How Unified is the Australian Agricultural Sector when ... - OSF

SocArXiv SocArXivPreprint : 4 May 2002 10.31235/osf.io/4nge5

How unified is the Australian agricultural sector when

talking to policy makers about digitalization?

Andrew TerhorstCSIRO Data61

Robert GarrardCSIRO Land & Water

Abstract

Technological advances are transforming agriculture in many parts of the world. De-spite the importance of agriculture to its economy, Australia lags behind other advancedeconomies in the adoption of digital agricultural technology. The country risks losing itscomparative advantage due to a weak policy around digital agriculture. This study com-bines network analysis and natural language processing to assess 654 public submissions tosix government inquiries addressing agricultural innovation, the digital economy, and ru-ral broadband communication networks, held between 2016 and 2018. The analysis showsthere is no consistent narrative driving policy development around digital agriculture.A fragmented policy landscape is a concern because it means that current and emerg-ing policies may not address key issues, resulting in sub-optimal outcomes for Australianagriculture.

Keywords: Australia, agricultural policy, digital transformation, natural language processing,network analysis.

1. Introduction

Technological advances are transforming agriculture in many parts of the world (Daum 2018).Digital agriculture helps farmers increase yields through data-driven decisions and technolo-gies and contributes to improved supply chain transparency (Watanabe et al. 2018; Nolet2018; Burroughs and Burroughs 2020; Chen 2020; Garrard and Fielke 2020). Moreover, digi-tal agriculture can help improve food security and resilience to the effects of climate change(Raheem et al. 2019).

In Australia, the agricultural sector comprises a relatively small proportion of the economy,accounting for approximately 2-3% of the gross domestic product. However, it is crucialnot only for feeding the domestic population but also as a significant exporter of premiumagricultural products to international markets. Approximately 77% of Australian agricultural

http://10.31235/osf.io/4nge5

2 How Unified is the Australian Agricultural Sector?

output is exported1. Australia enjoys a comparative advantage in agricultural production dueto its large endowment of arable land2. Technological advancement allows for productivitygains, such that more output can be produced with fewer resources3. If Australian agricultureis unable to adopt new technology at a similar rate to other countries, it risks losing thatcomparative advantage. Losing comparative advantage could result in short-run economicpain for Australia if resources currently employed in agriculture slowly reallocate to othersectors.

Preconditions for the uptake of digital tools by farms and agribusiness include access tohigh-speed internet, having sufficient digital literacy, and a coherent legal framework for thegovernance of digital assets (Jakku et al. 2018; Tirado-Morueta et al. 2018; Zhang et al. 2018;Wiseman et al. 2019). The recent rollout of the National Broadband Network (NBN) hashighlighted several gaps in government policy. For instance, the use of hybrid technologiescomplicate policy development as network capability varies according to the technology used.Neither is the planning of digital infrastructure across different levels of government (local,state, and federal levels) well coordinated. Some confusion exists among policymakers aboutthe role of government versus the private sector in developing the digital economy (Alizadeh2017). There is a need for coherent and integrated policy to support the uptake of digitaltools in agriculture.

One channel for affecting government policy in Australia is electronic submissions to parlia-mentary inquiries. Parliamentary inquiries direct a parliamentary committee to inquire intoa topic of interest, usually providing terms of reference determining the scope of the inquiry.When a committee begins an inquiry, it solicits views and opinions from interested membersof the public, both individuals and organizations, with online lodgement of these submissionsbeing preferred. Submissions are screened for relevance to the terms of reference and arepublished on the inquiry’s website. Public hearings usually follow after submissions close anda report is presented to parliament.4.

In this article, we use a combination of network analysis and natural language processing(NLP) to investigate 654 submissions to six Australian parliamentary inquiries relating tothe digital economy. As many other sectors stand to gain from the digital economy, thereis a concern the views of the agricultural sector may be drowned out by other sectors withcompeting interests, such as priority for obtaining NBN access. Our research question is “towhat extent is the agricultural sector unified regarding policy development around the digitaleconomy?”. Answering this question may help the agricultural sector better coordinate itsapproach to the emerging digital economy. We classified each submission as either relatingto the agricultural sector or not by keyword search on terms contained in the document. Wesought to determine whether submissions within the agricultural sector and across inquiriesare similar to each other, suggesting a consistent message, or whether submissions acrosssectors and within inquiries are most similar. We constructed a network linking submissionsthat were sufficiently similar according to their cosine similarity.

Our methodology is similar to that of Asher et al. (2019), who use social network analysis

1See https://www.nff.org.au/farm-facts.html2data.worldbank.org/indicator/AG.LND.AGRI.K2?most recent value desc=true3Although technology does not necessarily lower the input requirements of other resources in all cases. It

depends on whether the technology and other inputs to production are substitutes or complements (Brezina2010).

4See www.aph.gov.au/Parliamentary Business/Committees/House/Making a submission

https://www.nff.org.au/farm-facts.html

https://data.worldbank.org/indicator/AG.LND.AGRI.K2?most_recent_value_desc=true

https://www.aph.gov.au/Parliamentary_Business/Committees/House/Making_a_submission

SocArXiv 3

and NLP to analyse Twitter conversations surrounding parliamentary debates triggered bye-petitions in the United Kingdom. However, our network consists of similarities betweenwhole documents rather than terms frequently used together, although we also capture thisusing bigram frequency within each inquiry. Koltsova and Koltcov (2013) used NLP to mappublic discourse around the 2011-12 Russian presidential election and associated protests byconducting topic modelling on posts harvested from the blog website “LiveJournal”. Here,we also conducted topic modelling and sentiment analysis to extract the main concerns andsentiments being expressed in submissions from both the agricultural and non-agriculturalsectors across inquiries.

