How can digital climate-smart
agriculture provide mitigation and
co-benefits to farmers in sub-
Saharan Africa and South Asia?

A Systematic Map

Authors 
Maaz Gardezi, Xinjing Yu, Pablo Carcamo, Sheetal Kumari, Aditi
Mukherji, Neal R. Haddaway

March 2025



2

Cite as: Gardezi, M., Yu, X., Carcamo, P., Kumari, S., Mukherji, A., & Haddaway, N, (2025). How can digital climate-
smart agriculture provide mitigation and co-benefits to farmers in sub-Saharan Africa and South Asia? A
Systematic Map. CGIAR Climate related Systematic Review Series, CGIAR, Montpellier, France. Pp: 23.

Keywords Digital agriculture, adoption, barriers and drivers, co-benefits, just transition

CGIAR is a global research partnership for a food-secure future. CGIAR science
is dedicated to transforming food, land, and water systems in a climate crisis.
Its research is carried out by 13 CGIAR Centers/Alliances in close collaboration
with hundreds of partners, including national and regional research institutes,
civil society organizations, academia, development organizations and the private
sector. We would like to thank all Funders who support this research through their
contributions to the CGIAR Trust Fund. 

How can digital climate-smart
agriculture provide mitigation and co-
benefits to farmers in sub-Saharan
Africa and South Asia?

A Systematic Map

Corresponding author: Maaz Gardezi, 560 McBryde Hall, 225 Stanger St, Blacksburg,
VA 24061, United States; email address: maaz@vt.edu 

1 Virginia Tech, 560 McBryde Hall, 225 Stanger St, Blacksburg, VA 24061, United States
2 Texas A&M University, College Station, TX 77843, United States

Maaz Gardezi   , Xinjing Yu  , Pablo Carcamo  , Sheetal Kumari  , Aditi
Mukherji   , Neal R. Haddaway

1 1 1 1

CGIAR, Kenya
Independent Researcher, Bangor, UK

3

4

3 4



3

Table 1: Bibliographic databases
used in the systematic mapping 7

TABLE OF CONTENTS

Background 4

5

Tables

Article screening and study
eligibility criteria

Data coding strategy

Data mapping method

Systematic mapping process

Conclusions
Implications for policy and
management

Implications for research

Table 2: Study inclusion criteria 8

5

8

9

9

19

20

20

20

19

17

11

9

Methods 5
Deviations from the protocol

Search for articles

5 Table 3: Types of digital agriculture
technologies 12

Descriptive information

Comparing mapping interventions
and outcomes

Inclusion, equity, and other co-
benefits of digital CSA

Limitations of the map

Table 4: Barriers to digital
agriculture technology use 14

Objective of the Review

Review Findings 9

Table 5: Drivers of digital
agriculture technology use 15

Table 6: Mitigation benefits
described in the studies 16

Declarations 21

References 22



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BACKGROUND
1

Climate-smart agriculture (CSA) is a strategic policy approach aimed at addressing the interconnected challenges
of food security and climate change by simultaneously increasing agricultural productivity, enhancing climate
resilience, and reducing greenhouse gas (GHG) emissions (FAO, 2021). As the global food system faces mounting
pressures from climate change and resulting extreme weather events, CSA has become a critical policy focus for
national governments and international organizations. Significant financial and technical investments are being
directed toward CSA initiatives, particularly in regions such as Sub-Saharan Africa and South Asia, where smallholder
farmers play a crucial role in food production and rural economies (Newell & Taylor, 2018; Taneja et al., 2014). Recent
advancements in digital technologies—such as real-time soil and weather monitoring, satellite imagery, and
precision advisory tools—have been integrated into CSA, and have started to be recognized as “digital CSA” (Prager
et al., 2022). These technologies leverage data to provide tailored agronomic and financial advice to farmers,
optimizing farm management practices such as irrigation, fertilization, pest control, and disease detection (Tsan et
al., 2019). A key challenge in implementing CSA for climate mitigation is ensuring that emission reduction efforts do
not compromise smallholder livelihoods or contribute to increased food prices for consumers. IPCC (2018) defines
co-benefits as the ‘positive effects that a policy or measure aimed at one objective might have on other objectives,
thereby increasing the total benefits for society or the environment’.  

Despite the increasing focus on digital CSA, there remains a critical gap in understanding the scope and distribution
of research on its role in climate mitigation and associated co-benefits, such as smallholder livelihoods and food
security. This study systematically maps the existing evidence, identifying key drivers, barriers, and on-farm impacts.
Rather than assessing the effectiveness of digital CSA interventions, this mapping aims to capture patterns in the
research landscape, highlighting what has been studied, where, and how. By identifying gaps and clusters in the
literature, this study provides a foundation for future systematic reviews and targeted research. The study is
grounded in a theory of change that assumes digital CSA can enhance climate mitigation outcomes by providing
smallholder farmers with better access to data-driven insights, leading to improved farm-level decision-making,
productivity gains, and emissions reductions (see Figure 1). However, the success of these technologies can be
mediated by several factors, such as accessibility, affordability, and their contextual relevance for smallholder
communities.  

Figure 1: Conceptual model

By systematically mapping the evidence on digital CSA’s role in climate mitigation and co-benefits, this study serves
as a first step in understanding the research landscape. This map identifies patterns, gaps, and clusters in existing
studies to inform future research and systematic reviews. To ensure the relevance and rigor of this research,
stakeholder engagement has been a key component of the study design. Following best practices for research in
evidence syntheses (Haddaway & Crowe, 2018a), we collaborated with the Consultative Group on International
Agricultural Research (CGIAR) and experts specializing in digital agriculture, climate change mitigation, and food
security.  However, this process primarily involved researchers and policymakers rather than direct engagement with
farmers or farmer representative organizations. While this approach provides valuable insights into policy and
research priorities, it does not fully align with participatory research best practices. This limitation should be
considered when interpreting the findings, and future research should incorporate broader stakeholder
engagement, including farmer perspectives. Through online consultations, we refined our research questions, search
strategy, and data extraction criteria. Feedback from stakeholders was incorporated into the research framework
through an iterative process before finalizing the systematic map protocol, which has been published in CGSpace,
CGIAR’s research repository (see Gardezi et al. 2024).
 



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One change was made to the methods described in the initial protocol. The initial protocol included CG Space
database for retrieving grey literature on digital CSA for mitigation. However, it was later decided to focus on peer-
reviewed literature and avoid grey literature because of time and resource constraints. To maintain a manageable
and high-quality dataset, the study prioritized peer-reviewed literature covered by Scopus, Web of Science Core
Collection.

OBJECTIVE OF THE REVIEW
The objective of this systematic map is to assess and map the current state of knowledge on CSA practices that are
enabled by digital agriculture technologies. Specifically, this review focuses on digital CSA technologies that are
tested or adopted at the farm or field-level, and have climate change mitigation and co-benefits as a stated goal.
 
The primary research question guiding this systematic map is: What evidence exists on the implementation of
digital CSA to support on-farm climate change mitigation outcomes in South Asia and Sub-Saharan Africa?

 To explore this question further, we ask the following secondary research questions:
1.How has the adoption of digital agriculture technologies for CSA (with a mitigation focus) changed from 2010 to

2024?
2.What are the key drivers and barriers influencing the adoption of digital CSA technologies for climate change

mitigation and co-benefits?
3.What mitigation benefits and co-benefits does digital CSA offer at the farm level?
4.What practices and policies are identified in existing research as contributing to a just transition in food systems

through digital CSA?

We selected 2010 as the starting point for this review because it marks the formal introduction of CSA in the global
policy discourse. The World Bank and the UN FAO highlighted CSA in the 2010 World Development Report:
Development and Climate Change (World Bank, 2009), and later that year, FAO formally launched the concept at a
global conference in The Hague, Netherlands.

