Breaking the ‘big data’ barrier when selecting agricultural export markets: 1

An innovative approach 2

3

4

Abstract 5

In the search for new markets or new product opportunities in existing markets, a major 6 challenge is making sense of the huge volumes of available product and market information, 7 which is one of the manifestations of ‘big data’. In the agricultural sector, erratic weather 8 patterns are upsetting global production patterns and creating new opportunities for many 9 producers. However, the latter have to react quickly and confidently in order to measure up to the 10 ever-present competition. Using an example from the South African fruit industry, this paper 11 illustrates how the big data challenge can be tackled using the TRADE-Decision Support Model 12 (TRADE-DSM). With its powerful data filtering system, the TRADE-DSM screens innumerable 13 product-market combinations (‘export opportunities’) to reveal the most promising at a national, 14 provincial or industry level. The TRADE-DSM is attracting a growing following among those 15 who prefer to put their energies into developing, rather than looking for, export markets. 16

17

Keywords: big data; decision support model (DSM); TRADE-DSM; realistic export 18 opportunities; fruit industry 19

JEL codes: F 140, F 130 20

Breaking the ‘big data’ barrier when selecting agricultural export markets: 21 An innovative approach 22

23

1. Introduction 24

In the face of increasingly erratic global weather patterns that are disrupting agricultural 25 production in many parts of the world, governments are under pressure to rethink their 26 agricultural policies so that they can respond efficiently if and when they are faced with 27 impending food shortages at home. On a more positive note, the changing agricultural landscape 28 is also creating new and/or extended market opportunities for agricultural exporters, particularly 29 from the developing world. Yet such opportunities need to be scientifically determined if they 30 are to generate sustainable returns; they cannot merely be the result of hearsay or casual analysis. 31

South Africa has reached the proverbial crossroads where its agricultural development is 32 concerned, and there is a growing realisation that a more focused, co-ordinated and supportive 33 government export strategy is needed. Like many other countries, South Africa is being 34 compelled to look for new outlets for its agricultural exports in the face of weak trading 35 conditions at home and lacklustre export performance. Given the sensitivity surrounding land 36 ownership and usage in South Africa, the government’s revised export strategy needs to reflect 37 both economic and social imperatives. It is within this context that South Africa’s Department of 38 Agriculture, Forestry and Fisheries (DAFF) is looking to formulate and implement a results-39 driven export strategy for the sector, which will be the launching pad for new farming enterprises 40 and will stimulate job creation. In its strategy, the Department aims to prioritise those crops with 41 high international growth potential, high value, and high job creation multipliers. However, 42 arriving at a set of answers that addresses this complex interplay of factors is far from 43 straightforward, given the correspondingly complex task of collecting, sorting, and analysing 44 data. 45

What South Africa’s agricultural export sector needs is a practical way of tackling the ‘big data’ 46 challenge, i.e. efficiently identifying the most promising export opportunities at a given point in 47 time from the confusing mass of information that is constantly spilling into the public domain in 48 the form of data sets, research findings, industry and government analyses, and general 49 commentaries. 50

This paper addresses this challenge by introducing the TRADE-Decision Support Model 51 (TRADE-DSM), a scientific methodology encapsulated in a convenient tool which is able to 52 reveal realistic export opportunities (in the form of promising product-market combinations), at 53 the 6-digit detail Harmonized System1 (HS) product level, for a particular country, province or 54 industry sector. The TRADE-DSM has the ability to reduce vast quantities of information to 55 manageable proportions, thereby creating order out of disarray. It is particularly valuable to those 56 in government and the business sector who are tasked with formulating export growth and 57 diversification strategies but who find the traditional tasks associated with ‘big data’ – i.e. high-58 volume and sophisticated data collection, processing and analysis – to be unfeasible from a 59 technological or skills perspective. 60

1 Harmonized Commodity Description and Coding System, also known as the Harmonized System (HS). See http://www.wcoomd.org.

1

The paper illustrates the practical application of the TRADE-Decision Support Model (TRADE-61 DSM) and its companion software, TRADE-DSM NavigatorTM, using fruit and nuts (chapter 08 62 in the Harmonized System) as an example in the South African agricultural context. More 63 specifically, it shows how the most realistic export opportunities involving fruit and nuts, both in 64 existing and in new markets, can be excised from an initially huge and disconnected collection of 65 product, market and economic data, and efficiently rank-ordered for analysis and further 66 investigation. The paper concludes by offering some insights into how South Africa’s 67 Department of Agriculture, Forestry and Fisheries (DAFF) can use the results of the model to 68 mould an export strategy that will encourage more sustainable export results for the agricultural 69 sector. 70

