (WSN), that allows wireless communication between sensor nodes and applications. Each protocol has important characteristics, such as the data exchange rate, range and power consumption. Based on these characteristics such protocols can be classified in short-range, cellular networks and long-range [ 34 ]. Protocols for short-range networks (e.g., Bluetooth, ZigBee and Wi-Fi) enable communication in short distances. According to [ 34 ], usually such protocols have a high data transmission rate and low power consumption. Therefore, they are used for the communication between devices that are near each other. Protocols for cellular networks (e.g., GPRS, 3G) enable communication in long distances and with a high data transmission rate. However, they have a high power consumption [ 35 ] and costs for licensing [ 34 ]. Protocols for long-range networks (e.g., LoRaWAN and Sigfox) enable communication in very long distances [ 34 ]. These protocols are used to establish the low power wide area networks (LPWAN) due to the fact that they have a low power consumption [ 36 ]. However, the data transmission rate of these protocols is low. Therefore, these protocols are appropriate for use when the solution needs to transmit a few amounts of data in very long distances. Table 1 presents the characteristics of some network technologies used for IoT.

Examples of network technologies used in IoT [ 34 ].

As shown in Table 1 there is a trade-off between coverage, data rate and energy consumption. Considering the technologies for star networks presented in Table 1 , it is possible to notice that energy consumption is higher in technologies with a high data rate and short coverage. On the other hand, LoRa has a small data rate but a large coverage and low power consumption. These questions are especially relevant when considering agriculture because agricultural scenarios often have limited or no energy supply and obstacles for wireless communication.

Different topologies can be used for implementing networks, such as tree, star and mesh. Star networks have a central node and several peripheral nodes. The communication in such topology occurs as follows: peripheral nodes send data directly to the central node. The central node can implement capabilities for routing messages and communicating through multiple network protocols [ 23 ]. Tree networks are composed of router nodes and leaf nodes. Such networks can be understood as a cluster of star networks. Within each cluster, leaf nodes send messages their father node. In mesh networks, in theory, each node can be a router with rerouting capability. Thus, messages in mesh networks are routed hop by hop until reaching the final destination [ 37 ].

Data is sent to the destination through application protocols such as the message queueing telemetry transport (MQTT) [ 38 ] or the constrained application protocol (CoAP) [ 39 ]. MQTT is an open-source messaging protocol that enables communication between constrained devices and in unreliable networks [ 40 ]. MQTT runs over TPC/IP or similar protocols (e.g., Bluetooth) [ 41 ], which makes the use of MQTT appropriate for different IoT solutions. The MQTT protocol, which is based on the publish/subscribe architecture, allows communication between devices to take place in the following way. First, devices publish messages that are structured in topics on a message broker. Then, other devices read these messages by subscribing to relevant topics on the message broker. These topics allow the organization of messages based on categories, subjects, etc. [ 42 ]. The use of MQTT for communication between device allows low coupling between the device that publishes the message and the devices that listen to the messages, the so-called “one-to-many” communication [ 38 ]. Like MQTT, CoAP is a communication protocol optimized for constrained devices and unreliable networks. However, CoAP messages are interchanged using User Datagram Protocol (UDP) and the CoAP protocol is based on the client/server architecture. This architecture requires that a connection is established between devices before any messages are transmitted [ 38 ]. For this reason, communication using CoAP works in the following way. First, the device that sends messages needs to know the address of each device that is expected to receive messages. Then, messages are sent over UDP to the specified address. Due to the use of UDP, CoAP messages are classified accordingly to the required status of confirmation of receival, for example, confirmable or non-confirmable [ 39 ]. The CoAP protocol does not implement a structure of topics for messages. However, a similar approach can be implemented using application programming interface (API). Nonetheless, the use of CoAP creates a high coupling between the device that sends messages and the device that is expected to receive messages, as the communication is “one-to-one” [ 39 ].

The processing layer comprises data storage, visualization and processing resources. In this context, big data allows distributed storage and parallel data processing, enabling the extraction of information in the shortest possible time [ 43 ]. Such information are used as models by artificial intelligence (AI) systems—which, according to [ 44 ], can be understood as the ability of a system to operate as if it had the thinking capacity of a human being—and machine learning—that, according to [ 45 ] is a data processing technique to detect patterns and correlation among complex and unrelated data—for the development of decision support systems and automation of irrigation control systems [ 46 ], monitoring [ 47 ] and diseases detection in crops [ 48 ], for example.

Finally, the application layer comprises IoT applications that, supported by the other mentioned layers, provide management information to farmers, being able to manage the entire production process in the plantations.

Several related works are being developed in recent years. This rich literature has already been analyzed by the academia from multiple perspectives with objective of determining the state of the smart farming development. Thus [ 49 ] presented a systematic review of precision livestock farming in the poultry sector and [ 50 ] made a review of state of the art of technologies used in precision agriculture, focusing in the innovations, measured parameters, technologies and application areas. On the other hand [ 4 ] has focused on the use of big data as a tool to support agriculture, pointing out the main opportunities and challenges of using this technology. Finally, [ 51 ] presented a quantitative literature review on smart farming related papers, helping to outline an overview of academic production related to the subject. In this way, the present work aims to complement such analyses by making a systematic review of IoT solutions applied to smart farming.

To reach the proposed objectives, this study has used the Preferred Reporting Items for Systematic Reviews (PRISMA) methodology, which is a framework developed to support reports and systematic reviews of literature [ 52 ].