This systematic map follows a Population-Intervention-Outcome (PIO) framework:
Population: The systematic map focuses on countries in South Asia (Afghanistan, Bangladesh, Bhutan, India,
Maldives, Nepal, Pakistan, Sri Lanka) and Sub-Saharan Africa (48 countries across Eastern, Southern, Central,
and Western Africa). These regions are prioritized due to the significant role of agriculture in their economies,
their vulnerability to climate change, and the importance of smallholder farming for food security and
livelihoods. Additionally, East and Southern Africa contribute the highest share of agricultural emissions on the
continent (34% and 27%, respectively; Tongwane & Moeletsi, 2018), while some West and Central African nations
(e.g., Ghana, Nigeria, Côte d’Ivoire, Rwanda) are emerging leaders in digital agriculture (Mabaya & Porciello,
2022).
Intervention: The systematic map examines the drivers and barriers influencing the implementation of digital
CSA technologies at the farm level.
Outcome: The primary outcomes of interest include:
1. Mitigation outcomes - both positive and negative contributions of digital CSA to reducing or exacerbating
agricultural greenhouse gas emissions.
2. Co-benefits and trade-offs-additional advantages, such as increased productivity, resilience, and resource
efficiency, alongside potential drawbacks or unintended consequences. 
3. Pathways toward just transition-incremental and transformative shifts in agri-food systems that promote or
hinder equity, inclusivity, and sustainability considering both enabling factors and barriers.

METHODS
Deviations from the protocol: 
The initial systematic mapping protocol guiding this systematic map was published in CG Space (Gardezi et al.
2024). The protocol outlined the use of CG Space as bibliographic databases for retrieving relevant literature on
digital CSA for mitigation and co-benefits. However, during the search process, these databases were excluded due
to the authors’ decision to remove grey literature search from the mapping. By prioritizing peer-reviewed literature
from WoS Core Collection, Scopus, and CAB Abstracts, and high-relevance Masters’ theses and PhD dissertations
from ProQuest Dissertations and Theses, the study aimed to maintain a manageable and high-quality dataset
while ensuring comprehensive coverage of literature. To reduce the risk of publication bias—which often favors
statistically significant, English-language, and Global North or Minority World-based research—we incorporated
theses and dissertations from ProQuest, capturing diverse perspectives that might otherwise be underrepresented
in traditional academic publishing.

Search for articles:
Our search strategy is composed of bibliographic search and a citation chasing strategy.

1



6

Search terms and strings: Following the methodology outlined in previous evidence mapping protocols on CSA
(Rosenstock et al., 2016), we defined our search strings using three key components: (i) a ‘practice’ or intervention string,
(ii) an ‘outcome’ string, and (iii) a population or ‘location’ string. The practice string was further divided into two
categories: ‘climate-smart agriculture’ and ‘digital agriculture technologies.’ The outcome string was structured to
capture two main aspects—one related to climate change mitigation and another concerning co-benefits, which
included just transition, labor and work, and matters of sustainability and social justice. The location string included both
specific countries (e.g., Pakistan, Kenya) and broader regional classifications (e.g., Sub-Saharan Africa, South Asia).

To maximize inclusivity, we combined individual search terms within each component using the Boolean ‘OR’ operator,
while the practice, outcome, and location strings were integrated using the ‘AND’ operator for searches in WoS Core
Collection, Scopus, CAB Abstracts, and ProQuest Dissertations and Theses. The search terms were derived from key
articles addressing climate-smart agriculture practices and mitigation outcomes in Sub-Saharan Africa (e.g., Anuga et
al., 2020) and South Asia (e.g., Jat et al., 2024). Additional terms and synonyms were incorporated from studies on digital
agriculture (e.g., Klerkx et al., 2019) and just transition (e.g., Tribaldos & Kortetmäki, 2022), including references to specific
mitigation strategies (e.g., livestock enteric fermentation, methane emissions) and social outcomes (e.g., power
relations, labor, gender relations). After an initial screening, relevant articles were merged with those in the benchmark
list, forming an extended dataset for citation chasing. We employed a citation chasing strategy on included studies only,
so after full text screen, incorporating both backward citation chasing (retrieving references cited by the selected
articles) and forward citation chasing (identifying articles that cite the selected studies). The process was facilitated
using “citationchaser,” (Haddaway et al. 2022) an R package and Shiny app, which has been found to outperform
manual searching in Web of Science and Scopus in previous systematic mapping efforts (Llopis et al., 2023). 

The search string included the following key terms and Boolean operators:

(agri* OR agro* OR farm* OR crop* OR arable OR cultivat* OR horticultur* OR "food produc*" OR cultivat* OR “yield
produc*” OR livestock OR "live stock" OR pastur* OR pastoral*) 
 
AND ( smart-farm* OR "smart farm*" OR smart-agri* OR "smart agri*" OR digital OR (4.0 W/3 agri*) OR drone* OR "internet
of things" OR sensor* OR "big data" OR "artificial intelligence" OR (machine W/6 learning) OR computer* OR electronic* OR
"decision support" OR decision-support* OR "climate information*" OR (precision W/6 farm*) OR (precision W/6
agricultur*) OR ("mobile phone*" W/6 agricultur*) OR ("mobile phone*" W/6 farm*) OR ("cell phone" W/6 agricultur*) OR
("cell phone" W/6 farm*) OR OR (market W/6 information) OR (climate W/6 advice) OR (climate W/6 advis*) OR (weather
W/5 advisor*) OR "predictive analytics" OR (index-based W/6 insurance) OR ("index based" W/6 insurance) OR (software
W/6 management) OR "remote sensing" OR "remote-sensing" OR "RFID tags" OR "automated feeding" OR (cyber W/6
agricultur*) OR (cyber W/6 farm*) OR (traceability W/6 agricultur*) OR (traceability W/6 farm*) 
  
AND ("South Asia*" OR Afghan* OR Bangladesh* OR Bhutan* OR India* OR Maldiv* OR Nepal* OR Pakistan* OR SSA OR
"Sub-Saharan Africa*" OR "Subsaharan Africa*" OR "Africa, Sub-Saharan" OR Subsaharan OR Sub-Saharan OR 

"Central Africa*" OR Cameroon* OR "Central Africa*" OR Chad* OR Congo* OR "Democratic Republic of the Congo" OR
"Republic of Equatorial Guinea" OR "Equatorial Guinea" OR "Equatoguinean" OR Gabon* OR "Sao Tome and Principe" OR
Santomean OR "East Africa*" OR "Eastern Africa*" OR "Republic of Burundi" OR Burundi* OR Djibouti* OR "Republic of
Djibouti" OR Eritrea* OR "Federal Democratic Republic of Ethiopia" OR Ethiopia* OR Kenya* OR "Republic of Kenya" OR
"Republic of Rwanda" OR Rwanda* OR Somali* OR "South Sudan" OR "South Sudanese" OR "Republic of the Sudan" OR
Sudan* OR Tanzania* OR Zanzibar* OR "Republic of Uganda" OR Uganda* OR 

"Southern Africa*" OR Angola* OR Botswana* OR Motswana* OR Swazi* OR "Kingdom of Lesotho" OR Lesotho OR Basotho
OR "Republic of Malawi" OR Malawi* OR "Republic of Mozambique" OR Mozambi* OR "Southwest Africa" OR "Republic of
Namibia" OR "South West Africa" OR Namibia* OR "Union of South Africa" OR "Republic of South Africa" OR "South Africa*"
OR "Republic of Zambia" OR Zambia* OR "Republic of Zimbabwe" OR Zimbabwe* OR

 "Africa, West" OR "West Africa*" OR "Western Africa*" OR "Republic of Benin" OR Benin* OR "Burkina Fas*" OR Burkinabe OR
"Republic of Cape Verde" OR "Cape Verde" OR "Cabo Verde*" OR "Ivory Coast" OR "Cote d'Ivoire" OR Ivorian OR "Republic of
the Gambia" OR "Gambia*" OR "Republic of Ghana" OR Ghana* OR "Republic of Guinea" OR Guinea* OR "Republic of
Guinea-Bissau" OR "Guinea-Bissau" OR "Bissau-Guinean" OR "Republic of Liberia" OR Liberia* OR "Republic of Mali" OR Mali*
OR Mauritania* OR "Republic of Niger" OR Niger* OR Nigerien OR "Federal Republic of Nigeria" OR "Republic of Senegal" OR
Senegal* OR "Republic of Sierra Leone" OR "Sierra Leone*" OR "Togolese Republic" OR Togo*)

