Venezuela agribusiness report q3 2014

Q3 2014 www.businessmonitor.com VENEZUELA AGRIBUSINESS REPORT INCLUDES 5-YEAR FORECASTS TO 2018 ISSN 2040-0497 Published by:Business Monitor International Venezuela Agribusiness Report Q3 2014 INCLUDES 5-YEAR FORECASTS TO 2018 Part of BMI’s Industry Report & Forecasts Series Published by: Business Monitor International Copy deadline: June 2014 Business Monitor International Senator House 85 Queen Victoria Street London EC4V 4AB United Kingdom Tel: +44 (0) 20 7248 0468 Fax: +44 (0) 20 7248 0467 Email: subs@businessmonitor.com Web: http://www.businessmonitor.com © 2014 Business Monitor International All rights reserved All information contained in this publication is copyrighted in the name of Business Monitor International, and as such no part of this publication may be reproduced, repackaged, redistributed, resold in whole or in any part, or used in any form or by any means graphic, electronic or mechanical, including photocopying, recording, taping, or by information storage or retrieval, or by any other means, without the express written consent of the publisher DISCLAIMER All information contained in this publication has been researched and compiled from sources believed to be accurate and reliable at the time of publishing However, in view of the natural scope for human and/or mechanical error, either at source or during production, Business Monitor International accepts no liability whatsoever for any loss or damage resulting from errors, inaccuracies or omissions affecting any part of the publication All information is provided without warranty, and Business Monitor International makes no representation of warranty of any kind as to the accuracy or completeness of any information hereto contained Venezuela Agribusiness Report Q3 2014 CONTENTS BMI Industry View SWOT 10 Agribusiness 10 Business Environment 12 Industry Forecast 13 Livestock Outlook 13 Table: Beef Production & Consumption (Venezuela 2013-2018) 15 Table: Pork Production & Consumption (Venezuela 2013-2018) 15 Table: Poultry Production & Consumption (Venezuela 2013-2018) 15 Table: Beef Production & Consumption (Venezuela 2008-2013) 17 Table: Pork Production & Consumption (Venezuela 2008-2013) 18 Table: Poultry Production & Consumption (Venezuela 2008-2013) 18 Grains Outlook 19 Table: Corn Production & Consumption (Venezuela 2013-2018) 21 Table: Wheat Production & Consumption (Venezuela 2013-2018) 21 Table: Corn Production & Consumption (Venezuela 2008-2013) 27 Table: Wheat Production & Consumption (Venezuela 2008-2013) 28 Coffee Outlook 29 Table: Coffee Production & Consumption (Venezuela 2013-2018) 30 Table: Coffee Production & Consumption (Venezuela 2008-2013) 34 Commodity Strategy 35 Monthly Grains Update 35 Table: Selected Commodities - Performance & Forecasts 44 Monthly Softs Update 45 Table: Select Commodities - Performance & BMI Forecasts 54 Upstream Analysis 55 Americas Machinery Outlook 55 Table: Deere & Company - Agriculture & Turf Sales Growth Forecasts By Region 56 Americas GM Outlook 62 Americas Fertiliser Outlook 66 Downstream Analysis 72 Food 72 Food Consumption 72 Table: Food Consumption Indicators - Historical Data & Forecasts, 2011-2018 73 Canned And Prepared Food 73 Table: Canned Food Volume/Value Sales - Historical Data & Forecasts, 2011-2018 74 Fish 74 Table: Fish Volume Sales, Production & Trade - Historical Data & Forecasts, 2011-2018 74 © Business Monitor International Page Venezuela Agribusiness Report Q3 2014 Oils & Fats 75 Table: Oils & Fats Volume Sales, Production & Trade - Historical Data & Forecasts, 2011-2018 76 Confectionery 77 Table: Confectionery Value/Volume Sales - Historical Data & Forecasts, 2011-2018 78 Drink 79 Alcoholic Drinks 79 Table: Alcoholic Drinks Volume/Value Sales - Historical Data & Forecasts, 2011-2018 79 Soft Drinks 80 Table: Soft Drinks Value Sales - Historical Data & Forecasts, 2011-2018 81 Hot Drinks 82 Table: Hot Drinks Value Sales - Historical Data & Forecasts, 2011-2018 83 Mass Grocery Retail 84 Table: Mass Grocery Retail Sales By Format - Historical Data & Forecasts, 2011-2018 