2. The ‘big data’ barrier 71

The challenge for policy makers and exporters alike lies in harnessing and correctly interpreting 72 the huge volumes of product- and market-related information in circulation today, much of 73 which lacks structure and coherence and, moreover, is constantly being revised and embellished. 74 As stated by Lucien Jansen, Chief Executive Officer of the Perishable Products Export Control 75 Board (PPECB), which is aligned to South Africa’s Department of Agriculture, Forestry and 76 Fisheries (DAFF): “With export markets becoming increasingly competitive, information has 77 become crucial in ensuring that South Africa can remain a major player on the global stage. It is 78 all about understanding export trends and supplying markets as per demand” (Department of 79 Agriculture, Forestry and Fisheries 2016: vii). 80

The process of assembling and analysing trade data and extracting value therefrom tends to be 81 limited by the technology and tools at people’s disposal. To provide some perspective: if one 82 were to analyse global trade (imports and exports) on a product-by-country bilateral basis at the 83 6-digit detail Harmonized System (HS) product level straddling a 6-year period, the theoretical 84 matrix of data requiring processing would comprise close to 4.7 billion cells. This excludes 85 various derivative variables and indicators that would be needed to properly inform decision 86 making. In practice, this number would be lower, as not all bilateral trade occurs on all product 87 codes. Yet in some cases, data are captured at the 8- and 10-digit detail level, which further 88 boosts the potential volumes of data. In practice, though, at the 6-digit level it would amount to 89 around 300 million cells (also see Sadovy 2015). 90

There is yet no clear consensus on the meaning of the term ‘big data’. For example, De Mauro, 91 Greco and Grimaldi (2015) define big data as “the information assets characterized by such a 92 high volume, velocity and variety as to require specific technology and analytical methods for its 93 transformation into value”. The Economist Intelligence Unit (2015) asserts that big data is not 94 about volume or velocity, but rather about value, and further states that: “.. we need to reduce the 95 quantities of data and focus on the value-add, not the noise” (The Economist Intelligence Unit 96 2015: 16). A more circumspect opinion is provided by Magoulas and Lorica (2009) who state 97 that what is considered to be ‘big data’ may vary depending on the capabilities of the users and 98 their tools, and that evolving technological capabilities therefore make big data a moving target. 99 In their opinion: “For some organizations, facing hundreds of gigabytes of data for the first time 100 may trigger a need to reconsider data management options. For others, it may take tens or 101 hundreds of terabytes before data size becomes a significant consideration” (Magoulas and 102 Lorica 2009: 02). 103

2

Already, the aggregated annual data pose a challenge, even before the underlying daily 104 transactional data are considered. The focus of this paper is on how to work with the annual trade 105 data (intensive), something that is testing the mettle of decision makers throughout the world – 106 not least of which in South Africa and other African countries. 107

3. Research method 108

The TRADE-Decision Support Model 109 (TRADE-DSM) is the brainchild of the TRADE 110 (an acronym for Trade and Development) 111 research entity at the North-West University, 112 Potchefstroom, South Africa. The methodology 113 was initially developed (see Cuyvers, De 114 Pelsmacker, Rayp and Roozen 1995) to reveal 115 the product-country combinations with the best 116 prospects of export success for a single country, 117 and was primarily aimed at export promotion 118 agencies looking to allocate their (scarce) 119 resources in the most efficient manner. Further 120 refinements were made and additional, more 121 powerful applications added to the TRADE-122 DSM. It is the latest, embellished model that is 123 the focus of this paper. 124

Complementing the TRADE-DSM is the 125 TRADE-DSM NavigatorTM, an interactive 126 computer instrument used to interpret the results 127 of the TRADE-DSM in a user-friendly way. In a 128 nutshell, the methodology, which is summarised 129 in Figure 1 (see Cuyvers, Steenkamp and 130 Viviers 2012), involves evaluating - for a given 131 country/province/industry sector - all worldwide 132 country and product combinations (a seemingly 133 daunting task, considering all the possible 134 permutations) and screening them using various 135 intelligent ‘filters’ to systematically eliminate 136 less promising/viable combinations in an effort 137 to categorise and prioritise realistic export 138 opportunities (REOs) on a grid, known as the 139 REO MapTM. Each of the filters in the TRADE-140 DSM examines different dimensions of the 141 product-country combinations, narrowing down 142 the possibilities after each successive round: 143 144

i) Filter 1: Broad, general market potential as reflected in economic size, growth, and 145 political and commercial risk; 146

ii) Filter 2: Market potential in terms of import growth and import market size; 147

Figure 1. Illustrative overview of the DSM methodology for products Source: Cameron and Viviers (2015), adapted from Jeannet and Hennessey (1988: 139)

3

iii) Filter 3: Market access conditions, including factors such as market concentration (see 148 Hirschmann 1964) and the prevalence of trade barriers; 149

iv) Filter 4: Categorisation of results based on ‘home market’ and ‘target market’ product-150 level trade characteristics. Further refinement can be achieved by applying either the 151 revealed comparative advantage (RCA; see Balassa 1964) or the revealed trade advantage 152 (RTA; see Vollrath 1991) as a production capability proxy, or a combination of the two. 153