AND ((mitigation OR (carbon W/6 sequest*) OR "livestock enteric fermentation" OR (emission W/3 methane) OR "carbon
stock change*" OR (reduction W/6 emission) OR "low emission strateg*" OR "low carbon develop*" OR "livestock enteric
emission*" OR (emission W/3 greenhouse) OR "nitrous oxide" OR "nitrous-oxide" OR "methane" OR (potential W/3 global
warming) OR (accumulat* W/3 carbon) OR "biomass carbon*" OR (global warming W/3 intens*) OR (carbon W/3 intens*)
OR (emission W/3 intens*) OR (carbon W/3 footprint) OR (carbon W/3 efficien*)) 



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OR (co-benefit* OR "local knowledge" OR "traditional knowledge" OR "indigenous knowledge" OR "ecological knowledge"
OR labour* OR labor* OR (living W/6 income*) OR inequalit* OR (gender W/6 equal*) OR revenue OR livelihood* OR
"income* diversi*" OR "domestic labo*" OR "seasonal labo*" OR "women group*" OR (women W/6 livelihood*) OR
cooperative* OR "employ* opportunit*" OR entitlement* OR (gender W/6 inequalit*) OR (gender W/6 equalit*) OR (gender
W/6 relation*) OR (female W/6 livelihood*) OR "female entrepreneur*" OR female-headed OR "female headed" OR
(female W/6 participat*) OR "power relation*" OR power-relation* OR (gender W/6 vulnerabilit*) OR (gender W/6 role*)
OR (gender W/6 knowledge) OR (gender W/6 adapt*) OR "gender asset*" OR "female asset*" OR "female propert*" OR
matriarchy* OR patriarchy* OR empowerment* OR "cost revenue*" OR membership* OR "farmer association*" OR
"peasant association*" OR "farmer union*" OR "farmer group*" OR (gender W/6 analysis*) OR income* OR women* OR
association* OR justice* OR diversit* OR participat* OR inclusion* OR equality* OR fair* OR equit* OR transition* OR
transformation* OR responsibilit* OR adopt*))

Search Limitations: Despite the systematic approach used in this study, several limitations should be acknowledged.
First, restricting the review to studies published in English may introduce language bias and limit the inclusion of diverse
perspectives on digital CSA. Furthermore, reliance on published literature means that valuable insights from
unpublished reports, policy documents, and stakeholder experiences have been excluded. Despite these limitations,
this systematic mapping provides a comprehensive synthesis of the challenges and opportunities associated with
digital CSA adoption for climate change mitigation in smallholder farming systems. It serves as a critical foundation for
future research, highlighting knowledge gaps and pathways for fostering inclusive and effective digital interventions in
agriculture.

Search sources: Table 1 shows the bibliographic databases used for creating the systematic map.

Type Platforms*

Bibliographic
database

Scopus

URL

Web of Science
Core Collection

SCI-EXPANDED; SSCI;
AHCI; CPCI-S; CPCI-
SSH; ESCI

Table 1: Bibliographic databases used in the systematic mapping

Database

Scopus https://www.scopus.com

https://www.webofscience.co
m/wos/woscc/advanced-
search

CABI Digital Library CAB Abstracts https://www.cabidigitallibrary.
org/

Dissertation
database

ProQuest ProQuest
Dissertations and
Theses

https://www.proquest.com 

*Searches in Scopus, Web of Science Core Collection, and ProQuest were conducted using subscriptions of Virginia
Tech, USA. Searches in CAB Abstracts was conducted using CABI subscription.

Estimating the comprehensiveness of the search: The comprehensiveness of the search string was assessed by
benchmarking it against a curated list of publications on digital CSA practices for climate change mitigation and co-
benefits. This benchmark list was compiled based on the expertise of the review team and insights from previous non-
systematic reviews (see attachment: Initial Article Benchmark). It served as a reference point to evaluate the
effectiveness of the search strategy. Scoping searches were conducted using Web of Science Core Collection and
Scopus and the proportion of benchmark articles captured by these searches was measured. The results showed that
all of the benchmark articles were retrieved across the two databases, indicating no gaps in the initial search strategy.

Managing search results: To manage the search results efficiently, we used Covidence to combine and deduplicate
records retrieved from Scopus, Web of Science, and ProQuest. This software allowed us to systematically remove
duplicate entries and organize the library of search results for screening and review.

https://www.proquest.com/


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Article screening and study eligibility criteria
Screening process: To answer the study’s research questions, we reported systematic mapping of literature following
the reporting standards for systematic evidence synthesis (ROSES) framework (Haddaway et al., 2018b). ROSES
provides a meticulous, transparent, and standard tool for the reporting of systematic evidence products. A detailed
flowchart was created to track the number of articles retrieved through different search strategies, those excluded and
included at each screening stage, and the final number of studies assessed as relevant after full-text review (Figure 1).
Additionally, a comprehensive list of excluded articles, along with reasons for exclusion, is shared in supplementary
documents (See spreadsheet titled “Excluded Articles”. Table 1 shows the inclusion criteria used in the study.     

The PIO model has formulated these criteria, as described earlier in the text. This includes:

Eligible Population: The review includes studies focusing on agricultural systems in South Asia (Afghanistan,
Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka) and Sub-Saharan Africa (48 countries across Eastern,
Southern, Central, and Western Africa). These regions are prioritized due to their dependence on agriculture, high
vulnerability to climate change, and the predominance of smallholder farming. East and Southern Africa, which
contribute the highest agricultural emissions on the continent, and West and Central African nations, emerging as
leaders in digital agriculture, are of particular interest.

Eligible Intervention: Studies must examine digital agriculture technologies, strategies, or practices aimed at climate
change mitigation and associated co-benefits in farming systems. This includes tools designed to reduce greenhouse
gas emissions, enhance resource efficiency, and improve farm management through data-driven insights.

Eligible Outcomes: The primary outcome of interest is the impact of digital CSA on climate change mitigation,
including both positive and negative contributions to reducing or exacerbating agricultural greenhouse gas emissions.
Secondary outcomes include: (a) Co-benefits and trade-offs, such as improvements in productivity, resilience, and
resource efficiency, alongside potential drawbacks, and (b) Pathways toward just transition, assessing whether digital
CSA fosters or hinders equity, inclusivity, and sustainability by identifying enabling factors and barriers.

The systematic map includes studies employing observational or experimental research designs, such as surveys and
field trials conducted on smallholder farms or in research stations. Both quantitative studies—measuring outcomes like
emissions reduction and nutrient efficiency—and qualitative research—examining farmer perceptions of digital
agriculture’s role in climate mitigation—were considered. To ensure a rigorous and reliable evidence base, only studies
published in peer-reviewed journal articles, conference proceedings, dissertations, or theses were included, while book
chapters, technical reports, and review papers were excluded. Due to language constraints, only studies published in
English were reviewed. The scope of the review was further limited to studies published between 2010 and 2025. Studies
were excluded if they lacked a clear methodological description, did not directly examine digital CSA applications for
climate mitigation, or merely mentioned mitigation without substantive discussion. Additionally, studies were excluded
if they were not conducted at the farm or field level, ensuring relevance to on-the-ground agricultural practices. 

To ensure consistency throughout the screening process, checks were conducted at the beginning of each stage,
including title and abstract screening as well as full-text screening. Initially, 10% of the articles were randomly selected
and screened at the title and abstract level by all authors simultaneously. Cohen’s Kappa score (Cohen, 1960) was
used to assess inter-reviewer agreement. Any discrepancies were reviewed on a case-by-case basis to refine and
clarify the inclusion criteria. If the initial Kappa score was below 0.6, indicating less than moderate agreement among
reviewers, an additional 10% of articles were screened collectively, followed by another Kappa score assessment. This
process was repeated until a Kappa score of at least 0.6 was reached. Once consistency was established at the title
and abstract screening stage, the same procedure was applied to the full-text screening phase.