84 Table: Sales Breakdown By Retail Format Type 85 Regional Overview 86 Competitive Landscape 92 Table: Venezuela Agribusiness Competitive Landscape 92 Demographic Forecast 93 Table: Venezuela's Population By Age Group, 1990-2020 ('000) 94 Table: Venezuela's Population By Age Group, 1990-2020 (% of total) 95 Table: Venezuela's Key Population Ratios, 1990-2020 96 Table: Venezuela's Rural And Urban Population, 1990-2020 96 Methodology 97 Industry Forecast Methodology 97 Sector-Specific Methodology 98 © Business Monitor International Page Venezuela Agribusiness Report Q3 2014 BMI Industry View BMI View: Venezuela is on track to record another lacklustre year for agricultural production in 2014, as output price restrictions and soaring input prices are keeping a lid on investment in crops and machinery Nicolás Maduro and ruling Partido Socialista Unido de Venezuela made the choice of continuity in terms of interventionist policies, which will maintain a challenging environment for agricultural and food production Price fixing in particular continues to be a source of woe for producers unable to meet input costs which are soaring in line with some of the highest rates of inflation in the world Meanwhile, consumption remains relatively robust causing shortages of some imported goods Agribusiness Market Value BMI Market Value By Commodity (% of Total) (2010-2018) 60 40 20 -20 2010 2011 2012 2013e 2014f Cocoa market value, % of total Sugar market value, % of total Palm Oil market value, % of total 2015f 2016f 2017f 2018f Livestock market value, % of total Cotton market value, % of total Grains market value, % of total e/f= BMI estimate/forecast, Source: FAO, BMI © Business Monitor International Page Venezuela Agribusiness Report Q3 2014 Key Forecasts ■ Corn consumption growth to 2018: 17.4% to 4.28mn tonnes Consumers hit by economic turmoil will turn to the cheapest staple food, more than compensating for reduced demand from the livestock sector ■ Coffee production growth to 2017/18: -6.9% to 680,000 60kg bags The outlook for coffee production in Venezuela remains dire, as hurdles to output expansion will remain in place over the coming years ■ Beef production growth to 2017/18: 3.5% to 367,500 tonnes High input costs, cheaper Mercosur competitors and reduced domestic demand will stymie growth ■ BMI universe agribusiness market value: USD3.72bn in 2014 (up 0.9% from 2013; forecast to grow annually by 2.8% on average from 2014 to 2018) ■ 2014 real GDP growth: 0.5% (down from 1.3% in 2013; forecast to grow annually by 2.2% on average between 2014 and 2018) ■ 2014 consumer price index: 51.2% year-on-year (y-o-y) (up from 44.5% in 2013; forecast to grow annually by 31.4% on average between 2014 and 2018) Industry Developments Venezuela's agricultural industry has been held back over the past decade by poorly executed control policies and limited agricultural inputs The country's socialist government has for some time pegged its procurement price for corn below the cost of production, which has resulted in declining output for several years The Venezuelan bolivar's weakness and limited loan availability in the country has also meant that there is limited usage of agricultural inputs Indeed, 2012/13 corn yields were at the same level as in 2004/05 As a result, we forecast stagnant growth for corn production in the coming years, following a temporary increase in output in 2013/14 Venezuela's largest private company Empresas Polar halted for several weeks in April-May 2014 its production of pasta over delays in foreign currency allocations from the government This highlights the difficulties in the wheat milling industry, plagued by poor planning and by the lack of timely foreign currency availability The temporary closure of the factory and overall issues regarding pasta production poses downside risks to our wheat consumption estimate for 2014 and 2015 Venezuela is the second largest consumer of pasta behind Italy on a per capita basis according to the International Pasta Association Coffee will also be hurt by the ongoing challenging environment