As a research strategy, in October 2019 a search was made in the Scopus database through the search tool available on the website. In addition, in June 2020 a new search was made in the same database to include papers published in 2020. The choice of this database took into consideration its scope and relevance in the academia, since this database indexes several journals and catalogues, such as IEEE, ACM and Elsevier, besides being widely used in similar bibliographic reviews, as in [ 4 ] and [ 51 ]. In addition, in February 2020 a new search was performed in the same database. The strategy adopted for the work research in this database looked for terms used to refer to the application of technology in the area of agriculture, such as “Precision Agriculture”, “Precision Farming”, “Smart Farming” and “Smart Agriculture” in association with “IoT” and synonyms terms. The publication date of the articles was not a criterion for ignoring them. The scope of the research was limited to documents such as journal and conference articles, published in English, Portuguese or Spanish, and whose access was fully available. Thus, the resulting search instruction for the database was as follows:

(“Smart Farming” OR “Smart Agriculture” OR “Precision Farming” OR “Precision Agriculture”) AND (“IoT” OR “Internet of Things” OR “internet-of-things”) AND (LIMIT-TO(ACCESSTYPE(OA)))

It should be noted that the quotation marks have the function of ensuring that terms composed of multiple words were searched together, thus preventing words from being considered individually.

After extracting the articles that resulted from the search, they were manually reviewed through the analysis of the title, keywords, abstract and text. Initially, based on this review, the works identified in the researched database were consolidated, thus eliminating duplicate articles.

Subsequently, the articles were validated as to their framing in the objectives proposed for this study and considered valid when: (i) they were not a review or bibliographical research (ii) they were related to theme (iii) they presented a technology or solution based on IoT to solve problems related to agriculture (iv) they were published in English, Portuguese or Spanish. Furthermore, works were also excluded when they were related to livestock activities instead of agriculture.

The process of searching and selecting papers for this study followed the workflow summarized in Figure 2 , where it can be observed that the initial search resulted in a total of 463 articles, which were analyzed, filtered and classified in a narrowing process that culminated in the selection of 159 articles.

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Object name is sensors-20-04231-g002.jpg

PRISMA flowchart of the systematic review on state-of-the-art IoT solutions in smart farming.

In the identification phase 463 articles were selected with the search tool.

During the screening phase, a manual review of the articles was carried out to identify in the titles, abstract and key words the papers adherent to the objectives proposed for this study, following the criteria mentioned in this section. Among these, 257 were considered invalid and discarded. About 62% of the discarded items did not consider smart farming to be the focus of the work, although some presented improvements for IoT that could benefit smart farming indirectly. Additionally, almost 31% of the discarded papers were studies or literature reviews related to smart farming and the use of various technologies. A smaller number of papers related to smart farming but not addressing IoT (about 5%) and papers where the abstract or text were not available (about 2%) were also discarded.

During the eligibility phase, the content of the 206 resulting articles were reviewed and the papers were classified using the same criteria used in the previous step. In this phase 47 articles were discarded. Among the discarded articles 29% were not related to IoT and 30% were not related to smart farming. The other 41% of the discarded papers were paper reviews or papers without content available. This analysis resulted in 159 articles considered eligible which were included as a sample for this study.

4. Discussion

Based on the results obtained in the analysis of the articles considered for this study, it was possible to observe a growth trend in the number of publications related to IoT and smart farming since 2011, with special emphasis from 2016 onwards, as shown in Figure 3 .

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Classification of reviewed papers according to the year of publication.

It is possible to observe an expressive increase of 278% in the number of published papers in 2017/2018. It’s also possible to observe a very similar number of published papers in 2019/2020, until the first semester of 2020. The amount of published papers in recent years evidences the increasing in discussion and the relevancy of the topic IoT applied to smart farming.

Within the reviewed papers it was identified the main scenarios and environments of agriculture. As shown in Figure 4 , such scenarios can be divided into indoor and outdoor. Environments for indoor scenario are protected from climatic impacts, such as solar radiation, rain and wind. Examples of environments for indoor scenarios include greenhouse, hydroponics, crop beds, pots, etc. In contrast, environments for outdoor scenario are more susceptible to climatic impacts. Examples of environments for outdoor scenario are arable lands, orchards and generic outdoor plantation.

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Typical scenarios in agriculture can be divided in indoor and outdoor. Indoor agriculture includes environments such as greenhouse, hydroponics and crop beds. Outdoor agriculture includes environments such as orchards and arable lands. IoT solutions that may be applied to multiple environments are referred to as “Generic”.

4.1. Application

Within the reviewed papers it was also identified that the most common applications of IoT solutions for smart farming are:

  • Chemical control (e.g., pesticides and fertilizers).
  • Crop monitoring.
  • Disease prevention.
  • Irrigation control.
  • Soil management.
  • Supply chain traceability
  • Vehicles and machinery control.

Table 2 presents the reviewed papers, grouped by agricultural environment and application of the IoT solution. It is worth mentioning that several IoT solutions presented on the reviewed papers could be applied to multiple environments ( Figure 4 ). Thus, such IoT solutions are classified as “Generic”. Additionally, the “Others” column in Table 2 includes papers whose IoT solutions were developed for agricultural environments that were less mentioned, such as pots, crop beds, etc. It is possible noting a predominance in projects where the application is for crop monitoring, irrigation management, and disease prevention.

Smart farming, application and environments.