Inclusion criteria

1 Studies published between 2010 and 2025.

2

3

4

Studies that examine the role of digital agriculture technologies in facilitating climate-smart
agriculture (CSA) practices and their impact on climate change mitigation. 

Studies that focus on farm- or field-level analysis. 

Studies that assess the on-farm adoption and implementation of digital CSA technologies. 

Table 2: Study inclusion criteria



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Inclusion criteria

5 Studies that employ observational or experimental research designs, including surveys and
field trials conducted on smallholder farms or in research stations. 

6

7

8

Studies conducted in the eight countries of South Asia and all 48 countries of Sub-Saharan
Africa. 

Studies published in English due to language constraints of the review team.

Studies published in peer-reviewed journal articles, conference proceedings, dissertations, or
theses. Book chapters, technical reports, and review papers will be excluded.

Data coding strategy: 
To systematically analyze the selected studies, we applied an interpretive approach. This approach enabled key
themes to emerge from the data, guided by frameworks related to technology adoption, agricultural transformation,
and institutional barriers in digital climate-smart agriculture (CSA). Using a combination of inductive and deductive
coding, we identified meaningful excerpts and themes within the studies reviewed (Braun & Clarke, 2006). A structured
data extraction process was followed using a pre-defined codebook aligned with the study’s research questions.
Metadata recorded included bibliographic details such as title, authors, journal, year, and DOI, as well as study location,
specifying country and regional relevance. The coding process was conducted in four steps: (1) systematically
reviewing and extracting relevant excerpts from studies; (2) applying initial codes to categorize references to drivers
and barriers of digital CSA adoption; (3) refining codes and identifying emerging themes; and (4) grouping and
organizing themes to highlight broader patterns in adoption dynamics. 

For example, codes for “barriers to adoption” that emerged from the studies include cost constraints, technical
complexity, farmer training, access to infrastructure, data governance, and institutional support. These were further
categorized into overarching themes such as financial and infrastructural barriers, capacity-building needs, regulatory
challenges, and enabling conditions for scaling digital CSA. This structured approach ensured consistency and
facilitated a systematic comparison of findings across studies. An example of how relevant studies were coded for their
drivers and barriers to digital CSA adoption is provided in the additional attachment, titled “Data Coding Process.”

To maintain consistency and reliability, three team members independently coded five sample of articles, beginning
with the most recent publications. After coding, the team reviewed discrepancies and resolved uncertainties through
discussion. In cases where disagreements persisted, the first author mediated to ensure consensus. A double-
screening process was applied to all eligible articles to uphold reliability, and weekly meetings were conducted to
address challenges, synchronize efforts, and refine coding practices. These measures ensured a rigorous and
systematic approach to data extraction, supporting transparency and replicability in the study’s findings.

Data mapping method: 
Our data mapping strategy was inspired by Troncarelli & Morsello (2022) and aimed to address research questions
related to the adoption of digital agriculture technologies for CSA. The full mapping database, including metadata and
a catalog of key findings, was compiled and included in the supplementary files (see spreadsheet titled “Extracted
Articles Coded”). To synthesize and present findings effectively, the research team employed narrative summaries,
descriptive statistics, and visualization tools. The results captured trends, drivers, barriers, benefits, and equity-related
insights. A range of visual outputs was used to present the findings, including: (a) Tables summarizing key drivers,
barriers, mitigation benefits, and equity considerations by farm characteristics identified in the literature, along with
references to relevant studies, (b) Temporal trend graphs illustrating the evolution of digital agriculture adoption for
CSA and mitigation from 2010 to 2024, (c) An Evidence Atlas mapping the geographical distribution of studies to
highlight regional adoption patterns of digital agriculture technologies, and (d) Heat maps displaying the frequency of
adoption drivers by digital agriculture technology type and frequency of adoption barriers by technology type.

REVIEW FINDINGS
Systematic mapping process



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The number articles returned during each stage in the systematic map process is reported in the ROSES flowchart
(Figure 2). The bibliographic search was conducted across Web of Science Core Collection, Scopus, CAB Abstracts, and
ProQuest Dissertations and Theses, with a total of 9864 articles retrieved from Scopus, 5658 from Web of Science, 4847
from CAB Abstracts, and 977 from ProQuest Dissertations and Theses. After duplicate removal, the initial dataset
included 13,437 articles that were included for further screening.

Next, we screened 13,437 articles based on their titles, abstracts, and keywords to determine relevance to digital CSA for
climate change mitigation and co-benefits at the farm-level in South Asia and Sub-Saharan Africa. This process
resulted in a refined set of 829 articles for full-text review. 52 articles met our inclusion criteria. Additionally, we applied
citation chasing to the articles deemed eligible for extraction, incorporating 5 additional articles that aligned with our
study scope. The final database consisted of 57 articles.

Figure 2: ROSES Flow Diagram for Systematic Maps (Source: Haddaway et al. 2017)



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Descriptive information
Publication geography: The geographical distribution of the 57 studies included in the systematic review reveals a
concentration of research on digital CSA in South Asia (31 papers), East Africa (11 papers), and West Africa (11 papers),
while Southern Africa (6 papers) and Central Africa (1 paper) are comparatively underrepresented. In South Asia, the
majority of studies (22 out of 31) were conducted in India, followed by Pakistan with four studies and Nepal with three.
Bangladesh and Afghanistan were each represented by a single study. The EviAtlas R Shiny application was used to
map the geographic locations of studies, with the resulting visualization included in Figure 3 below, and made available
online here: https://tinyurl.com/digitalCSAMitigation

The uneven distribution of studies suggests a potential regional research bias. Research on digital CSA for climate
change mitigation is more prevalent in South Asia, East Africa, and West Africa. The limited representation of Southern
Africa and Central Africa in the literature also indicate a possible gap in empirical evidence. The imbalance in research
distribution in regions suggests that barriers and drivers might be different across regions, which should encourage
more regionally inclusive research to tailor digital CSA solutions to local contexts in the long run.

Figure 3: Mapping the geographic location of studies

Source: https://tinyurl.com/digitalCSAMitigation 
Characteristics of digital agriculture technologies used for implementing CSA: This systematic map focusing on
digital technology, CSA, mitigation, and mitigation co-benefits identified 57 studies that detail the implementation of
digital CSA to support on-farm climate change mitigation outcomes in South Asia and Sub-Saharan Africa. The key
technologies found in the systematic map include mobile phones and apps, decision support tools, sensor-based
technologies, precision agriculture, and CSA practices. Table 3 provides a description of each of these technologies
along with the associated papers.

https://tinyurl.com/digitalCSAMitigation
https://tinyurl.com/digitalCSAMitigation


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Technologies Description

Mobile phones
and Apps

Digital agriculture include tools like
mobile phones and apps, which allow
farmers to access critical data for
information. Climate-smart agriculture
(CSA) uses platforms for weather and
climate information to support resilience
agricultural practices.

Mthethwa et al., 2022; Jones et al., 2023;
Ouedraogo et al., 2021; Djodi et al., 2021; Cox
& Sseguya, 2015; Muriithi et al., 2021; Khan et
al., 2021; Mwikamba et al., 2024; Haswell &
Khataza, 2024; Khan et al., 2021; McKune et
al., 2018; Kulat et al., 2022; Das et al., 2023;
Maertens et al., 2023; Damba et al., 2024;
Aoki et al., 2024; Dhanya et.al., 2022; Sutanto
et.al, 2022; Mwalupaso et al., 2020; Kaur et
al., 2024; Okumu et al., 2025; Nepal et al.,
2024; Mittal et al., 2018; Asante et al., 2024;
Omotoso et al.,2024; Nchanji et al., 2022;
VanCampenhout et al., 2020

Decision
Support
Tools

Decision Support tools, such as Rice
Advice and Nutrient Expert, provide more
tailored recommendations to optimize
agricultural management. These tools
assist farmers make informed decisions
based on local data and practices.