We found that the agricultural sector documents tended to be linked more often to non-agricultural sector documents within the same inquiry rather than agricultural documentsin other inquiries. Results of the NLP analysis were also consistent with this finding. Thisindicates the absence of a truly unified message regarding digitalization in the agriculturalsector. However, this does not rule out that such coordination does not exist in other policychannels, nor that successful agricultural policies cannot be achieved in the face of fragmentedpolitics (Hadwiger 1992).

2. Methodology

This study used a combination of natural language processing and network analysis to assess654 public submissions across 6 government inquiries. Manually assessing this many sub-missions in one study would be tedious and time-consuming. Natural language processingallows one to scan hundreds of submissions in a matter of minutes. One can easily surfaceabstract topics hidden in submissions. These can offer interesting insights into key concernsthe public may have. Combining natural language processing with network analysis allowsone to identify relationships between submissions. This is important for identifying commonpolicy concerns across the different inquiries and sectors.

2.1. Data sources

We searched the Parliament of Australia website for inquiries held by senate, house, andrelated joint committees5. Our criteria for inclusion into the data set were that the inquiryhad to relate to digitalization, digital economy, or agricultural innovation, according to itsterms of reference; and that the inquiry was completed sometime between 2011 and 2018,corresponding to the rollout of the NBN. We were able to find 6 relevant inquiries which hadpublished a total of 654 submissions. Table 1 lists the terms of reference, year, and totalnumber of public submissions received by each inquiry.

Submissions were downloaded from each inquiry’s website or from shared folders. Apartfrom those submissions relating to the digital economy strategy consultation process, allsubmissions were downloaded as PDF documents. The digital economy strategy consultationprocess accepted submissions captured via a web form, contained in email messages, and asemail attachments (attached documents). Non-PDF submissions were all converted into PDFfor the text analysis.

5www.aph.gov.au/Parliamentary Business/Committees

https://www.aph.gov.au/Parliamentary_Business/Committees


Tab

le1:

Data

sources.

Inqu

iryD

escriptio

nL

edby

Term

sof

reference

Year

Subm

issions

received

1A

gricultu

ralin

novation

Parlia

men

tary

Sta

nd

ing

Com

mittee

on

Agricu

lture

an

dIn

dustry

Investig

ate

(a)

how

the

efficien

cyof

agricultu

ralpractices

isb

eing

improv

eddu

eto

new

and

emergin

gtech

nology,

and

(b)

barriers

toth

ead

option

ofem

erging

technology.

2016135

2D

ataava

ilab

ilityan

duse

Pro

ductiv

ityC

om

missio

nD

etermin

ew

ays

toim

prove

the

availability

and

app

licationof

pub

licand

priva

tesector

data.

2017301

3D

igital

delivery

of

governm

ent

services

Parlia

men

tary

Fin

an

ceand

Pu

blic

Ad

min

istratio

nR

eferences

Com

mittee

Assess

(a)

wheth

erp

lann

edan

dex

isting

program

sare

able

todig

itally

deliver

services,

(b)

strategiesfor

whole

ofgov

-ern

men

tdig

ital

tran

sformation

,(c)

digital

pro

jectdelivery,

and

(d)

any

oth

errelated

matters.

201730

4D

igital

econom

ystrateg

ypu

blic

consu

ltationpro

cess

Dep

artm

ent

of

Indu

stry,In

novatio

nand

Scien

ceP

ublic

inp

ut

tohelp

Au

straliam

axim

iseth

ep

otential

eco-nom

icb

enefi

tsarisin

gfrom

new

and

emergin

gd

igitaltech

-nolo

gies.

2017141

5R

oll-o

ut

of

the

NB

Nin

rural

and

region

alareas

Parlia

men

tary

Join

tS

tandin

gC

om

mittee

on

the

Natio

nal

Bro

adb

and

Netw

ork

Assess

the

cap

acity

and

reliability

ofsatellite,

fixed

wireless

and

fixed

-line

netw

orks.

201822

6T

rade

system

and

the

dig

italecon

om

yP

arliam

enta

ryJoin

tS

tandin

gC

om

mittee

on

Tra

de

an

dIn

vestm

ent

Grow

th

Inqu

irein

toand

report

onth

eresp

onsiven

essof

Australia’s

trad

earch

itecture

and

regulatory

system

toth

econ

temp

o-ra

ryn

eeds

ofth

edigital

econom

yan

dd

isruptive

technology,

and

mea

sures

toim

prove

the

cyb

erresilien

ceof

Australia’s

trad

e-focu

sedbu

siness

sector.

201822

SocArXiv 5

Table 2: Corpus statistics. Relevant submissions refers to submissions determined to be related todigitalization in agriculture according to a keyword search on each document.

Inquiry Totalsubmissions

Relevantsubmissions

% Minwords

Maxwords

Meanwords

Agricultural innovation 135 34 25.2 898 69838 5784

Data availability & use 301 26 8.6 964 40475 8135

Digital delivery of governmentservices

30 1 3.3 27574 27574 27574

Digital economy strategy 143 50 35.0 585 26364 6874

NBN roll-out in rural/regionalareas

22 5 22.7 1276 5994 3138

Trade systems & the digitaleconomy

23 3 13.0 1573 3181 2516

654 119

2.2. Pre-processing

We created a corpus by loading by all PDF documents using the R programming language(R Core Team 2019). First, we removed document control characters, numbers, and universeresource locator (URL) references from the corpus. The corpus was then filtered to extractonly those submissions that referenced agriculture or digital technologies. Essentially, thisinvolved filtering documents that either included the morphemes “agri” or “farm” in the caseof the non-agriculture specific inquiries, or the morphene “digit” in the case of the inquiry intoagricultural innovation. This allowed us to classify each submission in the corpus as relatingto digitalization in agriculture or not.