in the agribusiness sector We forecast production to decrease in 2013/14 and 2014/15 as elevated operating costs, lack of input supplies and price regulations significantly discourage farmers from grinding coffee of a higher quality The outbreak of the coffee rust disease in Central and South America in 2013 is also still hurting Venezuela's coffee crop The © Business Monitor International Page Venezuela Agribusiness Report Q3 2014 rust disease is unlikely to disappear in 2014/15, given the ongoing shortages of agrichemicals and other products to treat it The outlook for coffee production in Venezuela remains dire, as hurdles to output expansion will remain in place over the coming years Growth in Venezuela will continue to be constrained in 2014, as soaring inflation impacts household purchasing power, oil production stagnates, and private sector investment remains wary of a hostile operating environment We are forecasting real GDP growth of 0.5% in 2014, down from 1.3% estimated for 2013 © Business Monitor International Page Venezuela Agribusiness Report Q3 2014 SWOT Agribusiness SWOT Analysis Strengths ■ Venezuela's tropical climate enables production of a diverse range of agricultural products ■ Venezuelan cocoa are known for their high quality Cocoa especially is sought after by producers of premium chocolate Weaknesses ■ Despite having large areas of fertile arable land, lack of investment in agriculture has left Venezuela a major food importer ■ High food price inflation and frequent supply shortages have dampened growth in food consumption ■ Price controls in place since 2003 squeeze the profits of producers and are a disincentive to investing in increasing production Opportunities ■ The government has shown interest in revitalising coffee and cocoa production after years of decline ■ The government has introduced a number of programmes, including financing and subsidies, to help smallholders increase production ■ Falling oil revenue is bringing more attention to increasing agricultural production to reduce the cost of food imports © Business Monitor International Page 10 Venezuela Agribusiness Report Q3 2014 Regional Overview In this quarter's regional overview for the Americas region, we discuss the increasing use of big data in agriculture; our initial thoughts regarding the Canada-EU free trade agreement; North American infrastructure challenges; global food price inflation; and the expected outperformance of US and EU dairy companies Big Potential For Big Data, But Questions Remain As part of a trend known as 'precision agriculture', mountains of data can now be collected and processed from agriculture fields, and companies are working on technology so data can be analysed to maximise yields Precision agriculture, which includes 'smart' tractors, highly localised weather forecasts and advanced insurance policies, looks set to expand as a key feature for agricultural input companies Firms are looking to tap into so-called 'big data' as a means to improve revenues via the sale of higher value-added (and thus more expensive) goods and services, while shielding themselves from the effects of lower grain prices However, we expect challenges along the way, as some ventures will take a while to show progress, and falling farm incomes and tightening credit conditions in several markets threaten to reduce farmer incentives to purchase more expensive products, especially machinery (see 'Big Potential For Big Data, But Questions Remain,' November 2013) © Business Monitor International Page 86 Venezuela Agribusiness Report Q3 2014 Key Players Weathering The Storm Share Prices Of Select Companies (USD) & S&P GSCI Grains Index Source: Bloomberg, BMI Transport Network Underinvestment Weighing On Export Prospects Although there have not been severe disruptions to grain transportation in North America as has been seen in South America, we caution that suboptimal infrastructure investment over the past decade will make the US and Canada less reliable grain exporters than they have traditionally been In both the US and Canada, investment in