Anuga et.al., 2022; Cotter et.al, 2020; Yadav
et.al., 2024; Arouna et.al., 2020; Oyinbo et.al.,
2022; Maertens et.al., 2023; Upadhyay et al.,
2025; Varinderpal-Singh et al., 2017;
Varinderpal-Singh et al., 2020; Bhattarai et
al., 2024; Timsina et al., 2021; Rahman et al.,
2024; MacCarthy et al., 2022; Pooniya et al.,
2015; 

Sensor-Based
Technologies

Sensor-Based Technologies include
remote sensors and in-situ sensors.
Remote sensors, like drones and
satellites, collect data on large-scale
agricultural landscapes. While in-situ
sensors like Greenseeker Optical Sensor
measure field-level conditions.
Together, sensor-based technologies
provide farmers precise monitoring of
crop health, soil conditions, and
resource use.

Aoki et.al., 2024; Oyeogbe & Das, 2015;
Bijarniya et al., 2020; Schekhawa et al., 2021;
Sutanto et al., 2022; Singh et al., 2020; Abera
et al., 2023; McCarthy et al., 2023; Rakholia
et al., 2024; Upadhyay et al., 2025;
Varinderpal-Singh et al., 2017; Sadhukhan et
al., 2024;

Included studies reporting this technology

Precision
Agriculture

Precision agriculture integrates
advanced technologies to manage
agricultural activities and enhance
productivity. Techniques such as
precision nutrient management
reduce environmental impacts and
help farmers to reach their
sustainable goals.

Maertens et.al., 2023; Damba et.al., 2024;
Aoki et.al., 2024; Rakholia et.al., 2024; Singh
et.al., 2020; Schekhawa et.al., 2021; Bijarniya
et.al., 2020; Jat et al., 2018; Warpe et al.,
2023; Liben et al., 2024; Krishna et al., 2024;
Seki et al., 2022; Varinderpal-Singh et al.,
2020; Sadhukhan et al., 2024; Varinderpal-
Singh et al., 2017; Upadhyay et al., 2025;
Haldar et al., 2022; Mbile et al., 2015; Tsolakis
et al., 2023; Okoli et al., 2024; Raza et al.,
2022; Sadhukhan et al., 2024

Table 3: Types of digital agriculture technologies



13

Evolution and trends in digital CSA on-farm use: We specifically examined the growth of relevant literature between
2010 and 2024 to show the evolution and trends of digital agriculture technologies for CSA in sub-Saharan Africa and
South Asia. Discussions in the mapped literature range from mobile phones and related apps to linking with
technologies that enable site-specific nutrient management and decision support tools. The first three years of
publication (2015 – 2018) mostly discussed technologies related to mobile phones and applications. Between 2019 and
2022, sensor-based technologies and decision support systems started to become more prevalent in the literature.
Since 2023, the reviewed literature has expanded to include other technologies, such as precision nutrient
management, mobile-enabled remote irrigation technologies, and technologies that are driven by artificial
intelligence (AI) and the Internet of things (IoT).

Key barriers and drivers of digital CSA adoption: The adoption of digital agriculture technologies for climate-smart
agriculture (CSA) is influenced by various reported barriers spanning economic, infrastructural, educational, and
technological challenges (Table 4). Cost and economic concerns appear frequently, noted in 18 studies (Damba et al.,
2024; Aoki et al., 2024; Bijarniya et al., 2020; Schekhawa et al., 2021; Singh et al., 2020; McCarthy et al., 2023; Rakholia et al.,
2024; Cox & Sseguya, 2015; Varinderpal-Singh et al., 2020; Warpe et al., 2023; Sadhukhan et al., 2024; VanCampenhout et
al., 2020; Tsolakis et al., 2023; Raza et al., 2022; Varinderpal-Singh et al., 2017; Jat et al., 2018; Mbile et al., 2015; Upadhyay et
al., 2025). Studies suggest that farmers are often discouraged by the high upfront costs of technology and uncertain
returns on investment.

Limited technology access and connectivity are also commonly reported challenges, highlighted in 16 studies (Damba
et al., 2024; Anuga et al., 2022; Cotter et al., 2020; Cox & Sseguya, 2015; Varinderpal-Singh et al., 2020; Okumu et al., 2025;
Sadhukhan et al., 2024; VanCampenhout et al., 2020; Krishna et al., 2024; Liben et al., 2024; Varinderpal-Singh et al., 2017;
Rahman et al., 2024; Jat et al., 2018; Timsina et al., 2021; Mwalupaso et al., 2020; Upadhyay et al., 2025), with infrastructure
deficiencies such as poor internet connectivity and restricted access to digital tools in rural areas are frequently noted.

Awareness and trust barriers are cited in 10 studies (Bijarniya et al., 2020; Rakholia et al., 2024; Ouedraogo et al., 2021;
Varinderpal-Singh et al., 2020; Sadhukhan et al., 2024; VanCampenhout et al., 2020; Krishna et al., 2024; Varinderpal-
Singh et al., 2017; Mittal et al., 2018; Upadhyay et al., 2025). These studies suggest that limited awareness of digital
agriculture solutions and concerns about the reliability of digital advisory services may hinder adoption.

Infrastructure and advisory limitations are mentioned in 18 studies (Damba et al., 2024; Aoki et al., 2024; Bijarniya et al.,
2020; Rakholia et al., 2024; Cotter et al., 2020; Arouna et al., 2020; Varinderpal-Singh et al., 2020; Sadhukhan et al., 2024;
VanCampenhout et al., 2020; Krishna et al., 2024; Liben et al., 2024; Pooniya et al., 2015; Varinderpal-Singh et al., 2017;
Timsina et al., 2021; Mittal et al., 2018; MacCarthy et al., 2022; Mwalupaso et al., 2020; Upadhyay et al., 2025), indicating
that a lack of extension services and institutional support can restrict farmers' ability to effectively integrate digital CSA
practices.

Farmer education and technical capacity are reported barriers in 22 studies (Damba et al., 2024; Schekhawa et al.,
2021; Anuga et al., 2022; Arouna et al., 2020; Cox & Sseguya, 2015; Okoli et al., 2024; Varinderpal-Singh et al., 2020; Warpe
et al., 2023; Okumu et al., 2025; VanCampenhout et al., 2020; Asante et al., 2024; Krishna et al., 2024; Liben et al., 2024;
Tsolakis et al., 2023; Raza et al., 2022; Pooniya et al., 2015; Varinderpal-Singh et al., 2017; Rahman et al., 2024; MacCarthy
et al., 2022; Mbile et al., 2015; Mwalupaso et al., 2020; Upadhyay et al., 2025), with several studies highlighting how a lack
of digital literacy and technical skills can prevent effective technology adoption.

Technology suitability and data gaps are noted in 10 studies (Aoki et al., 2024; Schekhawa et al., 2021; McCarthy et al.,
2023; Rakholia et al., 2024; Okoli et al., 2024; Varinderpal-Singh et al., 2020; Tsolakis et al., 2023; Timsina et al., 2021;
MacCarthy et al., 2022; Upadhyay et al., 2025). These studies report that digital tools are often tailored for specific crops
or large-scale commercial farming, making it difficult for smallholder farmers to adopt them effectively. Finally,
technological risk perception is mentioned in five studies (McCarthy et al., 2023; Rakholia et al., 2024; Okoli et al., 2024;
Timsina et al., 2021; MacCarthy et al., 2022), with concerns that risk-averse farmers may be hesitant to adopt new
technologies due to uncertainties regarding their effectiveness and return on investment. Collectively, these reported
barriers suggest the need for further research and policy interventions that enhance digital infrastructure, expand
farmer education programs, and promote the development of cost-effective, inclusive digital agriculture solutions
tailored to diverse farming contexts.