As shown in Table 2 , the most common application of IoT solutions for smart farming is crop monitoring. Moreover, as shown in Table 2 , these solutions have been developed for multiple agricultural environments, such as arable lands, orchards, greenhouses, etc. The fact that this type of application is so common in agriculture can be justified by the relevance that crop monitoring has for farmers. IoT solutions developed for monitoring crops focused on collecting environmental data of plantations (such as temperature, humidity, luminosity, etc.). Farmers can use these data to obtain a better insight of the plantations. For example, such data was used to determine the vigor of rice [ 47 , 58 ], alfalfa [ 30 ] and maize [ 57 ] crops and to control the environmental conditions of greenhouses [ 99 , 100 , 102 , 104 ]. Similarly, IoT solutions for irrigation control has also been developed for multiple agricultural environments, as demonstrated in Table 2 . Such IoT solutions aimed to optimize the use of water resources in agriculture in different ways, such as by simply using sensors for measuring the soil moisture and using these data for controlling the irrigation source [ 34 , 139 ] or in a more sophisticated way, by combining humidity data with datasets of weather to determine the amount of water required during the irrigation [ 140 ]. IoT solutions for disease prevention aimed to identify and prevent diseases on plantations. For this purpose, these IoT solutions collected multiple environmental and plantation data, such as images of plants [ 129 , 132 , 134 ], sounds [ 135 ], temperature, humidity, etc. [ 131 , 137 ]. These data were processed with different approaches, such as image processing [ 129 , 134 ] or artificial intelligence [ 132 , 137 ]. For example, the IoT solution developed in [ 129 ] processes images collected from a sugarcane crop and identifies diseases on the leaves of plants. In addition, [ 135 ] developed an IoT-enabled device that captures sounds produced by larvae inside trees. IoT solutions for chemical control presented in Table 2 aimed to optimize the application of fertilizers and pesticides on plantations. For this purpose, these IoT solutions collect data (such as nitrogen, salinity or PH) from the crops. Based on the collected data, such IoT solutions can identify crop areas that may require the application of fertilizers or pesticides. For example, in [ 54 ] aerial images of crops are processed to determine the nitrogen concentration in a large plantation. These images are useful to determine the specific region that requires fertilizer. In addition, [ 55 ] developed an automated robot that optimizes the application of pesticides in greenhouse cultivations. IoT solutions for soil management aimed to identify different soil attributes used for planting. For example, such IoT solutions are used to measure the soil moisture [ 163 ], to identify the water consumption pattern [ 159 , 161 ] and to identify the nutrients of the soil [ 158 ]. IoT solutions for vehicles and machinery control focused on collecting data of and managing agricultural equipment and machinery such as tractors, harvesters and trucks. For this purpose, IoT solutions had to deal with the characteristics inherent to agricultural equipment, such as mobility. Data from the equipment itself, such as implement status, engine performance, or speed are collected using sensors [ 41 ] to optimize their maintenance cycle. Additionally, due to the mobility of agricultural equipment, opportunistic computing was used to collect data from remote crop areas by using sensors coupled to tractors [ 174 ].

Each agricultural environment presented in Table 2 brings its own challenges for the projects, which includes the environment impact on the communication between sensors, either by the distance between the sensor nodes [ 25 , 105 , 180 ], by the lack of communication in the croplands [ 98 , 174 ] or even by the impact of vegetation in the signal propagation [ 70 , 175 ]. Furthermore, as indicated in [ 28 ], climatic elements—such as rain, snow or solar radiation—have influence on both the planting and the sensor nodes.

To cover these scenarios commercial electronic sensors are used by 96% of the reviewed papers. This expressive usage can be justified by the fact that such sensors are affordable, certified, ready-to-market and meet the main monitoring needs in IoT solutions for smart farming. Such sensors are used for collecting real-time data about multiple agricultural parameters, such as climatic data, substrate information, luminosity, CO2 concentration and images through cameras and multispectral sensors, as shown in Table 3 . Moreover, several papers (4%) focused on developing custom-made sensors for monitoring specific agricultural aspects, such as soil nutrients (e.g., nitrate [ 158 ]) and leaf evapotranspiration for measuring the hydric stress in tobacco crops [ 81 ].

Types of physical sensors and use in smart farming.

Different types of sensors are used in agriculture for collecting data from different aspects of agriculture such as crop monitoring, substrate monitoring and environment monitoring.

As presented in Table 3 , different types of sensors were used in IoT solutions for smart agriculture to collect data from multiple aspects of agriculture, such as the crop, substrate, environment and other. For this purpose, as shown in Table 3 , for environment monitoring electronic sensors were used in IoT solutions to collect environmental data, such as temperature, humidity and luminosity [ 104 , 109 , 114 ]. In addition, for substrate monitoring electronic sensors were used to collect data from the substrate (e.g., soil and water), such as temperature, moisture and nitrogen. Likewise, pH sensors were used for measuring the acidity or the alkalinity of the water in hydroponics cultivations. For crop monitoring, cameras and multispectral sensors were used to collect images of crops. These sensors can be installed on an UAV to obtain aerial images of large plantations [ 47 , 57 , 58 ] or used in robots to retrieve a detailed image of the leaf of a plant [ 111 ].

4.2. Perception

The choice of hardware is a very important aspect of the IoT project development because it impacts the costs and the technologies that can be used. 60% of the reviewed papers mentioned the hardware used to support the IoT solution. Furthermore, SBCs were mentioned by 40% of the reviewed papers. The use of SBCs can be justified by the fact that these devices are affordable and versatile [ 38 ], enabling the development of custom-made IoT devices. For example, some SBCs such as Arduino has an integrated development environment (IDE). This IDE enables the development of custom programs to be installed as firmware on the Arduino boards [ 184 ]. Similarly, Raspberry Pi is compatible with several operating systems, such as Raspbian, Ubuntu Core or Mozilla Web Things [ 185 ]. Some of these operating systems are open-source, which allow for the customization of its source-code. Besides, these operating systems support applications developed with programming languages such as Python [ 26 ]. Furthermore, the capabilities of SBCs can be extended by associating them with other hardware components, such as sensors or transceivers. This characteristic makes SBCs able to work as gateways or sensor nodes in IoT solutions. Among the papers that mentioned SBCs, 82% mentioned the use of Arduino, Raspberry Pi and ESP boards (such as ESP8266, ESP12 and ESP32). Table 4 presents the application of embedded system platforms and UAV devices in smart farming.

Embedded system platforms and UAV devices in smart farming.

As shown in Table 4 , IoT-enabling devices are used for multiple applications on IoT solutions for smart farming. SBCs were used both as sensor nodes and gateways. Table 4 reveals that Arduino was the most commonly used embedded system platform among the reviewed papers. The extensive use of Arduino can be justified by the fact that Arduino is open-source hardware that enables the development of different devices through the use of boards that extend their native functionality. Table 4 also shows that embedded system platforms have been more widely used in IoT solutions for crop monitoring. As sensor nodes, for example, in [ 124 ] sensors for collecting environmental data such as soil humidity, solar radiation and rain are connected to an Arduino Uno. The Arduino is, then, used to monitor the health of a vineyard. Likewise, in [ 117 ] a Raspberry Pi is used to manage the temperature and air humidity of a greenhouse. IoT devices are also used as gateways to connect short-range WSN with the internet by using long-range communication protocols. For example, in [ 127 ] a gateway is used to connect WSNs using 3 different protocols (ZigBee, Bluetooth and Wi-Fi) with a remote server by using 3G. In [ 84 ] a LoRaWAN gateway obtains data from sensor nodes using LoRa and retransmits this data to a cloud-hosted platform by using 4G. 3G and 4G are cellular network technologies that, as discussed in Section 2.3 , enable communication in long distances and with a high data transmission rate. These technologies will be discussed with more details in Section 4.3 .