We removed all punctuation and accents, and converted all the words to lower case. Unhelpfulterms were removed from the corpus. This involved limiting the vocabulary and removingstop words (e.g. a, the, we, to, too, this, that, with, when, etc.). A custom dictionarywas created to limit the vocabulary. The custom dictionary included 122806 words from theGrady Augmented word list in the QdapDictionaries package (Rinker 2013) and a list ofcomputing and IT abbreviations6. Stop words are rarely informative about the content of atext. Removing unhelpful terms from the corpus has the benefit of reducing the size of thedata, reducing computational load, and in some cases, also improving accuracy (Welbers et al.2017). Submissions to each inquiry often refer to the inquiry terms of reference. Repeatedreferences to the terms of reference or inquiry itself, can skew word counts and suppress inter-esting sub-texts (interesting content that distinguishes each submission). Hence, additionalfiltering was required to remove repeated references to the inquiry in question.

Each submission in the corpus was then broken down into a list of unigrams (single words) orbigrams (paired words such as“digital technologies”and“cyber security”). Table 2 summarizeskey statistics for the set of unigrams.

6Sourced from en.wikipedia.org/wiki/List of computing and IT abbreviations.

https://en.wikipedia.org/wiki/List_of_computing_and_IT_abbreviations


2.3. Network analysis

The aim of the network analysis was to link entities expressing common concerns in theirsubmissions using network analysis. A network was created by measuring the semantic dis-tance between submissions to determine similarity, and then applying a threshold to createedges linking between documents. To measure document similarity, we first constructed a listof unique words that appear in the entire corpus and determine how frequently each wordappears in each document.

We applied word stemming to the corpus to reduce inflectional and derived forms of a wordto a common base or root form. It is easier to relate documents using base or root forms ofwords compared to using morphological variants of words. We computed the“term frequency-inverse document frequency” (tf-idf) statistic for each word in the word-stemmed corpus.This statistic measures the relative importance of a word within each document in a corpus.Essentially, the importance of a word increases proportionally to the number of times itappears in each submission but is offset by how frequently the word appears in the corpus asa whole. The corpus was then transformed into a document-feature matrix, representing thetf-idf weight of stem words for each document in the corpus. Rows of this matrix correspondto each document, columns correspond to the set of unique stem-words in the corpus, andeach entry in the matrix is the tf-idf statistic corresponding to that document and word.

We measured similarity between two documents according to the cosine similarity of theircorresponding rows in the tf-idf matrix7. Cosine similarity measures the extent to whichdocuments share “important” stem words, and ranges from 0 (no similarity) to 1 (completelysimilar).

We constructed a network between documents by choosing an appropriate threshold for cosinesimilarity. Each vertex in the network represents a document and associated authorship(s)(i.e. entity or entities making the submission). An edge exists between two vertices if thecosine similarity of the corresponding documents exceeds a given threshold. Setting thatthreshold to 0 would would result in a complete network, where all documents are connectedto each other; setting the threshold to 1 would create an empty network, with no connections.A sensible threshold is one such that the resulting network is dense, but not “too” dense. Wechose the threshold to be two standard deviations above the mean of the empirical distributionof cosine similarities across all pairs of documents (Figure 1).

2.4. Term frequency counts

The text analysis was broken down into two parts. Part one measured how frequently a termappeared in the corpus. This included counting all the unique unigrams and bigrams, aswell as measuring the frequency of words with either a negative or positive sentiment. Termfrequency counts were done per inquiry and for the whole corpus. This allowed us to compareand contrast the different inquiries and assess how the different inquiries resonate with thebigger picture.

A basic task in sentiment analysis is classifying the polarity of words in a document as eitherpositive, negative, or neutral. More advanced sentiment analysis looks beyond polarity byexamining sentiment strength or emotional states (e.g. angry, sad, or happy). This study

7For two vectors v and u, cosine similarity is defined as (v ·u) / ||v|| ||u||, where || · || denotes the Euclideannorm of the vector. This value corresponds to the cosine of the angle between the two vectors.

SocArXiv 7

0

3

6

9

0.0 0.1 0.2 0.3 0.4 0.5Cosine Similarity

Den

sity

Figure 1: Empirical distribution of cosine similarities between pairs of documents in the corpus.

only considered the polarity of words using the “Bing” lexicon, which contains 6786 words, ofwhich 4782 are classed negative and 2006 are classed positive (Hu and Liu 2004). Assessingsentiment on the polarity of words alone has limitations. For example, the phrase “this ispretty bad” expresses a negative sentiment but the words “pretty” and “bad” have oppositepolarity. Despite the high ratio of negative to positive words, and the inability to accountfor qualifiers before a word, the “Bing” lexicon has been shown to capture overall sentimentreasonably accurately (Silge and Robinson 2017).

By counting unigrams and bigrams and then assessing the overall sentiment in each submis-sion, we were able to obtain insights into what issues were of most concern to the entitiesmaking submissions, and how they felt about these issues. However, term frequency countsare unable to tell us much about abstract topics hidden in documents. The topic structure ishidden insofar as we can only observe the documents and words, not the topics themselves.Part two of the text analysis used more advanced statistical text analysis to surface thesehidden topics. Two natural language processing methods were employed to infer the topicstructure, namely Latent Dirichlet Analysis (LDA) and Word Vector Analysis. Both methodsassume that each document in a corpus contains a mix of topics found throughout the corpus.