The above filtering process serves to pare down large quantities of data into more digestible 154 information, which can then be analysed and used as the basis for strategic and financial 155 planning. In short, it allows data to be distilled into intelligible results, as depicted on the REO 156 MAPTM. Depending on the position of the realistic export opportunities on the REO MAPTM, 157 appropriate strategic export responses can be determined (see Figure 2). 158

For example, for REO1,1 to REO 2,5, 159 the exporting country has a non-existent 160 to low market share, and an offensive 161 market exploration strategy is 162 appropriate in the case of products with 163 an existing or potential comparative 164 advantage. For REO3,1 to REO3,5, the 165 exporting country has a relatively 166 medium-large market share, and an 167 offensive market expansion strategy is 168 recommended. 169

The REO MapTM produces an outcome 170 that makes it possible to evaluate 171 realistic export opportunities and, in 172 turn, inform the nature of the export 173 promotion strategy to be developed 174 based on the specific allocated REOxy 175 category. However, the authors extended 176 the methodology to reflect (as opposed 177 to eliminate) the REOs based on average 178 market potential per opportunity, while 179 the relative (existing) specialisation (or 180 not) of South African exports 181 represented by the RCA is shown in a 182 conceptual framework similar to that of 183 the well-known Boston Consulting 184 Group (BCG) growth-share matrix (also 185 applied by ITC Trade Map), as depicted 186 in Figure 3. 187

The conceptual framework applied is underpinned by the following logic: 188

a) The REOs have already been ‘filtered’ through the DSM methodology, which considers 189 many factors, including market share and growth (as per the BCG approach). Thus, the 190

Figure 2. REO MAPTM Source: Adapted from Cuyvers et al. (2012)

4

intention is to inform decision makers of the additional attributes associated with each 191 opportunity as it passes through the DSM filtering process. 192

b) The authors therefore present the products being evaluated/investigated and their associated 193 opportunities (based on the identified REOs), which are further categorised according to five 194 existing DSM attributes, namely: 195 196 i) export potential (average per opportunity); 197

ii) maturity (as indicated by the RCA); 198 iii) market diversification potential (as indicated by the number of different markets for 199

which the REO indicates an opportunity for a specific product); 200 iv) relative market share (REOs in columns 1 and 2 indicating low market share are 201

associated with ‘new’ markets in Q2 and Q3, while REOs in columns 3 and 4 are 202 associated with ‘existing’ markets for which the exporting country in question already 203 has an intermediately-large to large market share); and 204

v) market growth potential (as indicated by the DSM classification of the market 205 characteristics of these potential markets). 206 207

208

209

210

211

212

213

214

215 216 217 218 219 220 221 222 223 224 225 226 The REOs are therefore plotted on the basis of the above dimensions, as follows: 227

a) X-axis = number of potential markets (diversification); 228 b) Y-axis = relative competitiveness (more or less mature [RCA]); 229 c) Bubble size = market potential per product (aggregated across markets); 230 d) Q2 and Q3 = REOs in columns 1 and 2 indicating low market share, termed ‘new’ markets; 231

and 232

Figure 3. REO export maturity, market share, and growth and diversification conceptual model

Source: Cameron and Viviers (2015)

5

e) Q1 and Q4 = columns 3 and 4 indicating intermediately-large to large market share, termed 233 ‘existing’ markets. 234

235 This places the various REOs in one of the four quadrants, namely: 236

i) Quadrant 1 / ‘Brown fields’: Mature export products2 with growth potential in markets 237 already well-serviced by the exporting country (product-market combinations classified into 238 columns 3 and 4 of the REO MAPTM, depicted in Figure 2). 239

ii) Quadrant 2 / ‘Green (new) pastures’: Mature export products with growth potential in new 240 markets (product-market combinations classified into columns 1 and 2 of the REO MAPTM, 241 depicted in Figure 2). 242

iii) Quadrant 3 / ‘Blue sky’: Less mature export products3 with growth potential in new markets. 243 iv) Quadrant 4 / ‘Grey fields’: Less mature export products with growth potential in markets 244

already well-serviced by the exporting country. 245

Figures 2 and 3 illustrate both the elegance and power of the TRADE-DSM methodology - 246 elegance in that it allows for a quick visual inspection and comparison of high-ranking REOs, 247 and power in that it points to where, with additional investment and/or support, promising export 248 opportunities could become true winners. 249

Analogous to the product focus, the authors extended the methodology based on a market 250 (aggregated over products) perspective to inform the strategic question regarding country or 251 market (as opposed to product) diversification potential. 252

This framework was therefore applied to develop a view of all the potential product-country 253 combinations that the Department is interested in analysing for the purposes of strategic decision 254 making. Such a view informs both a product-centric and market-centric approach, as well as a 255 combination of the two. 256