In addition, Table 4 also reveals that UAV is widely used by IoT solutions for monitoring crops, disease prevention and chemical control. The use of UAV for crop monitoring is due to the fact that UAV has the potential to accelerate and reduce the cost of monitoring extensive crops. For this purpose, cameras and multispectral sensors are attached to UAV devices that are used to obtain aerial images from large crops. Such images are processed by the IoT solution to calculate agricultural parameters, such as the leaf area index (LAI). The LAI is a parameter used to determine the vegetation coverage within a specific area. LAI, combined with other parameters, can be used to evaluate the amount of nitrogen in rice crops [ 58 ], determine the vigor of rice and maize [ 47 , 57 ] crops and detect diseases in sugarcane crops [ 129 ]. Moreover, UAV devices are used in [ 46 ] to optimize the application of pesticides and fertilizers in arable lands.

4.3. Network

Data obtained with sensor nodes are usually sent to the destination (e.g., database, server, IoT platform) through a wired or wireless network. Within the reviewed papers, 60% have mentioned the network protocol used in the IoT solution. Among the mentioned network protocols, CAN and Ethernet were the most used ones for wired networks. Likewise, LoRaWAN and protocols for cellular network (e.g., GPRS, 3G, etc.) were the most used protocols for long-range wireless networks. Analogously, ZigBee, Wi-Fi and Bluetooth were the most used protocols for short and mid-range wireless networks. Table 5 shows network protocols used for the IoT solutions within the reviewed articles.

Use of network protocols in smart farming for different farming scenarios.

As shown in Table 5 , several network protocols are used in different environments of agriculture (e.g., arable land, greenhouse, orchard) to enable communication between IoT solution devices, such as sensor nodes and gateways. Such network protocols enable the creation of short or long-range networks. Table 5 reveals that for short and middle-range communication, IoT solutions of the reviewed papers used different technologies, such as Wi-Fi, ZigBee and Bluetooth. Moreover, it is possible to observe in Table 5 that Wi-Fi is the most common network technology for communication within the analyzed articles. This extensive use of Wi-Fi can be justified by the fact that Wi-Fi is a ubiquitous technology and, therefore, easy to implement. However, due to the higher energy consumption of Wi-Fi, low-energy consumption technologies, such as ZigBee or Bluetooth, are also extensively used. For example, [ 62 ] used ZigBee to send images from a plantation to a remote server and [ 188 ] developed a sensor node that uses Bluetooth to deliver monitoring information from the farm directly to an application installed on a smartphone. Table 5 also demonstrates that IoT solutions of the reviewed papers used cellular networks, Sigfox, or LoRaWAN for long-range networks. Cellular networks are prevalent in IoT solutions for Smart Farming. This can be justified by the fact that cellular networks allow the communication of IoT devices in long distances and with a high data rate. For example, [ 141 ] uses cellular network to send data collected from humidity sensors to a cloud-based platform and to control an irrigation system. Similarly, Sigfox and LoRaWAN enable communication in very long distances while requiring low energy to operate. Based on these characteristics, Sigfox and LoRaWAN were used for long-range communication, as an alternative to cellular networks or in regions where there was no cellular network coverage. Sigfox is used in [ 34 ] as the network protocol of an IoT solution used to control the irrigation of a plantation. Likewise, in [ 137 ] the LoRaWAN is used to send data from multiple sensors installed in a greenhouse to a remote platform.

Besides the distance between sensor nodes, gateways, and other network elements, the vegetation itself can be an obstacle for sensor communication, as demonstrated by [ 175 ] and [ 70 ] who analyzed the impacts on signal propagation on 433 MHz and 2.4 GHz frequencies in rice plantations and an orchard. An additional challenge for greenhouses arises from the high density of sensors, which can lead to interference in the wireless signal due to proximity [ 105 , 112 , 180 ]. To mitigate this problem wired networks, such as CAN [ 100 ] or Ethernet [ 122 ], can be used. As shown in Table 5 , these technologies have been more used in greenhouses, because usually this type of agricultural environment is more appropriated for implementing wired networks. Moreover, [ 112 ] investigated the path loss on wireless signals and concluded that the proper positioning of directional antennas can optimize the number of sensory nodes required for monitoring a greenhouse.

Network topology is another important aspect of an IoT solution. According to [ 61 ] the topology of sensor networks can be star, tree (or cluster) or mesh. The network topology impacts the distance between the sensor nodes and the destination and, consequently, the number of sensor nodes in the WSN [ 190 ]. For example, star networks are composed of a central node (coordinator) and several peripheral nodes. In such topology, peripheral nodes send data to the central node [ 93 ]. Therefore, the maximum distance between the peripheral nodes and the central node is limited by the maximum distance allowed by the physical layer communication standard. On the other hand, as discussed in Section 2.3 , in mesh networks each node has routing capability, hence extending the network coverage by allowing multi-hop communications [ 191 ]. Based on the architecture of the IoT solution and on the project description it was possible to identify the topology adopted by 61% of the reviewed papers. For example, a star topology is used in [ 34 ] for connecting sensor nodes to a central node using the LoRa protocol. This central node acts as a gateway and retransmits messages to a cloud-based application that controls an irrigation system using Sigfox. Also, in [ 107 ] the star topology is used to connect multiple sensors within a greenhouse. Such sensors use the ZigBee protocol to send messages to a central node, which acts as the network gateway. Mesh networks are considered more complex to be implemented but also more reliable due to the redundancy of communication between the sensor nodes [ 105 ]. Such topology is used in [ 105 , 107 ] for monitoring a greenhouse. Tree (or cluster) networks combine multiple star networks. Both [ 61 ] and [ 178 ] implement a cluster network for monitoring crops. In [ 61 ] sensor nodes collect information from a crop and send messages to a router node. This router node acts as the gateway of the cluster and retransmits the message to the main router node of the network. In [ 178 ] several router nodes are deployed in the crop area in order to optimize the energy consumption of sensor nodes.