grain elevators has not kept up with increases in production capacity; this could create temporary bottlenecks in times when crop volumes outweigh capacity We believe the US has ramped up investment in its railway and port infrastructure, but its elevator and barge networks have declined in terms of efficiency and competitiveness in recent years For Canada, investment in grain transportation has been below infrastructure upkeep levels for decades, but the impending end of the monopoly of the Canadian Wheat Board is likely to increase competition and efficiency in the sector (see 'Transport Network Underinvestment Looming Over Export Prospects', November 2013) © Business Monitor International Page 87 Venezuela Agribusiness Report Q3 2014 Capacity Reached LHC: Canada Elevators & Delivery Points (units); RHC: US Grain Stocks, Production & Storage In Select States (bn bushels) Note: Dates on horizontal axis reflect average over the period; Source: Government Of Canada, Quorum Corporation, USDA Canada-EU FTA: Initial Thoughts We believe the Canadian beef and pork industries will gain the most from the signing of the EU-Canada Free Trade Agreement in October 2013 The EU dairy industry will also benefit from increased market access, though Canada will remain a marginal market over the medium term EU producers are likely to be only slightly negatively affected by the increase in beef imports from Canada, as we expect them to regain competitiveness in the near term In contrast, EU pork producers are expected to become less competitive (see 'Canada-EU FTA: Initial Thoughts', October 25 2013) © Business Monitor International Page 88 Venezuela Agribusiness Report Q3 2014 Little To Europe Canada - Beef Exports By Country In H113 (% of global) Source: USDA Global Food Price Inflation: Where Are The Risks? We continue to expect CBOT grain prices to generally average lower in 2014 than 2013 as supply improves and demand growth remains relatively subdued However, we highlight some key flashpoints that could drive food prices higher in the coming months In particular, we see upside risks to prices from the prospect of stronger ethanol production, the possible return of El Niño, improved livestock production and trade disruptions The effect of these issues could be exacerbated by speculative sentiment, which has been rebounding and shows room for further upside © Business Monitor International Page 89 Venezuela Agribusiness Report Q3 2014 A Return To 2010 Unlikely S&P GSCI Grains Index & % chg y-o-y Note: After March 2014, prices are assumed to be constant spot price for illustrative purposes Source: BMI, Bloomberg Turning More Positive On US & EU Dairy Companies Dairy companies have suffered from increases in farmgate milk prices in recent months In light of this, we have turned more positive on these companies, as we expect moderations in raw milk prices while dairy product prices (and demand) remain strong In addition, consolidation in the industry and gains in efficiency, in a context of positive changes to large national subsidy programmes, will help processor margins climb higher We believe the least diversified companies, such as Dairy Crest and Dean Foods, will perform the best in that context, as their margins have bottomed and valuations are relatively cheap © Business Monitor International Page 90 Venezuela Agribusiness Report Q3 2014 Big Differentials Selected Dairy Companies - Operating Margins (%) Note: As of February 14 2014, Dairy Crest and Saputo have already announced 2013 results, while the others have not Source: Bloomberg © Business Monitor International Page 91 Venezuela Agribusiness Report Q3 2014 Competitive Landscape Table: Venezuela Agribusiness Competitive Landscape Fiscal Y/E Market Capitalisation (USDmn) Employees 672.2 08/2011 122.2 - Dairy 120.4 09/2011 28.9 - Dairy 804.9 12/2013 83.6 - Sub-Sector Revenue (USDmn) Feed & Livestock Productos efe sa Empresas la polar sa Company Proagro ca Sources: BMI, Bloomberg © Business Monitor International Page 92 Venezuela Agribusiness Report Q3 2014 Demographic Forecast Demographic analysis