4. The data 257

The international trade data underlying the TRADE-DSM model pertain to the French 258 international economics research centre (CEPII) BACI world trade database. This database is 259 constructed from the United Nations Statistics Division’s UN Comtrade database and reconciles 260 the data reported by almost 150 countries. The CIF import values and FOB export values 261 reported are reconciled to provide one trade figure for each bilateral trade flow, which excludes 262 CIF costs. Furthermore, the CEPII team assesses the reliability of country reporting and takes 263 these reporting quality weights into consideration when reconciling the bilateral trade flows. The 264 BACI database covers bilateral trade values at the HS 6-digit product disaggregation for more 265 than 200 countries since 1995 and is updated every year (CEPII 2015, HS 2002 revision). The 266 HS 2002 version has the longest time series available. Although a newer version is available on 267 the HS 2007 revision, this information has to date only been consolidated and reported by CEPII 268 for the period 2008-2012. Therefore, the current TRADE-DSM database is based on the CEPII, 269

2 Mature export products are identified as those products with a Revealed Comparative Advantage (RCA) of greater than 1, indicated on the vertical axis in Figure 3. 3 Less mature export products are identified as export products with a Revealed Comparative Advantage (RCA) of less than 1, indicated on the vertical axis in Figure 3.

6

2015 HS 2002 revision, with some refinements being made, where required, on a more detailed 270 product basis as issues are uncovered and addressed. 271

The CEPII data applied in this report contain SACU aggregate data. In practice, South Africa 272 accounts for the majority of transactions. In this version of the results, South Africa still 273 represents SACU, whereas a future update of this information will provide a more refined view 274 of South Africa only. 275

This DSM analysis and outputs therefore make use of data for the period 2009-2013. Although 276 later (2014) data are available from the Statistics Division’s UN Comtrade database and the 277 ITC’s Trade Map, the modelling requirement for reconciled data places a limit on the currency of 278 the data. However, relative fundamental outcomes informed by this approach should not differ 279 significantly from one year to the next. 280

As mentioned in the introduction to this 281 paper, the focus is on HS chapter 8 – 282 edible fruit and nuts; peel of citrus fruit or 283 melons. The global trade in products of 284 Edible fruit and nuts continues to grow 285 and during the period 2009-2013 286 increased from just under US$70 billion to 287 US$100 billion, as shown in Figure 4. The 288 latest corresponding value reported by 289 Trade Map for 2014 is US$112 billion. 290

South Africa averaged around 3.3% of 291 this trade during the period 2009-2013, as 292 indicated in Figure 4. 293

South Africa continues to be a net 294 exporter on an aggregate product group 295 level and continues to have a positive 296 trade balance in terms of the imports and 297 exports of edible fruit and nuts on an 298 aggregated basis. 299

As shown in Figure 5, the CEPII data 300 report South Africa’s exports as US$3.4 301 billion for 2013, while Trade Map reports 302 US$2.6 billion for the same year. 303

The major HS08 product group 304 contributions for South African exports 305 are shown relative to the contributions of 306 these products in a global trade context in 307 Figure 6. 308

Evident is the fact that the relative contributions of three major groups feature more strongly in 309 the South African export make-up than they do in global trade. These are 310

Figure 5. RSA HS08 trade balance

Source: Authors, calculated from CEPII (2015 HS 2002) mirror country trade data

Figure 4. RSA HS08 trade in context of global trade

Source: Authors, calculated from CEPII BACI world trade database

7

• HS0805 – Citrus fruit; 311 • HS0806 – Grapes; and 312 • HS0808 – Apples and pears 313 • 314

315 The current CEPII data (2015 HS 2002 316 revision) do not contain any 317 information on Macadamia nuts 318 explicitly. Under the HS 2002 revision, 319 Macadamia nuts (HS080260) are 320 included under HS080290 Nuts, n.e.s., 321 fresh/dried, whether or not 322 shelled/peeled. The split for 323 Macadamia into HS080261 Macadamia 324 nuts: In shell and HS080262 325 Macadamia nuts: Shelled was only 326 applied from 2012 onwards. 327 328 Based on the historical share as reported 329 by Trade Map, HS080260 Macadamia 330 nuts (on a time-weighted basis during 331 the period 2009-2013) account for 332 18.2% of the total value reported for 333 HS080260, HS080261, HS080262 and 334 HS080290 for global trade. 335

Similarly, for South Africa’s exports to the world, HS080260 Macadamia nuts account for 72.9% 336 of the same codes. In terms of the outcomes obtained, the above two shares can therefore be 337 applied to calculate an approximation for Macadamia nuts (HS080260) from the outcomes for 338 Nuts n.e.s., fresh or dried (HS080290). 339

The global spread of product-market combinations with reported trade (illustrated by relative 340 size of bubbles) is illustrated in Figure 7 and Figure 8. 341

