Rediscovering productivity in European agriculture

Humboldt Forum for Food and Agriculture e.V. (HFFA)

Dieter Kirschke, Astrid H채ger and Steffen Noleppa

Rediscovering productivity in European agriculture

HFFA Working Paper 02/2011

Theoretical background, trends, global perspectives, and policy options

Rediscovering productivity in European agriculture Theoretical background, trends, global perspectives, and policy options by Dieter Kirschke and Astrid Häger Humboldt University of Berlin, and Steffen Noleppa agripol – network for policy advice GbR

Contents Main findings of the study ........................................................................................... iii 1

Introduction ............................................................................................................ 1

2

Theoretical aspects of productivity and its measurement .................................. 2

3

Development and comparison of productivity in European agriculture ............. 8

4

Determinants of agricultural productivity and implications of productivity change .............................................................................................. 18

5

Global perspectives and the role of productivity ................................................ 31

6

Technological options for productivity increases ................................................ 39

7

Policy implications and options ........................................................................... 43

References .................................................................................................................... 46 Annexes........................................................................................................................ 59

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Acknowledgment The study was initiated by European Crop Protection Association and EuropaBio, Brussels. We gratefully acknowledge the support by these institutions and, particularly, thank the study coordinators for continuous backstopping and open discussion. The results of the study are the sole responsibility of the authors.

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Main findings of the study The study analyses the role of productivity in agriculture from an European perspective. It addresses the various dimensions of agricultural productivity from a theoretical and empirical point of view; it looks at determinants and consequences of productivity changes; it looks at challenges ahead discussing various options for productivity increases; and it addresses policy implications and options. The main findings of the study are presented here.

Introduction 1.

Productivity has played and will play a key role in European agriculture. In the past, productivity growth was a major factor for enhancing agricultural production and income in Europe and the world bringing down agricultural prices and providing food for a growing population. This evolution has shaped our view on modern agriculture and the demand for agricultural policy making in the European Union. It has led to changing priorities on the agricultural policy agenda emphasizing environmental objectives and rural development in a multifunctional context, whereas the ‘traditional’ agricultural policy perspective of enhancing productivity and production has been somewhat neglected.

2.

The high prices on world agricultural markets in recent years, however, have raised concerns about future food supply and demand and the role of productivity in meeting challenges ahead. Is it time to rediscover productivity as a key driver and important policy objective in international and European agriculture? Generally, productivity not only affects production and prices, but is a key determinant for competitiveness, and the role of Europe in global agriculture has been and will be determined to a large extent by productivity changes in this region as compared to the rest of the world.

Theoretical aspects of productivity and its measurement 3.

Productivity is a major concept in economics, but there is no unique approach and the discussion and measurement of productivity depends on specific questions pursued. Basically, productivity is defined as the ratio of output (produced) over the resources used (input) in the production process. It describes the technology level of a production process: A better technology leads to a higher degree of productivity meaning that the output/input ratio improves.

4.

A historically widely used partial productivity indicator in agriculture just relates the output of a crop to the land used and is defined as land productivity or, simply, yield. Another important partial productivity indicator is agricultural

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labor productivity or the income of farmers. Total Factor Productivity, on the other hand, tries to capture all outputs produced and contrast them with all inputs used. A change in Total Factor Productivity, thus, indicates an overall change in productivity due to technological change.

5.

Productivity measurement is a technical concept and yields technical indicators requiring a careful economic assessment and interpretation of productivity levels and changes. Yield levels as such do neither indicate a high or a low profitability of farming, nor do they allow for policy conclusions without further analysis.

6.

The concept of productivity in economics is close to the idea of intensity and intensification in the production process and productivity as well as intensity indicators are related. While productivity relates output to input, intensity is defined as the ratio of one input to another. The idea of intensification in agriculture typically refers to the input use per hectare. Increased input intensity of land will certainly increase land productivity, whereas the implication on labor productivity or Total Factor Productivity needs to be analyzed.

Increasing land productivity, on the other hand, may not indicate technological progress but increasing capital and/or labor intensity.

Development and comparison of productivity in European agriculture 7.