14

Barriers Included studies referencing this barrier

Cost & economic
concerns

Damba et.al., 2024; Aoki et.al., 2024; Bijarniya et.al., 2020; Schekhawa
et.al., 2021; Singh et.al., 2020; McCarthy et.al., 2023; Rakholia et.al., 2024;
Cox & Sseguya, 2015; Varinderpal-Singh et al., 2020; Warpe et al., 2023;
Sadhukhan et al., 2024; VanCampenhout et al., 2020; Tsolakis et al.,
2023; Raza et al., 2022; Varinderpal-Singh et al., 2017; Jat et al., 2018;
Mbile et al., 2015; Upadhyay et al., 2025

Limited technology
access & connectivity

Damba et.al., 2024; Anuga et.al., 2022; Cotter et.al, 2020; Cox & Sseguya,
2015; Varinderpal-Singh et al., 2020; Okumu et al., 2025; Sadhukhan et
al., 2024; VanCampenhout et al., 2020; Krishna et al., 2024; Liben et al.,
2024; Varinderpal-Singh et al., 2017; Rahman et al., 2024; Jat et al., 2018;
Timsina et al., 2021; Mwalupaso et al., 2020; Upadhyay et al., 2025

Awareness & trust
barriers

Bijarniya et.al., 2020; Rakholia et.al., 2024; Ouedraogo et.al., 2021;
Varinderpal-Singh et al., 2020; Sadhukhan et al., 2024;
VanCampenhout et al., 2020; Krishna et al., 2024; Varinderpal-Singh et
al., 2017; Mittal et al., 2018; Upadhyay et al., 2025

Infrastructure & advisory
limitations Damba et.al., 2024; Aoki et.al., 2024; Bijarniya et.al., 2020; Rakholia et.al.,

2024; Cotter et.al, 2020; Arouna et.al., 2020; Varinderpal-Singh et al.,
2020; Sadhukhan et al., 2024; VanCampenhout et al., 2020; Krishna et
al., 2024; Liben et al., 2024; Pooniya et al., 2015; Varinderpal-Singh et al.,
2017; Timsina et al., 2021; Mittal et al., 2018; MacCarthy et al., 2022;
Mwalupaso et al., 2020; Upadhyay et al., 2025

Table 4: Barriers to digital agriculture technology use

Farmer education &
technical capacity

Damba et.al., 2024; Schekhawa et.al., 2021; Anuga et.al., 2022; Arouna
et.al., 2020; Cox & Sseguya, 2015; Okoli et al., 2024; Varinderpal-Singh et
al., 2020; Warpe et al., 2023; Okumu et al., 2025; VanCampenhout et al.,
2020; Asante et al., 2024; Krishna et al., 2024; Liben et al., 2024; Tsolakis
et al., 2023; Raza et al., 2022; Pooniya et al., 2015; Varinderpal-Singh et
al., 2017; Rahman et al., 2024; MacCarthy et al., 2022; Mbile et al., 2015;
Mwalupaso et al., 2020; Upadhyay et al., 2025

Technology suitability
and data gaps

Aoki et.al., 2024; Schekhawa et.al., 2021; McCarthy et.al., 2023; Rakholia
et.al., 2024; Okoli et al., 2024; Varinderpal-Singh et al., 2020; Tsolakis et
al., 2023; Timsina et al., 2021; MacCarthy et al., 2022; Upadhyay et al.,
2025

McCarthy et.al., 2023; Rakholia et.al., 2024; Okoli et al., 2024; Timsina et
al., 2021; MacCarthy et al., 2022 

Technological risk
perception

The analysis of drivers for digital agriculture adoption highlights six key themes, with varying levels of emphasis across
the literature (Table 5). The most frequently cited driver is resource efficiency & precision farm management,
referenced in 29 studies (Aoki et al., 2024; Bijarniya et al., 2020; Schekhawa et al., 2021; Singh et al., 2020; Rakholia et al.,
2024; Anuga et al., 2022; Cotter et al., 2020; Arouna et al., 2020; Oyinbo et al., 2022; Okoli et al., 2024; Varinderpal-Singh et
al., 2020; Warpe et al., 2023; Sadhukhan et al., 2024; VanCampenhout et al., 2020; Asante et al., 2024; Krishna et al., 2024;
Liben et al., 2024; Tsolakis et al., 2023; Raza et al., 2022; Pooniya et al., 2015; Varinderpal-Singh et al., 2017; Rahman et al.,
2024; Jat et al., 2018; Timsina et al., 2021; Mittal et al., 2018; MacCarthy et al., 2022; Mbile et al., 2015; Mwalupaso et al., 2020;
Upadhyay et al., 2025). This driver underscores the role of digital tools in enhancing nutrient use efficiency, optimizing
resource allocation, monitoring crops, and enabling real-time decision-making on farms.



15

Market & information access is the second most frequently mentioned driver, appearing in 13 studies (Damba et al.,
2024; Aoki et al., 2024; Rakholia et al., 2024; Arouna et al., 2020; Okoli et al., 2024; Warpe et al., 2023; Okumu et al., 2025;
VanCampenhout et al., 2020; Krishna et al., 2024; Mittal et al., 2018; MacCarthy et al., 2022; Mbile et al., 2015; Mwalupaso
et al., 2020). This theme highlights the importance of digital agriculture in providing farmers with market linkages,
access to climate and agronomic data, and support for farm decision-making.

CSA integration is another key driver, cited in 12 studies (Haswell & Khataza, 2024; Das et al., 2023; Cotter et al., 2020;
Yadav et al., 2024; Varinderpal-Singh et al., 2020; Sadhukhan et al., 2024; Pooniya et al., 2015; Varinderpal-Singh et al.,
2017; Rahman et al., 2024; Timsina et al., 2021; MacCarthy et al., 2022; Upadhyay et al., 2025). This category reflects how
digital agriculture aligns with climate-smart agricultural (CSA) goals by leveraging mobile networks and integrating
social, ecological, and economic sustainability objectives.

Farmer support & organizational networks is mentioned in eight studies (McCarthy et al., 2023; Ouedraogo et al., 2021;
Okumu et al., 2025; Sadhukhan et al., 2024; VanCampenhout et al., 2020; Krishna et al., 2024; Pooniya et al., 2015; Mbile et
al., 2015), emphasizing the role of extension services and farmer organizations in facilitating technology adoption.
Government & institutional trust is cited in seven studies (Mwikamba et al., 2024; Cox & Sseguya, 2015; Okoli et al., 2024;
Okumu et al., 2025; Krishna et al., 2024; Raza et al., 2022; Mwalupaso et al., 2020). This suggests that trust in state-led
programs and digital advisory services plays a role in technology adoption, although it is discussed less frequently
than resource efficiency and market access.

Lastly, farmer characteristics & education is noted in seven studies (Ouedraogo et al., 2021; Varinderpal-Singh et al.,
2020; Sadhukhan et al., 2024; VanCampenhout et al., 2020; Asante et al., 2024; Pooniya et al., 2015; Varinderpal-Singh et
al., 2017; Upadhyay et al., 2025), indicating that individual traits such as age and education levels may influence digital
agriculture uptake, though this is less commonly examined in the literature. Overall, the prominence of resource
efficiency is noticeable because of higher number of collated results in the studies, while institutional trust remain
relevant but is discussed less frequently as enablers of adoption.