Figure 7. Illustrative map of global potential for HS08 products

Source: TRADE-DSM NavigatorTM

Figure 8. Illustrative map of African potential for HS08 products

Source: TRADE-DSM NavigatorTM

Figure 6. HS04 product group contributions to HS08 – Edible fruit and nuts

Source: Authors, calculated from CEPII (2015 HS 2002) mirror country trade data

8

Although it may appear that some countries on the map (Figure 7) have no potential, this could 342 be due to the relative scale (illustrated in Figure 8 for Africa). The number of countries (markets) 343 that reported trade in the products contained in chapter 08 – Edible fruit and nuts based on the 344 CEPII database (2015) totals 193 (out of 253). Based on product-market combinations with 345 reported trade, there are 8,672 product-market flows reported for the product group (55 346 products). 347

Based on a calculation that considers the average of the six largest suppliers (exporting 348 countries) into each market (excluding exports by South Africa into each market) as a proxy for 349 ‘realistic’ potential that South Africa could aim to achieve, the associated total potential is 350 around US$14.4 billion (2013). 351

5. Results 352

To assist the Department in formulating a longer term strategy, the DSM results across the 55 353 possible products (HS 6-digit) and possible target markets (as shown in Figure 9) provide a 354 higher level aggregated view of which products - based on the DSM methodology - exhibit the 355 most potential in terms of both value and market diversification opportunities. By applying the 356 DSM methodology for the total set of 8,672 possible products and target market combinations, 357 the following outcomes were obtained during the various stages of the process: 358

In total, there are 13 products that exhibit an average potential per opportunity of more than 359 US$10 million. Of these, four appear in Quadrant 1, three in Quadrant 2, three in Quadrant 3 and 360 none in Quadrant 4. This information can assist the Department in introducing a product 361 ‘diversification’ dimension into its export strategy. 362

363

364

365 Figure 9. DSM approach applied for 55 possible products (HS 6-digit), all markets

Source: Authors, TRADE-DSM NavigatorTM

9

366

367

368

The ‘Brown fields’ quadrant (Q1) lists 19 products of the potential 54 products associated with a 369 relative comparative advantage of greater than 1. 370

Keeping in mind that these opportunities have already been ‘filtered‘ through the DSM 371 methodology, the authors initiated a process of informing on various dimensions to further assist 372 the Department in its strategy formulation. 373

The 54 products and their associated opportunities (based on the identified 1,221 REOs) 374 categorised in terms of export potential (average per opportunity), maturity, diversification and 375 market growth matrix’s quadrants are presented in Figure 10. 376

The largest opportunities based on average size above US$10 million (ranked by total potential) 377 identified in the ‘Brown fields’ of Quadrant 1 in Figure 11 are: 378 • HS080510: Oranges, fresh/dried 379 • HS080610: Grapes, fresh 380 • HS080810: Apples, fresh 381 • HS080290: Nuts, n.e.s., fresh/dried, whether or not shelled/peeled (mostly HS080260 382

Macadamia nuts – refer to the product definition section for an explanation) 383

To assist in interpreting the outcomes, the authors used HS080510: Oranges, fresh/dried as an 384 example. 385

Figure 10. Overall realistic export potential, maturity, diversification and market growth matrix

Source: Authors, TRADE-DSM NavigatorTM

10

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

On the horizontal axis (x-axis) it is evident that there are 15 markets associated with the product, 403 which has a relatively high comparative export advantage, as indicated by the vertical axis (y-404 axis). The average size of the group of 15 opportunities is more than US$10 million, but within 405 the group there can obviously be a wide distribution, as illustrated in Figure 12. 406

Figure 11. Quadrant 1 (‘Brown fields’)

Source: Authors, TRADE-DSM NavigatorTM

11

407

408

What is evident from Figure 12 is that within the group of markets that South Africa already 409 supplies HS080510: Oranges, fresh/dried to, the further potential value to be associated with 410 these markets ranges from nearly US$50 million (Russian Federation) down to US$2,000 411 (Malawi). This group of countries represents a total further potential of US$151.4 million (2013 412 values). 413

Another example is HS080540: Grapefruit, fresh/dried – since, although this product is ranked 414 fourth largest in terms of total potential, per opportunity the average potential is only US$2.1 415 million due to the potential being concentrated in a single country (as opposed to more of a 416 spread in the previous example), i.e. the Netherlands. 417

These examples simply serve to illustrate that more detailed investigation is still required within 418 each of the opportunities identified if they are to be properly understood. 419

With reference to Figure 13, the ‘Green (new) pastures’ quadrant (Q2) lists 22 products of the 420 potential 54 products associated with a relative comparative advantage of greater than 1. What is 421 evident is that the largest opportunities based on average size above US$10 million are (ranked 422 according to total potential): 423 • HS080610: Grapes, fresh 424 • HS080810: Apples, fresh 425 • HS080520: Mandarins, incl. tangerines & satsumas; clementines, wilkings & sim. citrus 426

hybrids, fresh/dried 427

It should be noted that HS080550: Lemons (Citrus limon/limonum) & limes (Citrus 428 aurantifolia/latifolia), fresh/dried are close to the US$10 million mark at US$9.876 million. 429