At first glance, productivity in European agriculture can be described by crop yields. Total cereal yields are highest in central and northwestern EU member states, and there is a clear productivity gap compared to Mediterranean and eastern EU member states. For wheat, high productivity levels and increases are noted, e.g., for Germany and France since 1961 until 2000. In the new millennium, however, we can observe a slowing down of productivity growth rates in these countries. We get different and specific developments for other EU member states, whereas the overall picture for maize and rapeseed is similar to wheat. Generally, growth rates of yields for major crops in EU member states have been rather heterogeneous and partly varying over time; no unique picture can be drawn.

8.

Land productivity in the EU has generally been higher than the average productivity level in the world, and the same is commonly true for growth rates, with rapeseed being an exception. The pictures for wheat and maize for the EU reveal that there has been a decline of growth rates in recent years.

The aggregated pictures for the world do not show this trend, but there is widespread opinion that the growth rates in land productivity have been slowing

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down in recent years in many regions of the world, especially in more developed countries.

9.

Labor productivity is another partial productivity indicator widely used. High labor productivity levels can be found in central and northwestern EU member states followed by southern EU member states. In contrast, most of the new EU member states are characterized by rather low labor productivity levels.

France is distinguished by both a high productivity level and growth rate followed by Italy, Spain, and Germany with high growth rates as well; Poland and Hungary as transition and new EU member states show a low labor productivity level and productivity growth.

10. European labor productivity is higher than the global average and this differentiation has been accentuated over time. Labor productivity growth in the EU was particularly high in the 1980s, whereas overall global labor productivity in agriculture stayed on a very low level during this period. In contrast, the high labor productivity level and development in Northern America is outstanding. Looking at the evolution of growth rates over time, there seems to be some indication that the growth in labor productivity has slowed down in the EU and some other developed countries in the recent decade, whereas rates have increased somewhat in some less developed countries over time.

11. Summarizing the overall development of land and labor productivity, it can be concluded that the EU and, especially, Western European countries have a high land and labor productivity level compared with other world regions.

However, productivity increases are slowing down in both cases; yet, agricultural productivity growth still seems to be higher in this sector than in other sectors of the economy.

12. Total Factor Productivity analyses provide a mixed picture on the level and the development of productivity in international agriculture. Recently, the international focus has been accentuated. There is some indication that Total Factor Productivity changes have stagnated in recent years, but there is no evidence of a general slowdown in global agricultural productivity growth.

High growth rates have been calculated for emerging economies for recent years. Nonetheless, there is a wide variation in productivity changes among regions and over time. It is argued, however, that Total Factor Productivity accounts for a rising share of agricultural growth over time. New calculations on global Total Factor Productivity changes based on most recent data also provide a mixed picture, but show now a rather decline of productivity changes in the last decade for several regions and notably for industrial countries. This

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confirms that Total Factor Productivity calculations and international comparisons still have to be interpreted with caution in view of the enormous statistical and methodological requirements.

Determinants of agricultural productivity and implications of productivity change 13. According to microeconomic theory, the technology and product and factor price determine output and input and, thus, productivity. For a given technology, an increasing factor price will reduce input and output, whereas an increasing product price enhances input use and output. An improved technology has the same effect as a higher product price.

14. Technological change and productivity increases are due to several factors, among which research and development expenditures certainly play a key role.

Research and development expenditures have remarkably increased over time, but their level is very different for developed countries and developing countries. Almost two thirds of overall agricultural research and development expenditures are spent in developed countries. The share of private and public research spending is nearly equal in developed countries, whereas in developing countries private expenditures are very low. This indicates the high relevance of private funding for productivity growth in developed countries, while the importance of public funding for agricultural research for productivity increases in developing countries is obvious.

15. In real terms, global public spending for agricultural research and development slowed down in recent years. In developed countries a change of priorities can be observed. More emphasis has been given to research on environmental effects of agricultural production and food quality and addressing consumer and social values. Less importance, on the other hand, has been given to productivity-oriented research. Hence, there has been a general shift away from cost-reducing and productivity-enhancing research towards more basic research and public interest research.

16. Private agricultural research and development expenditures have, traditionally, been more productivity-oriented. A more recent shift towards breeding and varietal technologies can be observed mainly driven by biotechnology. While chemicals research still holds a high share in overall private research and development spending, there is an increasing focus on breeding.

17. Research and development and the corresponding technological change in agriculture is generally considered as ‘the’ driver of past productivity growth.