2.5. Topic modelling using LDA

LDA is a probabilistic topic model and it treats each document as a mixture of topics andeach topic as a mixture of words. This allows documents to “overlap” each other in terms ofcontent, rather than being separated into discrete groups, in a way that mirrors typical useof natural language (Silge and Robinson 2017).

This study used the topicmodels package (Grun and Hornik 2011) to find topics of interestusing LDA. The LDA employed Gibbs sampling (Geman and Geman 1984) to randomly assignwords from each document to one of k topics. This random assignment generates inaccuratetopic representations of all the documents and word distributions of all the topics. To improve


min

imiz

em

axim

ize

2 4 6 8 10 12 14 16 18 20 25 30 35 40 45 50 60 70 80 90 1002 4 6 8 10 12 14 16 18 20 25 30 35 40 45 50 60 70 80 90 1002 4 6 8 10 12 14 16 18 20 25 30 35 40 45 50 60 70 80 90 1002 4 6 8 10 12 14 16 18 20 25 30 35 40 45 50 60 70 80 90 100

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

number of topics

metrics:

Griffiths2004

CaoJuan2009

Arun2010

Deveaud2014

Figure 2: Finding the optimal number of topics to model using LDA. The plot indicates kshould be either 35 according to Arun et al. (2010), 50 according to Cao et al. (2009) andGriffiths and Steyvers (2004), or 18 according to Deveaud et al. (2014). This study usedk = 18.

the accuracy, the algorithm iterates through each document d and for each word w and foreach topic t in d, computes two things: P (topic t | document d) representing the proportionof words in document d that are currently assigned to topic t, and P (word w | topic t) repre-senting the proportion of assignments to topic t over all documents that come from this wordw. The algorithm then reassigns w to a new topic, where it samples topic t with probabilityP (topic t |document d) ·P (word w | topic t). Each iteration assumes all the topic assignmentsexcept for the current word in question are correct. The assignment of the current word isupdated using the assumed mixture of topics and mixture of words (topic model). This isrepeated either for a fixed number of iterations or until a steady state is reached, where theprobability of word w belonging to topic t and the probability of topic t belonging to documentd is high. We can estimate the topic mixtures of each document by counting the proportion ofwords assigned to each topic within that document, and the words associated to each topic bycounting the proportion of words assigned to each topic overall (Liu 2015). Determining thenumber of topics to model is always a challenge with LDA. This study used the ldatuning

package to find the optimal number of topics in a corpus (Nikita 2019). Figure 2 shows thethe optimal number of topics falls between 18 and 50. We chose to use the most parsimoniousvalue and set k = 18.

SocArXiv 9

2.6. Word vector analysis

Though LDA is effective for identifying topics defined by a dense mixture of words, it performsless well when attempting to identify topics with a low density of words. Word vector analysisis a relatively new approach to topic modelling that can deal with this limitation. The wordvector encodes the semantic relationship among words by examining the extent to which atarget word is embedded amongst other words (Mikolov et al. 2013).

A novel “skipgram” approach for word vector analysis was used in this study (Levy et al.2015). This approach, outlined by Moody (2017) and implemented in R by Silge (2017),uses a fixed-size sliding window to count how often a word is found near another word (inour case, we fixed the window to span 8 words). From this, one can calculate which wordpairs occurred together more often than expected based on how often they occurred on theirown (skipgram probabilities). When this number is high, the two words are associated witheach other and are likely to occur together. When this number is low, the two words are notassociated with each other and are unlikely to occur together. The probability of any twowords occurring together is then transformed into a sparse matrix from which word vectorscan be extracted (Levy et al. 2015). This involves compressing the sparse matrix into densesub-matrices that represent semantic relationships among words. These relationships are thendecomposed into word vectors using singular value decomposition. The final step is to applyprincipal component analysis (PCA) to the word vectors to identify topics (Shin et al. 2018).Lower order components are likely to contain general purpose words that account for the mostvariation in the corpus whereas higher order components are more likely to contain technicalor topical words.

3. Results

3.1. Network analysis

Figure 3 displays the similarity network for all documents. This network has a density of0.048, which is consistent with many real world networks (Melancon 2006)8. The algorithmused to construct the embedding for drawing the graph (Fruchterman and Reingold 1991)forces the graph to be separated into highly connected clusters. Figure 3 uses vertex symbolsto differentiate between the different inquiries and vertex colour to distinguish documents thatreference agriculture specifically and those that do not. Documents clearly cluster by inquiryrather than sector. This is further reinforced by Figure 4 which separates the corpus intotwo sub-networks, one relating to documents that refer to digital agriculture and the otherto documents that do not. We see that digital agriculture submissions also cluster by inquirywith very little between-inquiry connectedness. This level of connectedness is reflected inthe analogous network for non-digital agriculture submissions. We found digital agriculturesubmissions are more similar to non-digital agriculture submissions within the same inquirythan to other digital agriculture submissions in different inquiries. This is not unexpected,given submissions will tend to address the terms of reference for each parliamentary inquiry.

In what follows, we explore the results of our NLP analysis. We performed this analysis on thesubset of documents identified as relating to digital agriculture to elucidate the key concernsof this sector. However, we have also repeated this analysis on the non-digital agriculture

8Density here is defined to be the number of edges present divided by the number of possible edges.


documents and the entire corpus and found no significant change to the results, consistentwith the finding from our network analysis.