Figure 12. Examples of distribution of potential

Source: Authors, TRADE-DSM NavigatorTM

12

Similar to the earlier Quadrant 1 examples, more detailed investigation is required within each of 430 the opportunities identified in order to understand them better. For the sake of brevity, Quadrants 431 3 and 4 are not included in this discussion. 432

Approximately 80% of the potential is vested in the top 10 products. The remaining 20% of the 433 potential is contained in 12 further products. The question that arises is whether it would be 434 worth investigating the potential of developing these products in the South African production 435 context. The products are: 436 • HS080300: Bananas, incl. plantains, fresh/dried 437 • HS080132: Cashew nuts, shelled 438 • HS081050: Kiwifruit, fresh 439 • HS081190: Fruit & nuts, n.e.s., uncooked/cooked by steaming/boiling in water, frozen, 440

whether or not cont. added sugar/oth. sweetening matter 441

Based on the ranked potential, approximately 80% is represented by seven of the top 10 442 products, two of which are HS080300: Bananas, incl. plantains, fresh/dried and HS080132: 443 Cashew nuts, shelled. The other five are: 444 • HS080610: Grapes, fresh 445 • HS080810: Apples, fresh, HS081090: Fresh fruit, n.e.s. 446 • HS080520: Mandarins, incl. tangerines & satsumas; clementines, wilkings & sim. citrus 447

hybrids, fresh/dried; and 448 • HS080510: Oranges, fresh/dried. 449

450

451

452

453

454

455

456

457

458 459 460 461 462 463 464

465

466

Figure 13. Quadrant 2 (‘Green (new) pastures’)

Source: Authors, TRADE-DSM NavigatorTM 13

467

The analysis has identified a total of 573 ‘new’ opportunities for existing mature South 468 African export products, as indicated in Quadrant 2 (‘Green (new) pastures’). The total 469 estimated market potential of these opportunities exceeds US$3 billion and is represented by 96 470 markets and 22 (of the 54) products (combinations of which have all passed the TRADE-DSM 471 methodology filters for market characteristics (Filter 2), market concentration (Filter 3.1) and 472 market accessibility (Filter 3.2)). 473

As explained in the methodology section, the same approach applied for a product view can be 474 applied for a market view. The 107 countries (based on the identified 1,221 REOs – see Figure 9 475 at the beginning of this section) categorised in the associated weighted basket of export 476 opportunities (average per opportunity), maturity, diversification and market growth matrix’s 477 quadrants are represented in Figure 14. Interestingly, a country such as Nigeria in Western Africa 478 does not pass the market accessibility filter (i.e. Filter 3.2) because of high logistical and other 479 costs as well as high tariffs on these specific products. 480

In this instance, each of the products in which South Africa has a weighted basket of 481 comparative advantages is presented. In total there are eight markets that exhibit an average 482 basket of potential of more than US$10 million per opportunity. Of these, five appear in 483 Quadrant 1, two appear in Quadrant 2 and one appears in Quadrant 4. 484

485

486

487 Figure 14. Overall target country market share, product basket maturity, and growth and diversification matrix

Source: Authors, TRADE-DSM NavigatorTM 14

488

These outcomes can assist the Department in identifying individual markets that can yield a large 489 return across various products in the group. Such information will be useful in the market 490 ‘diversification’ dimension of the strategy. To give the diagram more impact, an example is 491 drawn from each quadrant. 492

The largest markets based on average size above US$10 million are (ranked by total potential): 493 • United States of America (30 products) 494 • Netherlands (37 products) 495 • United Kingdom (34 products) 496 • Russian Federation (23 products) 497 • China (18 products) 498

In a similar fashion to that of the product focus, the outcomes can be interpreted with the help of 499 an example – in this case, Ireland (ranked at number 20 based on total potential in US$ 2013 500 values): 501

HS080610: Grapes, fresh

Figure 15. Examples of distribution of potential and RCAs 502 Source: Authors, TRADE-DSM NavigatorTM 503

On the horizontal axis (x-axis) one sees in Figure 15 that there are five products associated with 504 the market, which has a weighted comparative export advantage for the basket of five greater 505 than 1, as indicated by the vertical (y-axis) axis. The average size of the group of five 506 opportunities is less than US$10 million. The potential as well as RCAs within the group can 507 obviously be distributed widely, as illustrated in Figure 15, but as a ‘bundled’ group this market 508 can be classified as mature from a South African export perspective, and 3/5 products in the 509 basket have RCAs > 1. 510

15

Evident from Figure 15 is that within the group of products for Ireland, South Africa already 511 supplies (HS080610: Grapes, fresh) and dominates the basket from the perspective of potential. 512 South Africa supplies a relatively small share of Ireland’s imports of this product, at around 4%. 513 The largest competition is from the United Kingdom and the Netherlands – both countries which 514 may be importing South African products and on-selling and re-exporting them to Ireland. For 515 HS080540: Grapefruit, fresh/dried, South Africa supplies 18% of Ireland’s imports of this 516 product, and although this represents relatively small potential it does place Ireland in Q1. 517