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Much of the land and labor productivity increases observed has resulted from crop nutrition, in particular nitrogen fertilizer, crop protection, and modern varieties. In the crop breeding sector, highlights have been the development of hybrids and semi dwarf varieties, biotechnological developments such as tissue culture techniques and marker-assisted breeding and genomics, and the use of genetically modified crops improving insect resistance or increasing herbicide tolerance. Agronomy activities have also played an important role for agricultural productivity increases. Among others, tilling practices, better farm management and equipment, improved pest control via herbicides and integrated pest management, improved crop rotations, and improved precision of operation can be emphasized.

18. Apart from technology, extension and education have also been highlighted as key factors to improve productivity. Both factors enhance information flows that are useful to adopt new technologies. Besides, the socio-economic, cultural and political frameworks are key determinants for productivity increases.

Institutions count when economic change is considered, and the same is true for technological change and productivity increases. Finally, the policy framework and governance, in particular, play a major role for improving productivity in agriculture.

19. The regulatory framework probably has a considerable impact on productivity.

It may generally have an influence on technology releases and uptakes by farmers. The role of the regulatory framework for productivity increases in EU agriculture is particularly discussed with respect to genetically modified crops.

The current EU framework remains restrictive, increases the costs of seed production and most likely hampers productivity growth. While a cost reduction effect of genetically modified crops is widely accepted, there is increasing evidence that positive impacts on yields are achieved.

20. Technological change is also driven by scarcity of factors and, thus, by high factor prices as featured by the idea of ‘induced innovation’. If land is scarce and, hence, land prices are high, like in the historical Indonesian rice sector, technological change will lead to increased land productivity, and we will observe an increased capital and labor intensity with respect to land. This is the famous example of the terrace farming system on Bali. If, on the other hand, labor is expensive and land abundant, like in the historical Northern American context, the technological change induced will be labor saving and lead to higher labor productivity and higher capital intensity with respect to other factors. In such a context, the combined harvester has been introduced and the tomato harvester has been invented.

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21. There are several hypotheses on the role of product prices for technological change and productivity growth. According to the classical ‘infant industry argument’, a temporary protectionist policy and, hence, high prices may lead to increased human capital and knowledge about new technologies. High prices also improve the profitability of research and development expenditures and/or investment in new technologies, thus, increasing incentives for innovation. The opposite hypothesis is that high product prices allow for some reduced efficiency in production and lower the incentives for innovations. According to this argument, high product prices could reduce productivity increases.

22. In view of the various determinants of technological change and productivity increase, the slowing down in productivity growth as recently discussed may be attributed to some key developments. With respect to research and development expenditures, the general slowing down of public funding and its new orientation towards environmental objectives and addressing consumer and social values has reduced productivity-oriented research. Furthermore, the shift in private breeding activities towards genetically modified crops has led to a certain neglect of other breeding activities. With respect to the policy and regulatory framework particularly in the EU, two main arguments are discussed: Specific agri-environmental measures constrain the use of inputs and, thus, productivity growth, and the specific genetically modified crops policy hinders the use of potentials of new varieties. From a global point of view, the limited land availability, the increasing cultivation of marginal land, and the problem of soil erosion have a negative impact on productivity growth.

23. Productivity increases from technological change reduce unit and marginal cost of production and lead to a shift of supply curves on markets. The cost effect of technological change comprises a ‘cost-saving’ effect and a ‘production expansion’ effect: As a consequence of technological change, the same output quantity could be produced with fewer resources, but due to the lower marginal cost, the production becomes more profitable and production and input use will increase. Hence, the overall cost effect of technological change may be positive or negative.

24. The income and welfare effects of technological change depend on the cost effect and the price effects on markets. From a global point of view, technological change will lead to a price decreasing effect depending on the demand elasticity. Rather ‘inelastic’ demand is a particular feature for agricultural commodities; hence, such a price-decreasing effect of technological change is an important effect to be considered. As a consequence of this price effect, technological change will definitely benefit the consumers. Equally, the overall welfare effect of technological change for the society is positive.