Furthermore, embedded system platforms have been used to support network topologies. The chart in Figure 5 presents the distribution of embedded system platforms by network topology or device connection type. It is worth mentioning that although point to point is not a network topology, this type of device connection was used in several IoT solutions within the review articles. As shown in Figure 5 , Raspberry Pi is often used in IoT solutions implementing the star network topology. Arduino is the embedded system platform used in multiple types of network topology or device connections. Additionally, Arduino is the most frequently used embedded system platform to support star network topology and point-to-point communication. Finally, ESP-based devices include devices that use system-on-a-chip (SoC) modules such as ESP-32 and ESP8266 (Espressif Systems, Shanghai, China). ESP-based devices are often used in IoT solutions that implement star network topology or point-to-point communication.

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Distribution of IoT-enabling devices by network topology or device connection type within the reviewed papers.

As mentioned in Section 4.2 , embedded system platforms can be used to build gateways or sensor nodes. As shown in Figure 5 the use of Raspberry Pi, Arduino and ESP stand out, probably because such embedded system platforms are cost-effective [ 38 ] and enable different network protocols (e.g., ZigBee, Wi-Fi and Bluetooth) with the use of transceivers. This characteristic allows such embedded system platforms to act as sub-nodes and central nodes in a star network [ 27 , 30 , 34 ] or as router nodes in mesh and cluster networks [ 106 , 178 ].

IoT devices transmit information to cloud-based platforms or applications through application protocols [ 109 ]. Such protocols can follow the publisher/subscriber architecture which, as mentioned in Section 2 , are appropriate for devices with limited computing resources. Among the application protocols used in the reviewed papers HTTP, MQTT and CoAP stand out. Such application protocols are useful to enable compatibility between non-standardized IoT devices and IoT platforms. For example, SmarFarmNet developed in [ 32 ] adopts the “bring your own IoT device” concept by implementing loosely coupled application protocols such as MQTT and CoAP. Furthermore, although HTTP is not a specific protocol for machine-to-machine (M2M) communication, its use associated with REST APIs enables low coupling between IoT devices and applications, analogous to MQTT, for example. However, as [ 109 ] concludes, the MQTT protocol is preferable for smart farming applications due to its resiliency, interoperability across different network protocols and transmission rate.

Finally, although the power consumption is not an exclusive topic within the transport layer, according to [ 178 ] the highest power consumption for IoT devices within a WSN occur during the transmission of data. This review identified several approaches for optimizing the power consumption in IoT solutions for smart farming. Among the identified solutions are the use of low energy protocols (e.g., BLE, ZigBee, Sigfox), reduction of data transmission in sensor nodes by an optimized duty cycle [ 177 , 178 , 192 ] and the use of message routing approaches that are more energy-efficient [ 72 , 193 ].

4.4. Processing

Among the analyzed papers it was possible to observe that initially, the main objective of IoT solutions was to collect and store data from sensor nodes. However, in more recent years, it is possible to observe an increasing number of IoT solutions that used supplementary techniques and technologies to treat the collected data, such as cloud computing and big data. Likewise, it is possible to observe an increasing number of works that used simultaneously two or more techniques or technologies for processing data. As shown in Figure 6 , the most cited technologies within the reviewed papers are cloud computing (34%), machine learning (15%), big data (13%), and artificial intelligence (9%).

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Techniques and technologies for data-processing in smart farming identified within the reviewed papers. In recent years, the use of modern processing techniques, such as artificial intelligence and big data became more common. IoT applications relies on cloud computing for storing and processing the big data of agricultural information collected by IoT devices.

Table 6 presents IoT solutions that relied on cloud-based platforms for processing data and highlights the main data processing techniques (e.g., Artificial Intelligence, Big Data, etc.). The column “Other/Not identified” comprehends IoT solutions that have used cloud-based platforms but have either (i) used any of the data processing technologies identified by other columns on Table 6 or (ii) not explicitly mentioned the type of data processing technology that was adopted.

IoT-enabling platforms and data processing technologies used in smart farming within the reviewed papers.

Table 6 reveals that the most found cloud-based platforms in the reviewed papers are ThinkgSpeak, FIWARE, Ubidots, SmartFarmNet, AWS IoT and Thinger.io. In particular ThingSpeak is the most used cloud-based platform across all the reviewed papers, due to the fact that this platform is open-source with low infrastructure requirements [ 34 ]. In addition, Table 6 shows that AWS IoT was used with a higher number of data processing techniques. Not all cloud-based platforms offer the same set of functionalities, but in general, they have capabilities for data storage [ 30 , 94 , 102 , 122 , 132 ], processing [ 194 ] and visualization [ 102 ] and action control on farms [ 34 ]. Furthermore, Table 6 also reveals that, even though there are multiple cloud-based platforms, several reviewed papers developed their own cloud-based platform for the IoT solution.

Cloud-based platforms provide scalability for IoT solutions by relying on cloud computing to process and data. For instance, some platforms shown in Table 6 , such as Thinger.io [ 33 ], are built entirely on top of infrastructure services provided by cloud providers (e.g., Amazon AWS and Microsoft Azure). Also supported by such services, the platforms make available data analysis modules with graphics and panels that allow real-time monitoring of the information obtained or the creation of customized panels from the integration of multiple data [ 33 ].