Drivers Included studies referencing this barrier

Resource efficiency &
precision farm
management

Aoki et.al., 2024; Bijarniya et.al., 2020; Schekhawa et.al., 2021; Singh et.al.,
2020; Rakholia et.al., 2024; Anuga et.al., 2022; Cotter et.al, 2020; Arouna
et.al., 2020; Oyinbo et.al., 2022; Okoli et al., 2024; Varinderpal-Singh et al.,
2020; Warpe et al., 2023; Sadhukhan et al., 2024; VanCampenhout et al.,
2020; Asante et al., 2024; Krishna et al., 2024; Liben et al., 2024; Tsolakis
et al., 2023; Raza et al., 2022; Pooniya et al., 2015; Varinderpal-Singh et
al., 2017; Rahman et al., 2024; Jat et al., 2018; Timsina et al., 2021; Mittal et
al., 2018; MacCarthy et al., 2022; Mbile et al., 2015; Mwalupaso et al., 2020;
Upadhyay et al., 2025 

Farmer support &
organizational networks

McCarthy et.al., 2023; Ouedraogo et.al., 2021; Okumu et al., 2025;
Sadhukhan et al., 2024; VanCampenhout et al., 2020; Krishna et al.,
2024; Pooniya et al., 2015; Mbile et al., 2015

Table 5: Drivers of digital agriculture technology use

Farmer characteristics &
education

Ouedraogo et.al., 2021; Varinderpal-Singh et al., 2020; Sadhukhan et al.,
2024; VanCampenhout et al., 2020; Asante et al., 2024; Pooniya et al.,
2015; Varinderpal-Singh et al., 2017; Upadhyay et al., 2025

Market & information
access

Damba et.al., 2024; Aoki et.al., 2024; Rakholia et.al., 2024; Arouna et.al.,
2020; Okoli et al., 2024; Warpe et al., 2023; Okumu et al., 2025;
VanCampenhout et al., 2020; Krishna et al., 2024; Mittal et al., 2018;
MacCarthy et al., 2022; Mbile et al., 2015; Mwalupaso et al., 2020



16

Drivers Included studies referencing this barrier

Government &
Institutional Trust

Mwikamba et.al., 2024; Cox & Sseguya, 2015; Okoli et al., 2024; Okumu
et al., 2025; Krishna et al., 2024; Raza et al., 2022; Mwalupaso et al., 2020

CSA Integration Haswell & Khataza, 2024; Das et.al., 2023; Cotter et.al, 2020; Yadav et.al.,
2024; Varinderpal-Singh et al., 2020; Sadhukhan et al., 2024; Pooniya et
al., 2015; Varinderpal-Singh et al., 2017; Rahman et al., 2024; Timsina et
al., 2021; MacCarthy et al., 2022; Upadhyay et al., 2025 

Types of mitigation benefits found in the studies: The reviewed literature primarily discusses three key areas related
to the mitigation benefits of digital agriculture technologies: enhanced carbon sequestration, nutrient use efficiency,
and the reduction of greenhouse gas (GHG) emissions. Enhanced carbon sequestration (4 references) is the least
frequently mentioned mitigation benefit, highlighting the need for further research on how digital agriculture can
contribute to soil carbon storage and climate change mitigation. Nutrient use efficiency (21 references) emerges as a
dominant theme, emphasizing the role of precision agriculture and data-driven decision-making in optimizing fertilizer
application, reducing environmental runoff, and improving overall sustainability. Reduction in GHG emissions (19
references) is also a highly cited benefit, with studies discussing the role of digital agriculture in minimizing fossil fuel
dependence, optimizing resource use, and integrating renewable energy solutions. The higher frequency of collated
reports of the latter two themes from the study authors suggests that research and policy efforts have largely focused
on efficiency-driven sustainability solutions, while direct carbon sequestration benefits remain underexplored in digital
CSA.

Mitigation benefits Summary

Enhanced carbon
sequestration

Carbon sequestration involves
capturing and storing atmospheric
carbon dioxide in soil and natural
systems. Enhancing this process
helps mitigate climate change and
improve soil health.

Nutrient use
efficiency

By improving nutrient use efficiency,
farmers can reduce environmental
impacts. Efficient nutrient
management improves yields and
minimizes runoff and pollution.

Table 6: Mitigation benefits described in the studies

Included studies that report this
mitigation benefit

Damba et.al., 2024; Okoli et al., 2024;
Varinderpal-Singh et al., 2020; Mbile et
al., 2015

Cotter et.al, 2020; Arouna et.al., 2020;
Oyinbo et.al., 2022; Ouedraogo et.al.,
2021; Khan et.al., 2021; Aoki et.al., 2024;
Oyeogbe & Das, 2015; Rakholia et.al.,
2024; Bhattarai et al., 2024; Krishna et
al., 2024; Liben et al., 2024; Tsolakis et
al., 2023; Raza et al., 2022; Pooniya et
al., 2015; Mwalupaso et al., 2020; Kaur
et al., 2024; Warpe et al., 2023; Okumu
et al., 2025; Sadhukhan et al., 2024;
VanCampenhout et al., 2020; Rahman
et al., 2024



17

Mitigation benefits Summary

Reduction in
Greenhouse Gas
Emissions

Lowering greenhouse gas emissions
including activity like reduced fossil
fuel usage contributes to climate
mitigation. This includes adopting
renewable energy sources and
energy-efficient technologies on
farms.

Included studies that report this
mitigation benefit

Anuga et.al., 2022; Yadav et.al., 2024;
Kulat et.al., 2022; Damba et.al., 2024;
Maertens et.al., 2023; Bijarniya et.al.,
2020; Schekhawa et.al., 2021; Singh
et.al., 2020; Varinderpal-Singh et al.,
2017; Jat et al., 2018; Timsina et al.,
2021; Haldar et al., 2022; Seki et al.,
2022; Nepal et al., 2024; Mittal et al.,
2018; Omotoso et al., 2024; Nchanji et
al., 2022; MacCarthy et al., 2022;
Upadhyay et al., 2025 

Co-benefits of mitigation: While digital CSA is widely promoted for its potential to enhance climate resilience and
productivity, its role in supporting climate mitigation and co-benefits, particularly for smallholder farmers, remains
insufficiently studied. The reviewed literature includes reports of various co-benefits associated with digital CSA, such
as increased farm productivity and crop yields, improved soil and water management, enhanced climate resilience,
economic and livelihood benefits, more efficient resource use, better pest and disease management, strengthened
social and knowledge networks, and contributions to biodiversity and ecosystem services. However, these findings
represent reported outcomes from individual studies rather than a systematic synthesis of evidence. Figure 4 presents
an overview of the co-benefits identified in the reviewed literature and the number of studies that mention each,
without assessing their methodological robustness or the strength of their findings.

Figure 4: Collated reports of co-benefits from study authors (x-axis = frequency)

Comparing mapping interventions and outcomes
Type of digital agriculture technology versus adoption drivers
Figure 5 presents a heat map illustrating the reported relationships between different types of digital agriculture
technologies and key drivers influencing their adoption. The technologies considered include Mobile Phones and Apps,
Sensor-Based Technologies, Decision Support Tools, and Precision Agriculture. From the studies reviewed, “resource
efficiency & precision farm management” appears as a frequently mentioned driver across all technologies, with
precision agriculture (n = 21) and sensor-based technologies (n = 14) being commonly associated with this factor. 



18

Figure 5: Type of digital agriculture technology by adoption drivers
reported by articles included in the data

Barriers affecting the adoption of different digital agriculture technologies: Figure 6 presents reported barriers
influencing the adoption of various digital agriculture technologies, reflecting challenges across economic, technical,
and infrastructural dimensions. Cost and economic concerns were frequently noted, particularly for precision
agriculture (n = 13) and sensor-based technologies (n = 7). Farmer education and technical capacity was another
commonly reported barrier, with mobile phones and apps (n = 10) and precision agriculture (n = 9) facing more
frequent challenges. Limited technology access and connectivity was reported as a barrier across multiple
technologies, particularly for precision agriculture (n = 10) and mobile phones and apps (n = 5). Awareness and trust
barriers were mentioned less frequently but still affected decision support tools (n = 5) and precision agriculture (n =
6). Similarly, infrastructure and advisory limitations were highlighted in several cases, most notably for precision
agriculture (n = 9). Technology suitability and data gaps were reported as challenges across technologies, particularly
for mobile phones and apps (n = 10) and decision support tools (n = 6). Technological risk perception was mentioned
infrequently, with only precision agriculture (n = 1) and sensor-based technologies (n = 3) having any reports of this
barrier. Overall, financial constraints, limited technical capacity, and connectivity issues were more frequently reported
barriers across digital agriculture technologies, based on study author reports. However, the relative influence of these
barriers requires further synthesis to determine their comparative impact on adoption.