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25. From the producer point of view, the overall income effect of technological change is less obvious. The income-enhancing effect of reduced production costs is contrasted with an income-reducing effect of the lower product price. The net effect needs to be calculated. This may be a surprising result, but simply shows that technological change leads to an overall welfare gain in an economy, whereas the distributional implication depends on demand and/or the policy framework. Looking at different producer groups, the ‘adopters’ of new technologies face the income-enhancing effect of these technologies and the income reducing effect due to the price decrease; the ‘non-adopters’ simply face an income loss. This is an apparent result and needs to be considered when assessing new technologies, since there will always be ‘losers’ due to technological change. The question is not ‘that’ technological change may have negative income implications for some groups, but ‘how’ the implementation of technological change should be managed within an accompanying policy framework to avoid or reduce such negative effects.

Global perspectives and the role of productivity 26. Productivity plays a major role for world agricultural markets. In the past, high productivity increases led to supply increases higher than demand increases resulting in a long-term trend of (real) price decrease. The situation, however, is about to change in the near future as indicated by the high world market price levels of 2007/08 and the recent price increase on agricultural markets. A continuously increasing price trend is expected for many agricultural commodities in the future.

27. In the first decade of the new century, three major forces have driven supply and demand on world agricultural markets. The first important force is demand in emerging economies, in particular in Asia. Population growth and income growth in developing countries will drive the evolution on world markets. FAO estimates that the additional food demand will be around 70 percent by 2050 compared to 2010. The second major driver is agricultural productivity growth, and there is evidence that the high productivity increases of the past have come down in recent decades. The World Bank has initiated the debate by showing in its 2008 World Development Report a decreasing trend of annual growth rates of yields for major cereals in developing countries from 1960 to 2005. The development on the supply and demand side is exacerbated by the third driving force: the energy price development. A high energy price increases cost of production and has a negative effect on supply, while it will enhance the demand for bioenergy on the demand side. Both effects will lead to an increase of agricultural prices.

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28. In view of such structural changes on world agricultural markets, the future role of productivity increases has to be evaluated. Price increases can only be avoided if demand increases come down and/or are matched by corresponding productivity increases. Both such perspectives for supply and demand are not yet in sight and, hence, world market prices are expected to increase.

29. The perspectives have to be further elaborated in view of additional challenges ahead. Climate change may have a negative impact on agricultural production in many developing countries, whereas other regions like Europe and northern countries might actually benefit from more favorable climatic conditions.

Impacts on agricultural production may also occur due to higher land demands for environmental and climate protection. While the direct competition between food and nature conservation is obvious, agriculture is not yet fully integrated in mitigation activities. For European agriculture, the greenhouse gas emission mostly results from methane and nitrous oxide and carbon dioxide from organic soils. A corresponding mitigation policy would have a considerable impact on regional production structures in European agriculture and other regions of the world. A specific challenge for the future will be the bioenergy policy in Europe and other world regions. Despite much criticism due to high mitigation costs in bioenergy production, the political support has become a major perspective for agriculture. The key questions are what land use requirements bioenergy production will have in the future and what the conflicting implications might be for food production. The message is clear: Integrating climate change, environmental and climate protection and bioenergy production into the world food system would certainly affect land use and contribute to increasing agricultural prices.

30. The new challenges accentuate the role of productivity increases in future world agriculture. Since they tend to reduce supply and increase demand, the growth of productivity becomes a key option for balancing agricultural markets and keeping price increases acceptable. Productivity increases and technological innovations have to be assessed in this new global framework.

The new competition for land with respect to food, energy and climate change is well characterized by the term ‘trilemma’. Today’s productivity growth in agriculture cannot be expected to meet the emerging gap.

Technological options for productivity increases 31. Future technological progress and productivity increases are widely expected to occur, and they are attributed both to improved breeding activities and improved agronomic practices. In particular, new breeding technologies are expected to offer an effective approach for enhancing agricultural productivity.

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The development of genetically modified crops is certainly an emerging technology for crop breeding with the potential to significantly increase agricultural productivity in the future. More traditional and currently widely used and accepted biotechnological breeding methods, however, may also provide promising options offering substantial productivity growth rates.

32. Agronomic technological options are also seen to have a high potential for future productivity growth in agriculture. New technologies related to the nutrition of species, to crop protection and soil management may contribute to substantial yield improvements, to reduced production cost and associated negative environmental impacts.