Most common unigrams

Figure 5 depicts the 20 most common unigrams in each inquiry (note that words foundin inquiry title are excluded). The top 20 unigrams draw attention to key concerns. Inboth the agricultural innovation and digital economy strategy inquiries, the terms “data”and “technology” feature strongly. With the inquiry into agricultural innovation, the subtextappears to be around data and technology to support the development of food productionsystems. The subtext in the digital economy strategy consultation process appears to beabout business opportunities, services, security, and skills development.

The terms “research”, “information”, and “public” feature strongly in the inquiry into dataavailability and use. However, the subtext is less clear in this inquiry. Judging by the topwords in the inquiry into the roll-out of the NBN in rural and regional areas, the type ofservices being offered seems to be the key concern.

The single relevant submission to inquiry into the digital delivery of government servicesappears to be concerned about disadvantaged people being able to access such services (e.g.remote indigenous communities, people from low socio-economic backgrounds, and peoplewith disabilities). The terms “standards” and “international” dominate the inquiry into tradesystems and the digital economy.

Looking at the corpus in its entirety, “data”, “digital” are the dominate terms. The subtextappears to be about research and development of innovative digital services in the agriculturalsector.

Most common bigrams

Figure 6 depicts the 10 most common bigrams in each inquiry. Bigrams offer more insightinto issues than what unigrams are able to show. The bigrams “farm policy”, “precisionagriculture”, and “animal welfare” dominate the inquiry on agricultural innovation. Lowerranked bigrams include “supply chain”, “genetically modified crops”, “farming systems”, and“production systems”. Looking at the inquiry into data availability and use, the bigrams“private sector”, “public sector” and“natural disaster” feature prominently. The other bigramsindicate investment and impact are key concerns. Switching attention to the digital economystrategy consultation process, “cyber security” is the dominant bigram. The other bigramssuggest “artificial intelligence”, “data analytics”, and “machine learning” are important for“industry innovation” and “economic growth”. With respect to NBN roll-out in rural andregional areas, the top 10 bigrams reinforce concern around the type of broadband servicebeing offered highlighted in the top 20 unigrams. Standards feature prominently in theinquiry into trade systems and the digital economy (viz. “international standards”, “standardsdevelopment”, and “blockchain standards”). Other issues of concern include “data flows”,“cyber security”, “technical regulations”.

Looking at the corpus as a whole, the bigrams “digital economy” and “agricultural innovation”dominate. This is unsurprising as submissions to the inquiry into agricultural innovation andthe digital economy strategy consultation process dominate the corpus. Of interest are thelower-ranked bigrams, namely “digital agriculture”, “private sector”, “public sector”, “digitaltransformation”, and “data sharing”. Another way to visualise term frequency is through the

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SocArXiv 13

digitalfarming

alsoadoption

productionmanagementdevelopment

ruraluse

systemsinformation

foodfarmers

technologiesresearchindustry

farmagriculturetechnology

data

0 300 600 900n

Agricultural innovation

healthcommunity

managementagenciesdisasterprivateimpact

valuealso

policyavailable

naturalservices

opensector

nationalaccesspublic

informationresearch

0 500 1000 1500n

Data availability & use

futurecyberworkuse

informationopportunities

agriculturealso

developmentneedskills

innovationsecurity

technologiesservices

businessesindustry

businesstechnology

data

0 500 1000 1500 2000 2500n

Digital economy strategy

sociallow

personalservice

infotwitter

indigenoustechnology

incomedisability

communicationsaccess

consumerremotepeople

communitiesinternetviewed

informationconsumers

0 50 100 150n

Digital delivery

consumersfarmersmuster

technologybroadband

skybusinesses

lineconnectivity

networkaccessremotedigital

infrastructurewesternwirelesssatellite

fixedservicesservice

0 50 100n

NBN roll−out

commercetechnologies

innovationeconomic

growthservicesstrategy

alsosecurity

informationindustry

opportunitiesglobal

businessesdevelopment

systemdata

businessinternational

standards

0 25 50 75n

Trade systems

sectorbusinesses

developmentalso

technologiesaccess

agricultureagricultural

publiceconomy

usebusinessservicesindustry

innovationinformation

researchtechnology

digitaldata

0 2500 5000 7500n

Complete corpus subset

Figure 5: Most common words in the corpus. Note the bag-of-words analysis did not involveany word-stemming (word-stemming featured only in the analysis of document similarity).


dairy industry

row crop

spring quarter

production systems

farming systems

gm crops

supply chain

animal welfare

precision agriculture

farm policy

0 40 80 120n


resilience decisions

issues paper

social impact

impact investing

disaster resilience

impact investment

natural disasters

natural disaster

private sector

public sector

0 50 100 150 200n


economic growth

supply chains

machine learning

public libraries

artificial intelligence

business models

private sector

data sharing

industry innovation

cyber security

0 100 200 300 400n


homeless people

action network

communications consumer

consumer action

income earners

vulnerable consumer

indigenous communities

personal communications

low income

remote indigenous

0 20 40 60n

Digital delivery

muster satellite

voice services

line services

northern territory

satellite fixed

retail service

service providers

digital connectivity

fixed line

sky muster

fixed wireless

0 20 40 60 80n

NBN roll−out

investment growth

asia pacific

data flows

standards development

cross border

iso iec

industry innovation

international standards

cyber security

northern territory

0 3 6 9n

Trade systems

data sharing

public sector

digital transformation

digital technology

digital technologies

private sector

digital agriculture

cyber security

agricultural innovation

digital economy

0 500 1000n


Figure 6: Most common bigrams in the corpus.