Reports also indicate that “market & information access” is another frequent driver, particularly in the case of mobile
phones and apps (n = 3) and decision support tools (n = 4). Mentions of “farmer support & organizational networks” as
a factor influencing adoption vary, with mobile phones and apps (n = 5) and precision agriculture (n = 3) being linked
to this driver in several studies. “Farmer characteristics & education” is referenced less frequently, with some
association noted for mobile phones and apps (n = 3) and decision support tools (n = 2). “Government & institutional
trust” is mentioned in fewer studies overall, with mobile phones and apps (n = 8) being the most commonly linked to
this factor. Finally, “CSA integration” appears in relation to decision support tools (n = 8), sensor-based technologies (n
= 5), and precision agriculture (n = 5), suggesting some relevance for these technologies. These findings reflect
reported associations rather than a synthesized assessment of influence, and further analysis would be required to
determine the relative weight of each driver in shaping digital CSA adoption.



Inclusion, equity, and other co-benefits of digital CSA
Our analysis reveals that 38 out of 57 papers include information on inclusion or equity, while 28 papers report
information on yield improvements or benefits to farmers’ livelihoods. Furthermore, 20 of these papers addresses both
inclusion or equity considerations and economic benefits for farmers. In contrast, 19 papers do not mention equity,
inclusion, or economic advantages. Among studies focusing on inclusion, key areas of concern include gender, age,
cost-related access barriers, education, community development, participation, inequalities, and vulnerable
populations.

Examining the relationship between digital agriculture technology and inclusion, non-ICT-based technologies are
mainly associated with participation, cost, and education. Meanwhile, gender and age are less prominent in studies on
decision support systems (DSS), drones, and sensor technologies. Mobile phone-based technologies, however,
frequently address inclusion topics related to gender, age, and education.

Regarding gender disparities, the literature acknowledges variations in technology adoption and use between men
and women. There remains a lack of understanding regarding how digital CSA affects labor markets, land access,
governance structures, and power dynamics within food systems. Addressing these gaps is essential to developing
more holistic and equitable digital CSA strategies that align with both environmental and social sustainability goals.

Limitations of the map
While this study followed a systematic approach, certain limitations should be considered. First, the exclusion of non-
English publications may have led to language bias, omitting valuable perspectives on digital CSA from non-English-
speaking regions. Additionally, by concentrating on South Asia and Sub-Saharan Africa, the findings cannot be
generalized to other agricultural contexts with distinct socio-economic and environmental conditions, beyond our
study scope. Moreover, the reliance on published literature excludes insights from unpublished reports, policy briefs,
and firsthand stakeholder experiences. These decisions were necessary for reasons of practicality and to narrow the
initial corpus of literature to the most pertinent results. However, our choices may have led to critical limitation in
comprehensiveness and potential bias.

19

Figure 6: Type of digital agriculture technology by adoption barriers reported by
articles included in the data



20

CONCLUSIONS
Implications for policy and management
A key insight from this analysis is the frequent reporting of mobile phones and apps in digital agriculture, highlighting
their role in facilitating data collection, farmer-extension interactions, and access to climate information services.
However, this trend may partly reflect a measurement bias due to the limited scope of existing literature. Policymakers
should capitalize on the accessibility and widespread use of mobile technologies to enhance agricultural support
programs by integrating mobile-based advisory services, improving market linkages, and expanding digital financial
services for farmers.

Equity and inclusion emerged as critical themes, with many studies addressing these dimensions. Policymakers can
use these insights to design inclusive digital agriculture strategies by ensuring equitable access to digital tools,
tailoring services to marginalized groups, and fostering participatory approaches that include smallholder farmers,
women, and underserved communities in technology development and decision-making.

Additionally, the diverse barriers to digital agriculture adoption pose challenges of policy implementation. To address
this, future policy-oriented research should evaluate the relative significance of these barriers, allowing for targeted
interventions that improve technology accessibility, affordability, and usability. This evidence-based approach can
help ensure that digital agriculture solutions effectively reach and benefit a broad spectrum of stakeholders.

Implications for research
The systematic mapping process identified four key research themes in the literature: (a) studies measuring nutrient
use efficiency linked to digital agriculture, with some directly assessing greenhouse gas (GHG) emission reductions;
(b) studies reporting GHG reductions without a direct attribution to digital agriculture, often in the context of broader
advisory services; (c) research exploring how digital technologies influence the adoption of climate-smart agriculture
(CSA) practices—such as row planting, zero tillage, and drought-tolerant seeds—without directly quantifying
mitigation outcomes; and (d) studies highlighting mitigation co-benefits, such as increased farm productivity and
improved resource efficiency. These findings provide important insights into existing research gaps and opportunities
for future investigation.

While the current body of literature examines a broad range of digital agriculture technologies, more research is
needed to rigorously quantify their mitigation benefits across diverse farming contexts and scales. Strengthening the
evidence base on how digital agriculture contributes to climate change mitigation is crucial for designing effective
interventions. A significant portion of the reviewed studies focuses on mobile phone-based technologies, likely due to
their widespread accessibility, and measurement bias in our search strategy. However, this focus presents an
opportunity for future research to examine the mitigation potential of precision technologies, decision support
systems, and sensor-based tools more comprehensively.

A critical gap in the literature is the role of digital agriculture in soil health improvement, a key factor in climate
mitigation. While existing studies primarily assess nutrient use efficiency and GHG emissions, future research should
investigate the broader implications of digital tools on soil health, including their impact on soil carbon sequestration,
organic matter retention, and microbial activity. Addressing this gap could provide a more holistic understanding of
how digital technologies contribute to sustainable agricultural landscapes.

Additionally, there is an underexplored opportunity to assess the role of agricultural data in mitigation-oriented CSA
practices. Future studies should examine how data availability, collection, ownership, and governance affect farmers’
ability to leverage digital agriculture for climate mitigation. For instance, research on data sovereignty and ownership
rights among smallholder farmers is crucial to understanding how these factors influence their access to and benefits
from digital tools.

Beyond evaluating existing technologies, there is also an opportunity for researchers to adopt experimental and
participatory approaches in digital agriculture development. Many studies focus on widely available tools, but
incorporating co-design methodologies could lead to the creation of digital solutions that better align with the
specific social, ecological, and economic needs of farmers. Strengthening local technological development could
further support the creation of contextually appropriate digital tools that enhance mitigation outcomes.

Geographically, research gaps remain pronounced, particularly in Southern and Central Africa, where limited studies
have examined digital agriculture adoption and its mitigation potential. Expanding research efforts in these regions
will improve understanding of how digital tools can be effectively adapted to diverse agricultural systems.

Lastly, future research should explore typologies within the digital agriculture-mitigation relationship. Examining how
natural factors (e.g., crop types), technological benefits (e.g., efficiency gains), and mitigation goals (e.g., GHG
reductions) interact will be key to identifying the most effective digital interventions for different agricultural and
policy contexts. A deeper understanding of these dynamics will allow for more tailored, impactful climate-smart
strategies that maximize the benefits of digital agriculture.



21

DECLARATIONS
Ethics approval and consent to participate: Not applicable

Consent for publication: Not applicable

Availability of data and materials: The datasets used and analysed during the current study are available from the
corresponding author on reasonable request. 

Competing interests: The authors declare that they have no competing interests. There are no financial or non-
financial competing interests that are known to the review authors.

Funding: This study was financed by the Consortium of International Agricultural Research Centers (CGIAR).

Authors' contributions: MG, XY, SK, and PC designed the protocol and systematic map. MG, XY, SK, and PC elaborated
the systematic map and tested the methods. MG wrote the first draft of the systematic map, while XY, SK, and PC
contributed to editing and improving subsequent drafts of the systematic map. All authors read and approved the final
systematic map.

Acknowledgements: The authors thank Aditi Mukherji and Caroline Bosire for providing useful comments on previous
versions of the systematic map. The authors would also like to thank Neal R. Haddaway, Jacqualyn Eales, and Linda
Errington for their comments and suggestions.

Additional Files:
·      ROSES checklist form for systematic maps (Excel)
·      List of articles excluded from review and reason (Excel)
·      Initial Article Benchmark (Word)
·      ROSES flow diagram (Powerpoint)
·      Data Coding Process (Word)
·      Extracted Articles Coded (Excel)
·      Link to Evidence Atlas (Web link)
·      Definitions of the question components (Word)



22

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