33. Some general remarks on technological options for productivity increases are worth being noted. First, it should be clear that partial effects of a new technology always have to be considered in a system context. Productivity increases can seldom be attributed to a single technology, but rather to a technological evolution of a system. In this context, the overall embedding of technological progress in the ongoing information technology revolution is emphasized. Second, there is no need to say that speculations about future technological changes and productivity increases must be vague. As Niels Bohr said: ‘Predictions can be very difficult – especially about the future’. Finally, the time horizon and the time lag of research and development activities have to be faced. Generally, technological innovations in agriculture are said to take more than one decade to show productivity effects on farms.

Policy implications and options 34. The global development of agricultural supply and demand is setting a new and challenging agenda for future policy-making. The emerging gap between supply and demand has to be faced squeezing agricultural markets and leading to food price increases. Having in mind agricultural policy priorities and a neglect of productivity orientation in the past, agricultural policy-making for the future will have to rediscover productivity as a key driver and important policy objective in international and European agriculture. Technological improvements and productivity increases are asked for and may help to reduce the agricultural squeeze, and a supportive policy framework is needed for productivity growth. The new policy paradigm does not mean coming back to Malthus, but coming to the end of Cochrane’s agricultural treadmill.

35. Global food security will have a high priority on the future agricultural policy agenda. A supportive framework for productivity growth has been emphasized, but there may be more demands on policy-making reducing the gap between

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supply and demand. All policies reducing agricultural supply and enhancing agricultural demand should be carefully re-evaluated in view of the challenges ahead. This refers, e.g., to the design of agri-environmental policies, the role of feed and animal production, the potential integration of agriculture in mitigation policies and the current bioenergy policy in the EU. An integrative agricultural policy concept is needed to meet diverging policy demands and handle the policy ‘trilemma’.

36. There is a long-lasting and ongoing debate on ‘intensification vs. extensification’ in agriculture; but neither intensification alone nor simple extensification will meet the challenges ahead and it does not really help to emotionalize and polarize. In view of diverging agricultural policy objectives, conflicts have to be faced anyway. An appropriate and responsible policy framework should take such conflicts into account and develop a comprehensive and integrated approach minimizing potential negative impacts. The challenge is to ‘do more with less’, e.g., to increase production and productivity with no additional or less environmentally negative impacts. The vision is to develop a policy framework for ‘intelligent and sustainable intensification and productivity increases’. This certainly is a still rather vague concept, but the need for such a concept is increasingly being underlined and acknowledged.

37. A particular point for future policy-making is research and development policy.

In view of the importance of public research and development expenditures in agriculture and the challenges ahead, there is a need to re-evaluate priorities and redirect expenditures towards productivity and technology-oriented research areas. There is no need to say that high-income countries have a particular responsibility for the global agricultural development.

38. In addition to public expenditure, private agricultural research and development has increasingly gained importance. Such private activities need a reliable and supportive policy framework. There have been complaints that the regulatory framework in the EU is rather strict and would need adjustment.

This would suggest a general re-assessment of this framework for attracting additional private sources in agricultural research and development, especially with respect to emerging technologies such as genetically modified crops.

Genetically modified crops should neither be privileged nor automatically be dismissed. This would certainly require an improved dialogue between policymakers, farmers, private research and development actors, and the public.

39. In view of the prospective global food balance, the EU responsibility has to be addressed. Agricultural productivity increases were high in the past and, in a protectionist policy setting, the view of abundant food and land availability is

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still widespread. As a consequence, global agricultural supply and demand developments are rarely considered when discussing European policy issues.

Today, European and international land use implications of policy changes are rather neglected, e.g., with respect to potential environmental priority areas or with respect to biofuels policies. There is a definite need to raise the awareness on the EU’s international responsibility in agricultural policy-making in general, and for balancing global agricultural supply and demand and supporting productivity growth in particular.

40. Looking finally at the Common Agricultural Policy, this policy field has only been slowly adjusted and integrated into the international framework, and there is no vision, yet, for the future policy in view of the emerging challenges.

The Common Agricultural Policy is still struggling with direct payments and the greening of subsidies, but, though policy-makers are talking on future challenges, there is no real effort for a re-orientation. The new policy requirements could well be addressed under the second pillar of the Common Agricultural Policy, and the new perspective for supporting productivity growth should be addressed explicitly.