is a key pillar of BMI's macroeconomic and industry forecasting model Not only is the total population of a country a key variable in consumer demand, but an understanding of the demographic profile is key to understanding issues ranging from future population trends to productivity growth and government spending requirements The accompanying charts detail Venezuela's population pyramid for 2013, the change in the structure of the population between 2013 and 2050 and the total population between 1990 and 2050, as well as life expectancy The tables show key datapoints from all of these charts, in addition to important metrics including the dependency ratio and the urban/rural split Population Pyramid 2013 (LHS) And 2013 Versus 2050 (RHS) Source: World Bank, UN, BMI © Business Monitor International Page 93 Venezuela Agribusiness Report Q3 2014 Population Indicators Population (mn, LHS) And Life Expectancy (years, RHS), 1990-2050 Source: World Bank, UN, BMI Table: Venezuela's Population By Age Group, 1990-2020 ('000) 1990 1995 2000 2005 2010 2013e 2015f 2020f 19,741 22,092 24,408 26,726 29,043 30,405 31,293 33,417 0-4 years 2,726 2,747 2,782 2,863 2,934 2,954 2,957 2,931 5-9 years 2,497 2,716 2,737 2,773 2,855 2,903 2,927 2,951 10-14 years 2,282 2,494 2,713 2,735 2,771 2,818 2,853 2,925 15-19 years 1,972 2,276 2,487 2,705 2,728 2,744 2,766 2,848 20-24 years 1,875 1,965 2,264 2,472 2,691 2,715 2,714 2,753 25-29 years 1,729 1,867 1,953 2,249 2,458 2,603 2,674 2,699 30-34 years 1,429 1,719 1,855 1,940 2,234 2,363 2,442 2,659 35-39 years 1,234 1,417 1,704 1,838 1,924 2,094 2,217 2,424 40-44 years 1,006 1,218 1,400 1,684 1,818 1,853 1,904 2,195 45-49 years 764 987 1,197 1,376 1,657 1,754 1,791 1,877 50-54 years 589 742 961 1,167 1,343 1,514 1,620 1,753 55-59 years 493 563 712 924 1,124 1,219 1,297 1,567 60-64 years 409 460 528 671 873 993 1,066 1,234 65-69 years 292 368 418 482 615 726 804 987 70-74 years 204 250 318 363 423 487 543 714 Total © Business Monitor International Page 94 Venezuela Agribusiness Report Q3 2014 Venezuela's Population By Age Group, 1990-2020 ('000) - Continued 1990 1995 2000 2005 2010 2013e 2015f 2020f 75-79 years 131 161 200 258 298 325 350 453 80-84 years 73 89 112 141 185 204 216 257 85-89 years 27 39 48 62 80 96 107 127 90-94 years 11 16 20 26 31 35 48 95-99 years 11 100+ years 0 1 1 e/f = BMI estimate/forecast Source: World Bank, UN, BMI Table: Venezuela's Population By Age Group, 1990-2020 (% of total) 1990 1995 2000 2005 2010 2013e 2015f 2020f 0-4 years 13.81 12.43 11.40 10.71 10.10 9.72 9.45 8.77 5-9 years 12.65 12.29 11.22 10.38 9.83 9.55 9.35 8.83 10-14 years 11.56 11.29 11.11 10.23 9.54 9.27 9.12 8.75 15-19 years 9.99 10.30 10.19 10.12 9.39 9.02 8.84 8.52 20-24 years 9.50 8.89 9.27 9.25 9.26 8.93 8.67 8.24 25-29 years 8.76 8.45 8.00 8.41 8.46 8.56 8.55 8.08 30-34 years 7.24 7.78 7.60 7.26 7.69 7.77 7.81 7.96 35-39 years 6.25 6.41 6.98 6.88 6.63 6.89 7.08 7.25 40-44 years 5.10 5.52 5.74 6.30 6.26 6.10 6.08 6.57 45-49 years 3.87 4.47 4.90 5.15 5.71 5.77 5.72 5.62 50-54 years 2.98 3.36 3.94 4.36 4.63 4.98 5.18 5.25 55-59 years 2.50 2.55 2.92 3.46 3.87 4.01 4.14 4.69 60-64 years 2.07 2.08 2.17 2.51 3.01 3.27 3.41 3.69 65-69 years 1.48 1.67 1.71 1.80 2.12 2.39 2.57 2.95 70-74 years 1.03 1.13 1.30 1.36 1.46 1.60 1.73 2.14 75-79 years 0.66 0.73 0.82 0.96 1.03 1.07 1.12 1.36 80-84 years 0.37 0.40 0.46 0.53 0.64 0.67 0.69 0.77 85-89 years 0.14 0.17 0.20 0.23 0.28 0.32 0.34 0.38 90-94 years 0.03 0.05 0.06 0.07 0.09 0.10 0.11 0.14 95-99 years 0.01 0.01 0.01 0.02 0.02 0.02 0.03 0.03 © Business Monitor International Page 95 Venezuela Agribusiness Report Q3 2014 Venezuela's Population By Age Group, 1990-2020 (% of total) - Continued 100+ years 1990 1995 2000 2005 2010 2013e 2015f 2020f 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 e/f = BMI estimate/forecast Source: World Bank, UN, BMI Table: Venezuela's Key Population Ratios, 1990-2020 1990 1995 2000 2005 2010 2013e 71.7 67.2 62.1 57.0 54.1 8,241 8,876 9,347 58.3 59.8 61.7 Dependent ratio, % of total working age Dependent population, total, '000 Active population, % of