The further potential value to be associated with these products ranges from nearly US$4 million 518 (HS080610: Grapes, fresh) down to US$427,000 (HS080540: Grapefruit, fresh/dried). The group 519 of products in aggregate represents a total further potential of US$7.7 million (2013 values). 520

Quadrant 2 in Figure 16 shows countries to which South Africa has supplied nothing to 521 intermediately-small shares of their imports. There are two countries (Germany and Canada) that 522 exhibit potential for their respective baskets of products of, on average, more than US$10 523 million. Germany demonstrates potential for a total of 40 products, while Canada demonstrates 524 potential for 21 products. 525

526

527

Figure 16. Country or market view – Quadrant 2 (‘Green (new) pastures’) 528 Source: Authors, TRADE-DSM NavigatorTM 529

16

Other countries worth mentioning that have high potential, but are just below the US$10 million 530 cut-off, are France (27 products), Thailand (only 3 products), Belgium-Luxembourg (33 531 products) and the Ukraine (14 products). 532

For the sake of brevity, Quadrants 1, 3 and 4 are not included in this discussion, but they follow a 533 similar logic. 534

When all markets are evaluated on the basis of overall potential, the top 10 markets and their 535 associated baskets of products represent nearly 75% of total market potential. 536

Although Germany offers the second highest potential, the number of products in which it 537 exhibits potential is 40 out of the possible 55 products, followed by the Netherlands with 37 538 (third in terms of potential). All top 10 markets are classified either into Q1 (‘Brown fields’) or 539 Q2 (‘Green (new) pastures’) based on their respective product baskets. 540

This analysis has identified a total of 124 ‘new’ opportunities for existing mature South African 541 products, as indicated by Q2 (‘Green (new) pastures’) for the top 10 markets. These are 542 potentially key product-market combinations to pursue from a market expansion and deepening 543 perspective. 544

When combining the top 10 markets with potential under-utilised or -penetrated products for 545 these markets, additional ‘new’ markets within the top 10 for existing relatively ‘mature’ 546 products from a South African export perspective can be obtained. Only four markets are 547 classified as ‘new’, the other six being mature markets - although not for this particular sub-set 548 of products. These ‘new’ markets are: 549 • Germany 550 • France 551 • Belgium-Luxembourg 552 • Canada 553

6. Key observations and conclusions 554

This paper set out to show how an innovative approach to collecting and processing highly 555 variable and voluminous data (within the ambit of so-called ‘big data’) can streamline the 556 product-market selection process in an international agricultural development or expansion drive. 557

For countries like South Africa, which are under enormous pressure (in the face of many local 558 and international challenges) to expand their export capacity and reach, the TRADE-DSM is an 559 invaluable tool – particularly as it has different advantages and uses for different categories of 560 client, and users do not need to be particularly techno-savvy. As a result, they are able to invest 561 the bulk of their time in developing (rather than exhaustively looking for) markets. 562

The Decision Support Model (DSM), which is at the heart of the North-West University’s 563 TRADE research entity’s research programme, is used to identify realistic export opportunities 564 for countries, provinces and industry sectors in the form of high-potential product-market 565 combinations. Complementing the DSM is the TRADE-DSM NavigatorTM, an interactive 566 computer instrument used to present the results of the DSM in a user-friendly format. 567

17

The DSM uses a 4-stage sequential filtering process, which eliminates less promising export 568 opportunities and ultimately reveals those country-product combinations that show the most 569 realistic, relative export opportunities (REOs). This methodology considers demonstrated market 570 (import) demand and is based on historical information (currently no forecasting/projections). 571 Furthermore, it considers production-related components or constraints by proxy, but the main 572 focus is on potential demonstrated international demand. 573

In this study the authors categorised the product and market combinations into four descriptive 574 quadrants (‘Brown fields’, ‘Green (new) pastures’, ‘Blue sky’ and ‘Grey fields’), thus allowing a 575 quick visual inspection of the implications of five different REO-related 576 dimensions/characteristics for different product-market combinations. 577

The authors also demonstrated how the practical application of the TRADE-DSM methodology 578 uncovers realistic export opportunities for South Africa as a whole in respect of fruit and nuts 579 (HS chapter 08). In this regard, some of the key findings are: 580 • An initial 1,221 REOs were identified in 107 markets. 581 • Of the overall 54 products in the fruit and nuts category, 22 have ‘major potential’, 582

representing about US$3.5 billion across 102 countries. 583 • Most of the potential for ‘mature’ products lies in ‘new’ markets (i.e. Quadrant 2 / ‘Green 584

(new) pastures’). 80% of this potential is found in 10 products (including grapes, apples, 585 mandarins, and lemons and limes). 586