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List of figures Figure 1. Productivity, technological progress, and shift of a production function ..................................................................................... 3 Figure 2. Total cereal production per hectare in EU member states, 2008/2009, in t/ha ....................................................................................... 8 Figure 3. Land productivity development in wheat production in selected EU member states, 1961-2009, in t/ha ........................................ 9 Figure 4. Land productivity development in wheat, maize and rapeseed production in the EU and the world, 1961-2009, in t/ha ........................ 11 Figure 5. Gross value added in agriculture per annual work unit in EU member states, 2008/2009, in EUR ................................................... 13 Figure 6. Value added in agriculture per worker in the EU and the world, 1980-2009, in real 2000 USD ................................................................... 14 Figure 7. Total factor productivity growth by region, average annual growth rate, in percent ............................................................................. 17 Figure 8. Profit maximization of a firm under perfect competition ....................... 19 Figure 9. Private and public agricultural R&D spending in developed and developing countries, 2000 ................................................................ 20 Figure 10. Private sector firms and R&D expenditures by type of research activity, 2006 ............................................................................. 21 Figure 11. Technological change and factor productivity ......................................... 26 Figure 12. Technological change, supply curve shift and production and cost effects ................................................................................................. 28 Figure 13. Technological change and global market effects ..................................... 29 Figure 14. Nominal prices on world agricultural markets, 1990-2011 .................... 32 Figure 15. Major drivers on world food markets and the world food equation ..................................................................................................... 32 Figure 16. Annual growth rate in agricultural R&D, by geographic area, 1976/81-1991/2000 .................................................................................... 34 Figure 17. Emissions of CO2-eqivalents in EU agriculture ...................................... 36 Figure 18. Global bioenergy potentials and corresponding land requirements for dedicated biomass plantations in 2050 for various scenarios ................................................................................. 37 Figure 19. Breeding and agronomic options for future agricultural productivity increases .............................................................................. 41

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List of abbreviations DBV

– Deutscher Bauernverband

DEA

– Data Envelopment Analysis

CAP

– Common Agricultural Policy

ECPA

– European Crop Protection Association

EU

– European Union

EuropaBio

– The European Association for Bioindustries

FAPRI

– Food and Agricultural Policy Research Institute

GHG

– Greenhouse gases

GM(O)

– Genetically modified (organism)

IEA

– International Energy Agency

MVP

– Marginal value product

OECD

– Organization for Economic Co-operation and Development

PIK

– Potsdam Institute for Climate Impact Research

R&D

– Research and Development

SFA

– Stochastic Frontier Analysis

TFP

– Total Factor Productivity

WEF

– World Economic Forum

WTO

– World Trade Organization

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1 Introduction Productivity has played and will play a key role in European agriculture. In the past, productivity growth was a major factor for enhancing agricultural production and income in Europe and the world bringing down agricultural prices and providing food for a growing population (see, e.g., HENRICHSMEYER AND WITZKE, 1991; 1994; KOESTER, 2010). This evolution has shaped our view on modern agriculture and the demand for agricultural policy making in the European Union (EU). It has led to changing priorities on the agricultural policy agenda emphasizing environmental objectives and rural development in a multifunctional context, whereas the ‘traditional’ agricultural policy perspective of enhancing productivity and production has been somewhat neglected.

The high prices on world agricultural markets in recent years, however, have raised concerns about future food supply and demand and the role of productivity in meeting challenges ahead (see, e.g., FAO, 2009A; JAGGARD, QI AND OBER, 2010).

Is it time to rediscover productivity as a key driver and important policy objective in international and European agriculture? Generally, productivity not only effects production and prices, but is a key determinant for competitiveness, and the role of Europe in global agriculture has been and will be determined to a large extent by productivity changes in this region as compared to the rest of the world.

It is against this background that the study tries to give a comprehensive overview on the role of productivity in agriculture in the past and in the future. The study addresses the various dimensions of agricultural productivity from a theoretical and empirical point of view, and it will look at determinants and consequences of productivity changes. It will also look at challenges ahead discussing various options for productivity increases and address policy implications and options.

The outline of the study is as follows:  Productivity is a major concept in economics, but there is no unique approach and the discussion and measurement of productivity depends on specific questions pursued. Chapter 2 starts from a simple definition of productivity as the ratio of output (produced) over the resources used (input) in a production process, and it discusses various indicators and measurement approaches.  Chapter 3 presents empirical information on the state and development of productivity in European agriculture. The analysis is based on widespread productivity indicators like yield, labor income and total factor productivity, and it will allow for a comparison between different regions in the EU. We will further set the European development into the international perspective.