SocArXiv 15

rolemany

online

help

example

farm

access

program

based

years

potential

highimpact

education

adoption

informationfa

rme

rscost

issues

provideacross

businesses

growth

digitalmarket

global

ma

ke

data

decision

benefits

public

opportunitiesprivacy

people

standardsneeds

industries

social

food

time

areas

cyb

er

research

system

business

also

future

service

used

risk

development

http use

health

technology

strategy

sta

te

improve

value

regional

including

infr

astr

uctu

re

agriculture

important

work

significant

better

private

support

well

world

security

skills

rural

one

services

sector

national

systems

industry

innovation

investment

need

community

eco

no

mic

making

agricultural

management

policy

international available

within

report

open

tech

no

log

ies

level

economy keyinternet

ensure

precision agriculturepolicy journal

digital economysector data

digital divide

cyber securityemerging technologies

digital transformation

digital infrastructuremachine learning

natural disaster

industr

y innovation

impact investing

public

data

data sets

economy strategy

artificial intelligence

digital technologiessupply chain

social media

rese

arc

h d

ata

real timedigital inclusion

data sharing

fixed wireless

na

tio

na

l d

igita

l

digital delivery

quarter agriculturalsupply chains

digital technology

digital agriculture

business models

private sector

service providers

public sector

linked data

agricultural innovation

se

rvic

e d

eliv

ery

data

managem

ent

disaster resilience

digital literacy

impact investment

agricultural sector

data availability

digital disruption

personal information

digital skills

farm policydata collection

natural disasters

Figure 7: Word clouds for the top 100 unigrams and top 50 bigrams.

use of word clouds. Figure 7 depicts the top unigrams and bigrams in the corpus. This figureis able to convey a little more information than what Figures 5 and 6 are able to.

Sentiment analysis

Figure 8 depicts the 10 most common positive and negative sentiment words. Negative wordscommon to all inquiries include“critical”, “limited”and“issue(s)”. “Risk” is another commonlyexpressed negative sentiment. Other negative words highlight inquiry-specific concerns e.g.“complex” and “lack” for the inquiry into agricultural innovation; “disaster” “hazards” and“emergency” for the inquiry into data availability and use; “disruption”, “threats”, and “com-plex” for the digital economy strategy consultation process; “vulnerable”, “discrimination”,“poverty” and “poor” for the inquiry into the digital delivery of government services; “lack”,“poor”, “emergency”, “inefficient”, “problems”, and “congested” for the inquiry into the NBNroll-out in rural and regional areas; and “unnecessary”, “threats”, “lack”, “inadequate”, and“disruption” for the inquiry into trade systems and the digital economy.

Positive words common to all inquiries include “support” and “work”. Other common positivewords include “available”, “benefits”, “better”, “important”, “improve(d)”, “significant”, and“innovation”. Other positive words that distinguish individual inquiries include “best” (agri-cultural innovation), “effective” (data availability and use), “trust” (digital economy strategy),“accessible” (digital delivery of government services), “affordable” and “reliable” (NBN roll-out), and “secure”, “leading”, and “competitive” (trade systems). Negative words “issues”,“risk”, and “disaster” dominate the corpus as a whole. “Innovation” is the dominant positiveword in the corpus.

Topic modelling

Interpreting unigrams and bigrams is difficult in the absence of context. Topic modelling ex-poses hidden semantic relations in a corpus and can offer a more nuanced view of key concerns.Figure 9 lists the top 10 topics modelled using LDA (k = 18, see figure 2). Because topic


negative positive

0 50 100 150 200 0 100 200

best

significant

improve

better

work

benefits

important

well

support

available

pest

problems

concerns

complex

lack

critical

risk

issue

limited

issues

n


negative positive

0 100 200 300 400 0 100 200 300 400

significant

effective

improve

innovation

important

work

well

better

support

available

issue

concerns

limited

hazard

risks

critical

emergency

issues

risk

disaster

n


negative positive

0 50 100 150 200 0 200 400 600 800

trust

like

significant

benefits

important

available

well

support

work

innovation

disruptive

complex

threats

limited

risks

disruption

lack

risk

issues

critical

n


negative positive

0 20 40 0 20 40 60

benefit

well

work

benefits

recommendations

accessible

better

free

engaging

support

burden

poor

poverty

risk

discrimination

issues

limited

critical

lack

vulnerable

n

Digital delivery

negative positive

0 5 10 15 0 10 20 30

affordable

better

work

support

improve

reliable

improved

recommendations

recommendation

available

congested

critical

problems

inefficient

emergency

limited

poor

lack

issue

issues

n

NBN roll−out

negative positive

0 2 4 6 0 5 10 15 20

competitive

leading

secure

improve

significant

supporting

important

support

work

innovation

disruption

disruptive

inadequate

limited

issues

lack

risks

threats

unnecessary

critical

n

Trade systems

negative positive

0 200 400 600 0 500 100015002000

improve

significant

better

benefits

important

well

work

available

support

innovation

concerns

complex

issue

risks

lack

limited

critical

disaster

risk

issues

n


Figure 8: Positive and negative sentiment.