• The potential for ‘new’ and ‘niche’ products lies in ‘new’ markets (i.e. Quadrant 3 / ‘Blue 587 sky’). 80% of this potential is found in 11 products (including bananas, cashew nuts, 588 kiwifruit and guavas). 589

• Europe still represents approximately half of the total estimated realistic potential in the short 590 term, estimated at US$6 billion, followed by North America (22%) and Asia (21%). 591

The cross-cutting nature of the findings makes for an interesting analysis and lends itself to a 592 range of deductions which, in turn, help to inform strategic decisions. 593

However, a cautionary stance needs to be adopted when interpreting the results. This is because 594 results may be influenced (favourably or otherwise) by logistical flows from foreign ports in 595 Europe, Asia and elsewhere to the final destinations, which are often in nearby countries. The 596 reported data cover products passing through the transit markets as well, thus influencing the 597 model’s outcomes. Industry therefore needs to investigate and understand the impact of these 598 logistics flows before drawing final conclusions and taking its planning to the next level. 599

At a practical level, the outcomes of this study constitute an important foundation for the 600 development of a fruit industry (export development) strategy by the Department of Agriculture, 601 Forestry and Fisheries in South Africa, and also offers a glimpse of what can be achieved when 602 the DSM takes centre stage in an industry’s quest to conquer the market selection challenge. 603

604

605

606

607 608

18

609

610

611

7. References 612

Balassa, B. 1964. The purchasing power parity doctrine – A reappraisal. Journal of Political 613 Economy 72: 584-596. 614

Cameron, M.J. and W. Viviers. 2015. Realistic Export Opportunity Analysis for Agricultural 615 Products in the Major Group: HS08 - Edible fruit and nuts; peel of citrus fruit or melons. Study 616 report prepared by TRADE (Trade and Development) research focus area, North-West 617 University, Potchefstroom Campus, for Department of Agriculture, Forestry and Fisheries, South 618 Africa. 619

CEPII. 2015. BACI. 620 http://www.cepii.fr/CEPII/en/bdd_modele/download.asp?id=1#sthash.jCRBRqXk.dpuf 621 [accessed November 25, 2013]. 622

Cuyvers, L., P. de Pelsmacker, G. Rayp and I.T.M. Roozen. 1995. A decision support model for 623 the planning and assessment of export promotion activities by government export promotion 624 institutions: the Belgian case. International Journal of Research in Marketing 12 (2): 173-186. 625

Cuyvers, L., P. de Pelsmacker, G. Rayp and I.T.M. Roozen. 1995. A decision support model for 626 the planning and assessment of export promotion activities by government export promotion 627 institutions: the Belgian case. International Journal of Research in Marketing 12 (2): 173-186. 628

Cuyvers, L., E.A. Steenkamp and W. Viviers. 2012. The methodology of the Decision Support 629 Model (DSM). In Export Promotion: A Decision Support Model Approach, edited by L. Cuyvers 630 and W. Viviers. Stellenbosch: Sun Media Metro. 631

Department of Agriculture, Forestry and Fisheries. 2016. Food Trade SA. Department of 632 Agriculture, Forestry and Fisheries, Directorate International Trade, Private Bag X250, Pretoria 633 0001, South Africa. ISBN 978-1-86871-427-8. 634

De Mauro, A., M. Greco and M. Grimaldi. 2015. What is big data? A consensual definition and a 635 review of key research topics. International Conference on Integrated Information (IC-ININFO 636 2014), American Institute of Physics (AIP) Conference Proceedings 1644: 97–104. 637 doi:10.1063/1.4907823. 2015 AIP Publishing LLC 978-0-7354-1283-5. 638

Hirschmann, A. 1964. The paternity of an index. American Economic Review 54, September, 639 761. 640

Jeannet, J.P. and H.D. Hennessey. 1998. International marketing management: strategies and 641 cases. Boston: Houghton Mifflin. 642

ITC Trade Map. 2015. 643 http://www.trademap.org. [accessed November, 2015]. 644

19

Magoulas, R. and B. Lorica. 2009. Introduction to Big Data. Release 2.0. Issue 11, February 645 2009. O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. ISSN 646 1935-9446. 647 http://r2.oreilly.com [accessed February, 2016]. 648

649

Sadovy, L. 2015. The New Map of Global Manufacturing. 650 http://www.allanalytics.com/author.asp?doc_id=277478&_mc=sem_otb_edt_allan [accessed 651 February 4, 2015]. 652

The Economist Intelligence Unit. 2015. Big data evolution: Forging new corporate capabilities 653 for the long term. London, 20 Cabot Square, London, E14 4QW, United Kingdom. 654 http://www.economistinsights.com/technology-innovation/analysis/big-data-evolution [accessed 655 February, 2016]. 656

Vollrath, T. 1991. A theoretical evaluation of alternative trade intensity measures of revealed 657 comparative advantage. Weltwirtschaftliches Archiv 127, 265-280. 658

20