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 Economic theory suggests technology as well as prices for products and factors as main determinants for productivity. Starting from here, chapter 4 presents major relationships and arguments and addresses new developments in the discussion. Productivity changes translate into shifts of supply curves and, thus, affect agricultural markets. The implications of such shift effects are also outlined in this chapter.  From a global perspective, productivity is a key factor for balancing world agricultural markets. In chapter 5, the main drivers on world markets as currently discussed are outlined and the problem of ‘feeding the world in 2050’ (FAO, 2009A) is highlighted. This is setting the framework for discussing the future role of productivity in agriculture and the potential need for productivity increases under various scenarios.  Future productivity increases face several challenges. Chapter 6 examines some technological options that are currently discussed. The perspectives of some innovations will be reflected helping to better assess evolutions and potentials.  Facing future perspectives and technological developments, society and policymakers will have to decide on policies to be pursued. Chapter 7 sketches out some policy implications and options. The focus will be on the European role and perspective for productivity in agriculture in a global context.

2 Theoretical aspects of productivity and its measurement Productivity is a major concept in economics with a simple definition, but the application of this concept is less straightforward and rather complex. Discussions on the theory of productivity can be found in many textbooks (see, e.g., PINDYCK AND RUBINFELD, 2009; SAMUELSON AND NORDHAUS, 2010) and in more specific recent publications (see, e.g., ALSTON, BABCOCK AND PARDEY, 2010; DISKIN, 1997; LATRUFFE, 2010). Basically, productivity is defined as the ratio of output (produced) over the resources used (input) in the production process. Let y be the output considered and x be the input used, the productivity π is: (1)  

y . x

Productivity describes the technology level of a production process. A better technology leads to a higher degree of productivity meaning that the output/input ratio improves. This understanding of productivity is visualized in figure 1. The

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production function y(x) shows an initial technology level using input x1 and producing output y1. The (average) productivity of this technology is indicated by tan(α). Technological progress shifts the production function upwards to y’(x) showing that the same output y1 could be produced with less input x2 or a higher output y2 could be produced with the same input x1. In any case, the result is a higher productivity level which, e.g., is shown in figure 1 by tan(β). Hence, technological progress can be visualized as a technology shift of a production function improving the output/input ratio, thus, enhancing productivity.

Figure 1.

Productivity, technological progress, and shift of a production function

Output y

y'  x  y x 

y2 y1



 x2

x1

Input x

Source: Own figure.

Technology shifts and productivity changes take place over time. Based on equation (1) such productivity changes can be identified by using the rates of change for output and input (see, e.g., CHIANG AND WAINWRIGHT, 2005): (2)

d





dy dx .  y x

Equation (2) describes the percentage change in productivity as the difference between the percentage change in output minus the percentage change in input. To give an example: If we observe a 2.0 percent output increase with no change in

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input, we can conclude that productivity has increased by 2.0 percent; if, on the other hand, input has increased by 0.5 percent in the period considered, then, the productivity increase would only be 1.5 percent. It has become very popular in the literature to look at productivity changes instead of productivity levels which is, of course, just another look at the same issue.

In real agricultural production, many outputs are produced with many inputs. The concept of productivity, then, has to be adjusted according to the outputs and inputs considered. There is no unique way in defining an appropriate productivity indicator for multiple inputs and outputs, but the definition depends on a question pursued (see, e.g., LATRUFFE, 2010). If, e.g., one is interested in looking at productivity changes just with respect to specific commodities or resources, then so-called partial productivity indicators have to be defined. Total Factor Productivity (TFP), on the other hand, tries to capture all outputs produced and to contrast them with all inputs used (FUGLIE, 2010A; HUFFMAN, 2009). A change in TFP, thus, indicates an overall change in productivity due to technological change.

A historically widely used partial productivity indicator in agriculture just relates the output of a crop to the land used and is defined as land productivity or, simply, yield. Land has been a key production factor in human development and, hence, the increase of yields has been and will be a key indicator of agricultural development. It has to be noted, however, that the increase of land productivity may not only be the result of technological progress such as breeding, but also of an increased use of other inputs like fertilizer, machinery, and/or labor. This is, obviously, an important and general point to be considered when interpreting partial productivity indicators.