Due to the scalability provided by these platforms, the large amount of data generated by the sensors is stored in databases to form the so-called big data, an unstructured set of information that is used to generate information about crops. According to [ 197 ] big data demands the use of technologies to optimize the processing time due to the large volume of information. For example, Hadoop—a parallel database for big data applications—proved to be efficient when analyzing the rainfall index data from several meteorological stations [ 197 ].

IoT solutions use different types of techniques and technologies for processing the collected data. Table 7 presents commonly used technologies per applications as identified in the reviewed papers. Column “Other Technologies” encompasses all the technologies that are not identified by any of the other columns in Table 7 .

Technologies and application in smart farming.

Table 7 reveals that the most commonly used technologies to support data processing are artificial intelligence, machine learning, and big data. The use of these technologies is related to their ability to process large amounts of information in a short time. In addition, Table 7 also shows that crop monitoring is the most common type of application for IoT solutions that have used data processing technologies. Moreover, crop monitoring is also the type of application that used the most different technologies for data processing. This can be understood by the fact that usually IoT solutions for monitoring crops collect a bunch of data and rely on machine learning and big data to process such data.

As demonstrated in Table 7 , bigdata was used for different applications in IoT solutions, such as crop monitoring, soil management and irrigation control. For example, supported by big data, in [ 142 , 161 , 194 ] the soil moisture data gathered by physical sensors were related to data made available in datasets, such as the NASA Prediction of Worldwide Energy Resources (POWER) [ 198 ]—which contains meteorological data—purchase and sale values of crops, information from the user and government agencies to optimize the amount of water in irrigation cycles, support the farmer in the acquisition of agricultural inputs—such as seeds and fertilizers—and generate information and perspectives about other activities related to agriculture. Big data was also used by [ 56 ] in the development of a decision support system to provide irrigation and monitoring advice to farmers from a knowledge base created with data obtained by physical sensors (e.g., temperature, soil moisture) and virtual sensors (e.g., soil type, season). Virtual sensor is a type of software that, given the available information, processes what a physical sensor otherwise would [ 199 ].

In addition, automatic management with IoT depends on the manipulation of multiple variables. Initially, the simple observation of soil humidity and temperature can be used to trigger irrigation or cooling systems, as proposed by [ 187 ]. Nevertheless, greenhouse management can be more complex. As shown in [ 104 , 107 , 120 ], greenhouse parameters like temperature and humidity are closely tied and changing one of them can affect several others.

Fuzzy logic, as indicated in Table 7 , was used in IoT solutions applications that need to handle multiple variables, such as irrigation control and monitoring crops. For this purpose, [ 120 ] uses fuzzy logic to handle multiple variables of temperature and humidity into a greenhouse and determine when a cooling system and an irrigation system should be started. Similarly, [ 73 ] uses fuzzy logic to optimize the number of sensors for monitoring soil temperature and moisture. Machine learning was also used in data processing by [ 46 ] to predict environmental conditions based on the forecast values of weather, humidity, temperature and water level and thus to control an irrigation system, by [ 47 ] to combine multiple parameters obtained from images, such as color and texture indices and by [ 48 ] to identify marks on the plants and, thus, to identify possible diseases. Similarly, in [ 58 , 125 ] it was used to detect diseases, identify growth stages and the health of plantations.

Similarly, as shown in Table 7 , IoT solutions used computer vision for applications that need to deal with image processing, such as crop monitoring and diseases prevention. It was also possible to observe in the reviewed papers the use of computer vision to identify and classify elements in images obtained by cameras, enabling the identification of fruit in an orchard [ 200 ] or the existence of diseases and pests in plantations [ 48 , 129 , 133 ]. Additionally, in [ 133 ] computer vision was used as a monitoring tool to detect the presence of insects that can cause diseases in olive groves and in [ 48 ] the same technique was employed to analyze diseases that cause morphological deformations in plants. Additionally, computer vision was used in crop management systems, for example in [ 134 ] where it was implanted in a robot equipped with a camera and other sensors, being able to obtain images of vegetation and, through computer vision, detect weeds in plantations and eliminate them. Similarly, in [ 111 ] a robot can identify a plant and interact with the environment to irrigate it, if necessary.

Finally, blockchain proved to be an opportune technology for systems that need to implement traceability of the supply chain, as shown in Table 7 . According to [ 171 ] blockchain is a global public distributed ledger that records all transactions between users. In fact, this type of control is relevant for agriculture in several aspects, such as food safety, guarantee of origin or cost reduction. To ensure information security, this technology was proposed by [ 30 , 167 , 171 ] for agricultural product traceability. For example, in [ 171 ] an IoT solution uses blockchain to record information regarding the tea production based on 5 business processes: production plan, quality inspection, sales processing, product quality inspection and order delivery. In [ 167 ] a production tracking system for agricultural cooperatives have been developed. In [ 30 ] a similar system is being proposed but still in development stage.

5. Considerations

IoT solutions for smart farming take advantage of the scalability provided by platforms and cloud computing to store large amounts of data obtained by sensors. These big data of specific information may be processed with artificial intelligence techniques—such as machine learning—to improve the management of smart farming. For example, the processing of big data may be used to obtain crop insights, optimize water resources and increase the crop quality by preventing disease and reducing the amount of chemical products employed. Crop monitoring solutions use SBC (e.g., Arduino and Raspberry Pi) or UAV (e.g., drones) together with sensors (e.g., humidity, temperature, CO 2 or image) to collect data in indoor or outdoor environments.

Different types of network connections are used for communication between IoT devices, such as wired and wireless connections. The review shows that wired networks, such as CAN and Ethernet, are used for indoor agriculture (e.g., greenhouses). The use of wired network on indoor agriculture may be justified by the fact that in this scenario the physical components of the network are less susceptible to climatic agents impacts. Likewise, generally distance between sensor nodes in indoor agriculture enables this type of connection. Wireless connection, on the other hand, is used both in indoor and outdoor agriculture. Wi-Fi is the most mentioned protocol within the analyzed projects, due to its ubiquitous utilization in the daily life. However, power consumption and signal range characteristics may limit use of Wi-Fi in larger projects or in projects with power restrictions. To overcome the power consumption issue, energy-efficient protocols such as ZigBee, BLE or LoRa are used for communication in wireless networks.