total Active population, total, '000 53.2 2015f 2020f 52.7 51.8 9,702 10,194 10,553 10,802 11,407 63.7 64.9 65.3 65.5 65.9 11,500 13,216 15,061 17,024 18,849 19,853 20,491 22,010 Youth population, % of total working age 65.3 60.2 54.7 49.2 45.4 43.7 42.6 40.0 7,505 7,956 8,232 8,371 8,560 8,674 8,737 8,808 Pensionable population, % of total working age 6.4 7.0 7.4 7.8 8.7 9.5 10.1 11.8 Pensionable population, total, '000 736 920 1,115 1,330 1,634 1,878 2,065 2,600 Youth population, total, '000 e/f = BMI estimate/forecast Source: World Bank, UN, BMI Table: Venezuela's Rural And Urban Population, 1990-2020 1990 1995 2000 2005 2010 2013e 2015f 2020f Urban population, % of total 84.3 87.3 89.9 91.9 93.3 93.9 94.3 94.9 Rural population, % of total 15.7 12.7 10.1 8.1 6.7 6.1 5.7 5.1 Urban population, total, '000 16,638 19,291 21,940 24,564 27,101 28,546 29,499 31,711 Rural population, total, '000 3,102 2,801 2,468 2,162 1,942 1,859 1,794 1,706 e/f = BMI estimate/forecast Source: World Bank, UN, BMI © Business Monitor International Page 96 Venezuela Agribusiness Report Q3 2014 Methodology Industry Forecast Methodology BMI's industry forecasts are generated using the best-practice techniques of time-series modelling and causal/econometric modelling The precise form of model we use varies from industry to industry, in each case being determined, as per standard practice, by the prevailing features of the industry data being examined Common to our analysis of every industry is the use of vector autoregressions Vector autoregressions allow us to forecast a variable using more than the variable's own history as explanatory information For example, when forecasting oil prices, we can include information about oil consumption, supply and capacity When forecasting for some of our industry sub-component variables, however, using a variable's own history is often the most desirable method of analysis Such single-variable analysis is called univariate modelling We use the most common and versatile form of univariate models: the autoregressive moving average model (ARMA) In some cases, ARMA techniques are inappropriate because there is insufficient historic data or data quality is poor In such cases, we use either traditional decomposition methods or smoothing methods as a basis for analysis and forecasting BMI mainly uses ordinary least squares estimators In order to avoid relying on subjective views and encourage the use of objective views, we use a 'general-to-specific' method BMI mainly uses a linear model, but simple non-linear models, such as the log-linear model, are used when necessary During periods of 'industry shock', for example, if poor weather conditions impede agricultural output, dummy variables are used to determine the level of impact Effective forecasting depends on appropriately selected regression models We select the best model according to various different criteria and tests, including but not exclusive to: ■ R2 tests explanatory power; adjusted R2 takes degree of freedom into account; ■ Testing the directional movement and magnitude of coefficients; ■ Hypothesis testing to ensure coefficients are significant (normally t-test and/or P-value); ■ All results are assessed to alleviate issues related to auto-correlation and multicollinearity; © Business Monitor International Page 97 Venezuela Agribusiness Report Q3 2014 Human intervention plays a necessary and desirable role in all or our industry forecasting Experience, expertise and knowledge of industry data and trends ensure analysts spot structural breaks, anomalous data, turning points and seasonal features where a purely mechanical forecasting process would not Sector-Specific Methodology Within the Agribusiness industry, issues that might result in human intervention could include but are not exclusive to: ■ Technology development that might influence future output levels (for example greater use of biotechnology); ■ Dramatic changes in local production levels due to public or private sector investment; ■ The regulatory environment and specific areas of legislation, such