SocArXiv 17

also, research, use, development, provide, support, well, information, based, including

digital, economy, businesses, business, technologies, technology, industry, security, cyber, skills

agricultural, innovation, agriculture, technologies, industry, technology, systems, farmers, farm, soil

data, public, information, use, access, sector, available, open, value, agencies

technology, data, people, business, cloud, customers, experience, customer, google, work

regional, areas, service, rural, services, mobile, satellite, access, network, internet

innovation, technology, economy, companies, policy, business, recommendation, must, capital, space

trade, standards, international, tourism, tax, https, system, consultation, technology, industry

data, agriculture, digital, industry, agricultural, decision, producers, value, farm, industries

iot, page, data, applications, city, devices, innovation, markets, cities, networksTopic 15

Topic 13

Topic 9

Topic 14

Topic 16

Topic 6

Topic 12

Topic 3

Topic 11

Topic 4

0% 10% 20% 30% 40% 50%γ

Figure 9: Top 10 topics in the corpus.

modelling requires a reasonably sized corpus to produce meaningful results, only the entirecorpus was modelled, not the individual inquiries. Topic 2 is ranked highest and appears tofocus on research and development. This is followed by Topic 5, which is about the role ofbusiness in the digital economy and cyber-security. Topic 13 addresses agricultural technologyand innovation. Access to public data and information dominates Topic 3. Topic 11 appearsto be about user experience while Topic 7 addresses open data. Telecommunications infras-tructure is the subject of Topic 12. Topic 9 is about companies investing in innovation. Topic4 is about how digital agriculture can aid farm management. Topic 8 addresses internationaltrade and standards development.

Word vector analysis

As stated earlier, word vector analysis allows one to assess word embeddedness i.e. wordsthat are closely associated with, or proximal to, a target word. Figure 10 depicts the first 16principal components of the word vectors extracted from the corpus. Lower-order componentsaccount for much of the word variation in the corpus whereas higher-order components are


Dimension 13 Dimension 14 Dimension 15 Dimension 16




−0.1 0.0 0.1 −0.1 0.0 0.1 −0.1 0.0 0.1 −0.1 0.0 0.1

datainformation

learningpeople

technologiesworld

jobsfuture

economytechnology

skillsdigital

systemssoftware

computerstorm

populationmillioncostssouth

cyclonedisasteraverage

disasters

kellyabuse

childact

policelaw

criminalpoliticalmother

researchinformation

data

vitamindeficiency

childrenkelly

aldiarrhoeamortality

africapeoplenaturalstormgene

productioncropsfoodcropmeat

riceanimal

gmfarm

cottonlivestock

agricultural

consumersfoodgm

privacypeople

attitudesfoods

consumerweather

watersensors

mapping

healthvitamin

riskhttp

systemssocial

peopleservices

publicfood

systemaccess

geneticviewed

wooltraits

participantstimemale

farmingfarmers

farmcattle

women

riskonlineworld

servicehealth

httpinternet

farmsystem

foodtime

people

researchdevicesnetwork

connectionwireless

fixedserviceremotesatellite

phonemobile

internet

ruralwomen

farmfarmingissues

indiacanada

modifiedgenetically

cropsgm

china

genesatellite

ruraltimelambewetax

businesscompanies

sheepmeatwool

digitaldata

innovationeconomycomputer

childrenresult

personfarms

measureday

kelly

httpuniversity

sydneyviewed

southinstitute

centredirector

executivecouncillibrary

data

regionalinnovation

serviceagricultural

wirelessmobile

skillskelly

learningchildren

schoolstudents

httphttps

pdfstudentssupportschool

trainingeducationprograms

regionalservice

program

Value

Figure 10: First 16 principal components of the corpus with top words contributing to eacheigenvector. Lower-order components account for much of the word variation in the corpuswhile higher-order components are more likely to contain words of technical or topical interest.

more likely to contain words of technical or topical interest. Dimension 3 appears to be aboutcrop and livestock farming more generally. Jobs and skills dominate Dimension 4. Dimension6 refers to telecommunications infrastructure. Natural disasters feature strongly in Dimension8. Dimensions 9 and 13 are about young people’s education. Dimension 10 refers to geneticallymodified crops in context of other countries. Health and nutrition appears to be the mainconcern in Dimensions 11 and 16. Dimension 12 refers to child abuse. Sheep farming is themain topic in Dimension 14. Dimension 15 refers to male and female participation in livestockfarming. Other dimensions do not appear to make much sense.

Figure 11 shows the top 20 proximal words to “digital”, “economy”, “agriculture”, and “tech-nology”. “Digital” is closely related to “economy” and vice versa. Other words commonlyassociated with “digital” include “government”, “technology”, “divide”, “strategy”, and “busi-ness”. Apart from the association with the term “digital”, the term “economy” is closelyassociated with “strategy”, “government”, “future” and “opportunities”. The term “agricul-ture” is closely related to “innovation”, “precision” (as in precision farming), “industry”, and“technology”. With respect to the term “technology”, it is closely related to “innovation”,“technologies”, “digital”, and surprisingly, “women”.

SocArXiv 19

skillsfuture

datainclusion

accessdivide

developmentpublic

businessestransformation

agriculturetechnologies

strategyinformation

businessindustryservices

innovationtechnology

economy

0.00 0.02 0.04 0.06value

Proximal words to *digital*

sectortechnologies

timenational

developmentworld

economicbusinesses

opportunitiesinformation

publicservicesbusiness

futurestrategy

conversationindustry

technologyinnovation

digital

0.00 0.02 0.04 0.06value

Proximal words to *economy*

iowahouse

informationproject

adoptionresearch

farmuniversity

sectortechnology

acceleratingtechnologies

decisionfuturereportdigital

innovationindustry

precisionagricultural

0.00 0.01 0.02 0.03 0.04value

Proximal words to *agriculture*

businessesdevelopment

agriculturegene

accessscience

publicresearchadoption

womenservices

sectorindustry

informationeconomy

agriculturaltechnologies

businessinnovation

digital

0.00 0.02 0.04value

Proximal words to *technology*

Figure 11: Words in close proximity to target words “digital”, “economy”, “agriculture”, and“technology”.