Furthermore, it is worth mentioning that this review investigated papers where the IoT solution for smart farming was applied to agriculture only. However, the use of IoT for smart farming can also be applied to other activities related to farming, such as livestock [ 201 ]. Moreover, despite the fact that power-supply in IoT solutions for smart farming does not represent a specific layer of an IoT solution architecture [ 17 , 18 ], this topic has been covered in some of the reviewed papers. For example, [ 72 , 177 ] proposed improvements in algorithms for message routing and in duty cycles in sensor nodes. These approaches contribute to the reduction of power consumption by IoT devices. Similarly, a mission-based approach was used in [ 53 ] to optimize the power consumption in UAV. This approach was used to identify the most efficient path for a set of drones. Likewise, [ 177 ] proposed an intelligent activity cycle to improve the performance of data aggregators in terms of energy efficiency on cloudy days.

6. Conclusions

This work presented a systematic review of the state-of-the-art of IoT adoption in smart agriculture and identified the main components and applicability of IoT solutions. This review reported a change in the treatment of data in recent works: while previous work showed that the majority of decision support systems used simple processing mechanisms to handle data collected in real-time, more recent work showed an increasing number of management systems that use complementary technologies that rely on cloud and big data computing for processing large amounts of data. Furthermore, it was observed in this review that in recent work the use of artificial intelligence and image processing techniques has become more common to improve the management of smart farming. From the identified applications of IoT for smart farming it was observed that the most common application is the monitoring of crops. This review also showed that different network protocols may be simultaneously used in IoT solutions for smart farming. In addition, the comparison of types of network connections used in IoT solutions for smart farming revealed that wired networks are used in indoor scenarios (e.g., greenhouse) while wireless networks are used both in indoor and outdoor scenarios (e.g., arable lands, orchards). Moreover, the review discussed in this work suggests the increasing relevance of IoT solutions for smart farming. Future work may extend this review by including other relevant articles and complementary analysis of project costs, usability and regional challenges intrinsic to IoT applications. Another important future research direction could be the analysis of the edge and fog computing usage in smart agriculture as a way to deal with challenges associated with traditional centralized cloud solutions such as high communication latencies, lack of support for real-time reaction to detected events, large bandwidths, etc.

Acknowledgments

The authors acknowledge to the Mobile Computing Laboratory of the School of Technology and Management of the Polytechnic Institute of Leiria and to the Computer Science and Communication Research Center for the facilities granted in the implementation of part of this work, in the context of the Smart IoT Ecosystems research line.

Author Contributions

All authors contributed equally to this work. Conceptualization, E.N., N.C., A.P.; data curation, E.N., N.C., A.P.; formal analysis, N.C., A.P.; funding acquisition, A.P.; investigation, E.N., N.C., A.P.; methodology, N.C., A.P.; resources, A.P.; supervision, A.P.; validation, N.C.; writing—original draft, E.N., N.C.; writing—review & editing, E.N., N.C, A.P. All authors have read and agreed to the published version of the manuscript.

This work was partially supported by Portuguese Fundação para a Ciência e a Tecnologia–FCT, I.P., under the project UIDB/04524/2020” and by Portuguese National funds through FITEC–Programa Interface, with reference CIT “INOV–INESC Inovação–Financiamento Base”.

Conflicts of Interest

The authors declare no conflict of interest.