as import and export tariffs and farm subsidies; ■ Changes in lifestyles and general societal trends; ■ The formation of bilateral and multilateral trading agreements, and political factors The following two examples show the demand (consumption) and the supply (production) of rice Note that the explanatory variables for both are quite similar, but the underlying economic theory is different Example Of Rice Consumption Model (Rice consumption)t = β0 + β1*(real private consumption per capita)t + β2*(inflation)t + β3*(real lending rate)t + β4*(population)t + β5*(government expenditure)t + β6*(food consumption)t-1 + εt Where: ■ β are parameters for this function ■ Real private consumption per capita has a positive relationship with rice consumption, if rice is a normal good in a particular country If rice is an inferior good in a country, the relationship is negative So the sign of β1 is determined by a specific product within a specific country ■ When inflation is high, people with rational expectations will consume today rather than wait for tomorrow's high price to come Higher rice demand in year t due to higher inflation in that year leads to an assumed positive sign of β2 ■ The relationship between real lending rate and rice consumption is expected to be negative When real lending rates increase, disposable incomes, especially for those with mortgage burdens, etc, will decrease So the sign of β3 is expected to be negative ■ Of course, other things being equal, growth in rice consumption can also be caused by growth in population Consequently, positive sign of β4 is expected © Business Monitor International Page 98 Venezuela Agribusiness Report Q3 2014 ■ ■ ■ Government expenditure typically causes total disposable incomes to rise So the sign of β5 is expected to be positive Human behaviour has a trend: A high level of food consumption in previous years means there is very likely to be a high level of food consumption the next year So the positive sign of β6 is expected ε is the error/residual term Example Of Rice Production Model (Rice production)t = β0 + β1*(real GDP per capita)t + β2*(inflation)t + β3*(real lending rate)t + β4*(rural population)t + β5*(government expenditure)t + β6*(food production)t-1 + εt Where: ■ The same as above: the relationship between real GDP per capita and rice production depends on whether rice is normal or inferior good in that country ■ If high inflation is caused by food prices increasing, farmers will be more profitable Then they will supply more agricultural product (eg rice) to increase their marginal (extra) profit, although this is tempered by the rising cost of other inputs in line with inflation ■ There is a global move towards corporate farming, away from small holdings, in order to achieve greater agricultural productivity Corporate farming means more investment in the modes of production, ie agricultural machinery Higher real lending rates discourage investment, which in turn reduce production ■ BMI assumes that only the rural population has a positive effect on agricultural product supply ■ With supportive government policy, other things being equal, rice production is expected to go up Government expenditure is likely to play some role in supporting agribusiness ■ Again, previous food production positively affects this year's prediction © Business Monitor International Page 99 Reproduced with permission of the copyright owner Further reproduction prohibited without permission [...]... for 2014 and 2015 Venezuela is the second largest consumer of pasta behind Italy on a per capita basis according to the International Pasta Association © Business Monitor International Page 23 Venezuela Agribusiness Report Q3 2014 Increasing Dependence Venezuela - Corn, Wheat & Rice Imports ('000 tonnes) Source: BMI, USDA © Business Monitor International Page 24 Venezuela Agribusiness Report Q3 2014. .. by 1.9% to 1.58mn tonnes in 2014; out to 2018, we believe consumption will grow by 14.3% on in 2013 level to 1.8mn tonnes © Business Monitor International Page 20 Venezuela Agribusiness Report Q3 2014 Table: Corn