4. Discussion

Our results show that one cannot rely on a single type of analysis to make sense of topicsbeing discussed in the corpus. This is especially true when looking at the results of the topicmodelling and word vectors analysis. These make more sense when interpreted together withunigrams, bigrams, and positive/negative sentiment words. Judging by the broad natureof topics discussed, it is clear many of the public submissions made only passing referenceto digital agriculture. This is not surprising, given the terms of reference for each inquirydid not specifically address digital agriculture. The analysis may have benefited from moresophisticated filtering to reduce unhelpful submissions.

Responses to the inquiry into agricultural innovation emphasise the importance of digital tech-nologies for precision farming, improved production, and effective supply chain management.The key issue with the inquiry into data availability and use appears to be disaster resilience.Farming practices hardly rate any mention, which is surprising, given digital agriculture isvery data intensive. The sentiment analysis did indicate better access to data would supportinnovation.

Only one submission to the inquiry into the digital delivery of government services referencedagriculture. This submission was mostly concerned about government services delivered toremote indigenous communities, not so much about agriculture.

In contrast, the digital economy strategy consultation process had the highest proportion ofsubmissions referencing agriculture. Though cyber-security is a major concern, the submis-sions also refer to the economic benefits stemming from agricultural innovation. Artificialintelligence and machine learning appear to be especially important in this respect. The sen-timent analysis highlighted the disruptive and risky nature of the digital economy. One cansee from the network analysis a shared concern around the role of business and cyber-securityin the digital economy.

Unfortunately, the inquiry into the roll-out of the NBN in rural and regional areas is notvery informative. Much of the concern is around the type of NBN service being offered. Howbroadband services can facilitate the uptake of digital agriculture is not really addressed. Nei-ther is the inquiry into trade systems and the digital economy very informative. Agriculturedoes not feature strongly at all. Nonetheless, submissions to this inquiry reinforced broaderconcerns around cyber-security.

Generally, the emerging digital economy is seen by many as a catalyst for innovation. Thetopic modelling and word vector analysis highlight the need for ongoing support of researchand development. Both point to improved farm management and enhanced productivitystemming from the use of digital technologies and the emergence of new and innovativeindustries. Better access to data and investment in skills development and training is alsohighlighted. However, the network analysis shows that the digital agriculture policy landscapeis extremely fragmented. Only the digital economy strategy consultation process showed anysign of coherency.

5. Conclusions

Is the Australian agricultural sector unified when talking to policy makers about digitaliza-tion? From this study, it is clear the digital agriculture policy landscape is quite fragmented.The fragmented policy landscape is a concern because it means that current and emerging

SocArXiv 21

policy may not address key issues, resulting in sub-optimal outcomes for Australian agricul-ture. There does not appear to be a consistent narrative driving policy development arounddigital agriculture. This may be partly due to under-appreciation of the potential economicbenefits arising from the digitalization of agriculture, something that may need to be ad-dressed through stronger advocacy (the under-appreciation is reflected in omission of digitalagriculture in the terms of references for each inquiry).

Natural language processing is a cost-effective approach for assessing public engagement withpolicy development. Not only does it allow one to scan hundreds of public submissionsvery fast, one can also easily surface abstract topics hidden in the submissions. These offerinteresting insights into key concerns the public may have. However, one needs a range ofmeasures to make proper sense of submissions.

This study has some limitations. Focusing only on public submissions to government inquiriesdoes not necessarily highlight all policy concerns. The terms of reference for each inquiry didnot address digital transformation of the agricultural sector specifically. It is clear many of thesubmissions in the corpus only made passing reference to digital agriculture. As a consequence,the corpus is quite noisy and some relevant or important issues may be buried in the noise.Repeated reference to the inquiry in question can skew word counts and suppress interestingcontent that really matters. We tried to address this by filtering out words contained inthe title of the inquiry. However, the effectiveness of this was not assessed. There is meritin in doing a comparative analysis of NLP results with results from traditional qualitativeapproaches (manual coding of texts).

Future studies should not be limited to analysing public submissions to government inquiries.Other data sources may yield valuable policy-related information e.g. social media posts,farmer blogs, research publications, on-line news articles, and industry association newsletters.

Applying more advanced and sophisticated natural language processing techniques shouldalso yield better results. Future natural language processing should also be more targetedto specific concerns. Word vector analysis is particularly useful in this regard. One canuse specific target words to surface important issues (by analysing word associations/wordembeddedness). It would be useful to do repeat studies of this nature to observe trends.Future work should also consider ways to reduce statistical noise in datasets through moresophisticated filtering.

Acknowledgements

The authors wish to acknowledge Ryan McAllister for inspiring this work. Thanks to CSIROData61 colleagues Stephen Wan, James McHugh, and Aditya Joshi for their suggestionsaround natural language processing strategies. CSIRO Land & Water Colleagues Bruce Tay-lor, Emma Jakku, Aysha Fleming, and Simon Fielke provided critical feedback at variousstages of analysis. This work would not have been possible without the online R tutorialsand recipes provided by Julia Silge, David Robinson, Bernhard Learns, and Kailash Awati.CSIRO’s “Digiscape Future Science Platform” funded this study.

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Affiliation:

Andrew TerhorstCSIRO Data61Hobart, AustraliaE-mail: [email protected]: https://people.csiro.au/T/A/Andrew-Terhorst.aspx

SocArXiv Website https://socopen.org/

SocArXiv Preprints https://osf.io/preprints/socarxiv

Preprint 2022 pp. 1–24 Submitted: 4 May 200210.31235/osf.io/4nge5 Accepted: 7 May 2022

mailto:[email protected]

https://people.csiro.au/T/A/Andrew-Terhorst.aspx

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