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  • Publication Tackling Gender Discriminatory Inheritance Law Privately: Lessons from a Survey Experiment in Tunisia ( Washington, DC: World Bank, 2024-02-05 ) Hauser, Christina Sarah When reform of gender discriminatory law fails, individual action can offer a second-best solution. As most Muslim-majority countries, Tunisia applies Islamic inheritance law, systematically favoring sons over daughters. By making gifts to their daughter, parents can privately attenuate gender discrimination in inheritance. This study investigates to what extent gifting can represent an alternative to legal reform and for whom. Within a randomized experiment, this study tests whether providing information on public support for inheritance law reform and/or the possibility to make a gift to one’s daughter has a causal impact on individual attitudes towards women’s right to inheritance. The overall evidence on the effectiveness of the proposed informational treatments to encourage gifting is mixed. However, approval of gifting daughters is high—especially among the wealthy. Men are more likely to gift than women. By contrast, demand for legal reform is significantly higher among women and individuals with low educational attainment. The findings thus suggest that gifting indeed represents an alternative to legal reform; but mostly for a relatively well-off subset of the population, leaving the agency to the traditionally male head of the family.
  • Publication Globalization, Dutch Disease, and Vulnerability to External Shocks in a Small Open Economy: The Case of Lebanon in 1916 and 2019 ( Washington, DC: World Bank, 2024-01-31 ) Bou-Habib, Chadi This paper investigates the similarities between the economy of 1912 Mount Lebanon on the eve of the famine of 1916 and the economy of 2004 Lebanon that set the stage for the major economic and social crisis of 2019. A simple general equilibrium simulation shows that, as long as the Lebanese economy remains reliant on foreign inflows, crises will persist, with different manifestations. Regardless of the period considered, foreign inflows increase domestic prices and induce real appreciation. Low productive capacities and insufficient job creation lead to high emigration. Emigration increases the reliance on foreign inflows, which in turn increase domestic prices and reduce competitiveness, hence triggering further emigration and further reliance on foreign inflows. Income and prices increase, but exports decline, and growth remains volatile. The interruption of the flows of capital and goods and the impossibility to migrate due to the First World War drove Lebanon into starvation in 1916. The interruption of inflows of capital in 2019 led to a major crisis and massive outmigration, as predicted through the simulations based on the structure of the Lebanese economy in 2004. The simulations effectively capture the impact of external shocks on the Lebanese economy and closely align with the actual changes in economic variables during 2005 to 2020.
  • Publication Missing Evidence: Tracking Academic Data Use around the World ( World Bank, Washington, DC, 2024-01-25 ) Stacy, Brian ; Kitzmüller, Lucas ; Wang, Xiaoyu ; Mahler, Daniel Gerszon ; Serajuddin, Umar Data-driven research on a country is key to producing evidence-based public policies. Yet little is known about where data-driven research is lacking and how it could be expanded. This paper proposes a method for tracking academic data use by country of subject, applying natural language processing to open-access research papers. The model’s predictions produce country estimates of the number of articles using data that are highly correlated with a human-coded approach, with a correlation of 0.99. Analyzing more than 1 million academic articles, the paper finds that the number of articles on a country is strongly correlated with its gross domestic product per capita, population, and the quality of its national statistical system. The paper identifies data sources that are strongly associated with data-driven research and finds that availability of subnational data appears to be particularly important. Finally, the paper classifies countries into groups based on whether they could most benefit from increasing their supply of or demand for data. The findings show that the former applies to many low- and lower-middle-income countries, while the latter applies to many upper-middle- and high-income countries.
  • Publication What Explains Global Inflation ( World Bank, Washington, DC, 2023-12-21 ) Ha, Jongrim ; Kose, M. Ayhan ; Ohnsorge, Franziska ; Yilmazkuday, Hakan This paper examines the drivers of fluctuations in global inflation, defined as a common factor across monthly headline consumer price index (CPI) inflation in G7 countries, over the past half-century. It estimates a Factor-Augmented Vector Autoregression model where a wide range of shocks, including global demand, supply, oil price, and interest rate shocks, are identified through narrative sign restrictions motivated by the predictions of a simple dynamic general equilibrium model. The authors report three main results. First, oil price shocks followed by global demand shocks explained the lion’s share of variation in global inflation. Second, the contribution of global demand and oil price shocks increased over time, from 56 percent during 1970–1985 to 65 percent during 2001–2022, whereas the importance of global supply shocks declined. Since the pandemic, global demand and oil price shocks have accounted for most of the variation in global inflation. Finally, oil price shocks played a much smaller role in global core CPI inflation variation, for which global supply shocks were the main source of variation. These results are robust to various sensitivity exercises, including alternative definitions of global variables, different samples of countries, and additional narrative restrictions.
  • Publication Fiscal Policy Effects on Poverty and Inequality in Cambodia ( World Bank, Washington, DC, 2023-09-25 ) Karamba, Wendy ; Myck, Michal ; Trzcinski, Kajetan ; Tong, Kimsun This study assesses the short-term impact of fiscal policy, and its individual elements, on poverty and inequality in Cambodia as of 2019. It applies the Commitment to Equity methodology to data from the Cambodia Socio-economic Survey of 2019/20 and fiscal administrative data from various government ministries, departments, and agencies for the assessment. The study presents among the first empirical evidence on the impact of taxes and social spending on households in Cambodia. The study finds that: (i) Cambodia’s 2019 fiscal system reduces inequality by 0.95 Gini index points, with the largest reduction in inequality created by in-kind transfers from spending on primary education; (ii) while Cambodia’s fiscal system reduces inequality, the degree of inequality reduction is small in international comparison; and (iii) low-income households pay more in indirect taxes than they receive in cash benefits in the short term to offset the burden. As a result, the number of poor and vulnerable individuals who, in the short term, experience net cash subtractions from their incomes is greater than the number of poor and vulnerable individuals who experience net additions. Fiscal policy can deliver more net benefits to poor and vulnerable households through expanding social assistance spending. Cambodia has embarked on this expansion during the coronavirus pandemic, bringing it closer in line with comparators.

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Developing a composite weighted indicator-based index for monitoring and evaluating climate-smart agriculture in India

  • Original Paper
  • Published: 17 February 2024
  • Volume 29 , article number  12 , ( 2024 )

Cite this article

  • Shiv Narayan Singh 1 ,
  • Jigyasa Bisaria 1 ,
  • Bhaskar Sinha   ORCID: orcid.org/0000-0002-2083-7540 1 ,
  • Maneesh Kumar Patasaraiya 2 &
  • P. P. Sreerag 2  

Climate change is a serious concern that threatens global food security in several ways and exerts pressure on the already stressed agriculture system. The future prediction of a decline in the yield of major food grains like rice, wheat, and maize due to adverse impacts of increased warming and other climatic variabilities paves the way to shift the existing agriculture practices to more resource-efficient agriculture. This has entailed the government promoting climate-smart agriculture with its triple objectives, i.e. adaptation, mitigation, and food security. The current study developed a composite weighted indicator-based index to compute climate smartness score (CSS) at the farm level in India and tested its effectiveness in measuring the climate resilience of the farmers in Sehore, Satna, and Rajgarh districts of Madhya Pradesh, India, who adopted climate-smart practices in a pilot project. Thirty-four indicators grouped in five dimensions were selected from relevant peer-reviewed articles and various technical documents through an intensive literature review. These indicators were validated through online and offline expert consultation with ninety-two experts and farmers, and weights were assigned using AHP-express. The study inferred that the final scores and weightage across dimensions and the indicators did not differ significantly, implying that each dimension and indicator is important. A strong positive linear relationship between the climate smartness score and the crop yield further suggested that the wider adoption of these interventions would reduce the climate risk in agriculture for farming communities. This framework would help monitor the effectiveness of various climate-smart agriculture programmes and improve the implementation and upscaling of such programmes.

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Acknowledgements

The authors would like to acknowledge the support of the Environmental Planning & Coordination Organization (EPCO), the Government of Madhya Pradesh, and Solidaridad Network Asia Limited for financial assistance. The comments and suggestions received from the anonymous referees and different experts and participants in the international conference on ‘Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development’ hosted by the Asian Development Bank Institute, Tokyo, Japan, helped immensely in improving the manuscript.

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Singh, S.N., Bisaria, J., Sinha, B. et al. Developing a composite weighted indicator-based index for monitoring and evaluating climate-smart agriculture in India. Mitig Adapt Strateg Glob Change 29 , 12 (2024). https://doi.org/10.1007/s11027-024-10109-5

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