Production & Consumption (Venezuela 2013-2018) Corn Production, '000 tonnes Corn production, % y-o-y Corn Consumption, '000 tonnes Corn consumption, % y-o-y 2013e 2014f 2015f 2016f 2017f 2018f... livestock industry © Business Monitor International Page 22 Venezuela Agribusiness Report Q3 2014 Stagnating Fundamentals Venezuela - Corn Production ('000 tonnes), Area Harvested ('000 ha) & Yields (tonne/ha) Source: USDA, BMI Wheat Milling Industry Woes Venezuela' s largest private company Empresas Polar halted for several weeks in April-May 2014 its production of pasta over delays in foreign currency... that it will recover in 2014 with the small increase in production Out to 2018, we see consumption increasing by 3.8% on the 2013 level to 138,000 tonnes, fuelled primarily by population increases © Business Monitor International Page 14 Venezuela Agribusiness Report Q3 2014 Table: Beef Production & Consumption (Venezuela 2013-2018) Beef & Veal Production, '000 tonnes 2013 2014f 2015f 2016f 2017f 2018f... Business Monitor International Page 30 Venezuela Agribusiness Report Q3 2014 Sector On The Decline Venezuela - Coffee Production ('000 60kg bags) & Area Harvested ('000 ha) Source: BMI, USDA, FAO From Net Exporter To Net Importer Venezuela was once among the world's largest producers of coffee At the beginning of the 20th century, coffee production was the mainstay of the Venezuelan economy, accounting for... Monitor International Page 35 Venezuela Agribusiness Report Q3 2014 upcoming 2014/ 15 season, which begins in September 2014 Indeed, we are expecting large surpluses in both seasons, and our 2013/14 global market surplus forecast (of roughly 60mn tonnes) remains well above official estimates We are forecasting another large surplus (by historical standards) of 21mn tonnes in 2014/ 15 More Downside Ahead... International Page 13 Venezuela Agribusiness Report Q3 2014 Venezuela produces only small quantities of pork Output has remained stable at around 125,000 tonnes in recent years We see production growing slowly in 2013/14 Over our forecast period, we see production increasing by 7.5% on the 2012/13 level to reach 129,000 tonnes in 2017/18 BMI Demand View: Meat consumption soared in Venezuela' s boom years... disincentive for prospective investment (domestic and foreign) © Business Monitor International Page 12 Venezuela Agribusiness Report Q3 2014 Industry Forecast Livestock Outlook BMI Supply View: After strong growth in the 1990s and the early 2000s, Venezuelan beef production has gone into reverse in the past few years Venezuela was self-sufficient in beef in 2003, but in recent years the country has become increasingly... Business Monitor International Page 16 Venezuela Agribusiness Report Q3 2014 shared border to clamp down on terrorist groups and drug trafficking Venezuela also agreed to pay debts amounting to some USD800mn to Colombian exporters The agreement paves the way for the restoration of trade relations between the two countries, which promises to ease supply shortages of beef on Venezuelan shelves In April 2011,... Misión AgroVenezuela in January 2013 for 2013-2019 In total, VEF7.81bn will be available for 2013, of which VEF3.0bn will be dedicated to improving farm roads However, we believe this renewed programme will also be a failure and forecast corn production to actually stagnate between 2012/13 and 2017/18 © Business Monitor International Page 26 Venezuela Agribusiness Report Q3 2014 Stagnating In Venezuela ... production, % y-o-y -1 6.4 -4 .9 20.0 -0 .9 4.3 -1 .4 Beef & Veal Consumption, '000 tonnes 615.0 508.0 523.0 560.0 580.0 580.0 12.4 -1 7.4 3.0 7.1 3.6 0.0 Beef & Veal consumption, % y-o-y Sources: USDA... Consumption (Venezuela 200 8-2 013) 2008 2009 2010 2011 2012 2013 695.0 680.0 650.0 625.0 655.0 660.0 -6 .1 -2 .2 -4 .4 -3 .8 4.8 0.8 1,047.0 861.0 887.0 859.0 853.0 960.0 15.9 -1 7.8 3.0 -3 .2 -0 .7 12.5... Venezuela Agribusiness Report Q3 2014 Increasing Dependence Venezuela - Corn, Wheat & Rice Imports ('000 tonnes) Source: BMI, USDA © Business Monitor International Page 24 Venezuela Agribusiness Report