Another important partial productivity indicator has been and will be agricultural labor productivity. The income of farmers and agricultural labor apparently is of major concern for farmers, policy-makers and the society. For defining an appropriate labor productivity indicator, several aspects have to be considered. First, the definition of labor raises some problems since different kinds of labor inputs, for example hired labor and family work, have to be identified and aggregated. Second, various outputs have to be aggregated, and this can only be done, meaningfully, on a monetary level using prices to calculate a total output level. If prices change over time, assumptions have to be made on the prices used for aggregation. Third, a distinction has to be made with respect to a ‘gross’ or ‘net’ view of labor productivity. In a gross labor productivity concept, the overall production value is taken into account, whereas in a net labor productivity concept, the value added is considered subtracting intermediate production cost from the production value. In this concept, the focus is on the income generated by the primary production factors land, labor and capital.

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Hence, land productivity or yield as discussed above is a simple volume productivity indicator, whereas labor productivity typically is a monetary productivity indicator. Both indicators can be calculated for farm level data or for aggregated data on the sector level for regions and/or countries. Both types of calculations are found in the literature. Sectoral data of national accounting systems are easily accessible and allow for a quick international comparison of partial land and labor productivities.

The calculation of TFP, on the other hand, has become an important and challenging research question. Basically, both outputs and inputs have to be aggregated requiring relevant information. Furthermore, an assumption has to be made on the aggregated multi-output, multi-input production function in order to ‘squeeze out’ technological changes. The state of the art is not yet a uniform and widespread standard approach, but there is an ongoing discussion on methodological procedures and different results (see, e.g., ALSTON, BABCOCK AND PARDEY, 2010; FUGLIE, 2008; RUNGSURIYAWIBOON AND LISSITSA, 2006).

The idea of deriving a TFP can be explained by the so-called Cobb-Douglas production function approach as used in the early TFP discussion (see SAMUELSON AND NORDHAUS, 2010). A Cobb-Douglas production function typically relates the production of one commodity and the input of land, labor and capital as primary factors and intermediate inputs. The functional form is as follows: 







(3) y  t  a  l  c  i

with t – technology factor, a – area of land, l – labor, c – capital, i – intermediate inputs, and α, β, γ, and δ – the partial production elasticities of the factors considered. For a homogenous production function, these elasticities sum up to 1, hence: α + β + γ + δ = 1, and under perfect competition, they correspond to the cost shares of these inputs.

Changes in the technology factor t then reveal the TFP change under this function.

Taking the rates of change of equation (3) and solving for dt/t, yields: (4)

dt dy da dl dc di .      t y a l c i

Equation (4) shows the basic approach to TFP change measurement. Based on a production technology assumed, TFP can be identified by looking at input and output changes and weighting the input changes by the corresponding production elasticities or input cost shares. If this information is available, TFP calculation will be straightforward. Let us consider an output increase of 2.0 percent and an increase of all inputs of just 1.0 percent; then TFP would increase by the same 1.0

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percent. If, in another case, land and labor remained constant, and capital and intermediate inputs increased by 1.0 percent and reflected an aggregate production elasticity or cost share of 0.5 percent, then the overall production increase due to these factors would amount to 0.5 percent, and the resulting TFP increase would be 1.5 percent.

For real TFP calculations, several problems have to be overcome. A key issue is the aggregation of commodities using product and factor prices. In many cases, this information is not available and has to be generated, e.g., by using data of comparable country settings. Also, an assumption has to be made with respect to the time of aggregation. This has led to a number of TFP indicators that have been used in calculations. A general idea is to decompose the aggregated TFP indicator and attribute productivity change to a change in technology and efficiency and sometimes also to economies of scale (KUOSMANEN AND SIPILÄINEN, 2009; O’DONNELL, 2010). A widespread index used in the literature is the Malmquist TFP index as suggested by CAVES, CHRISTENSEN AND DIEWERT (1982) and COELLI AND PRASADA RAO (2005).

In view of the data problems faced for TFP calculations, a ‘dual approach’ has been proposed using product and factor prices for productivity calculations. The idea is that for profit maximization and competitive conditions price developments will indicate productivity changes (BALL ET AL., 2010; O’DONNELL, 2010).

TFP analyses have been carried out for commodities and countries (see, e.g., NINPRATT, YU AND FAN, 2010; JIN ET AL., 2010; FUGLIE, 2010B). In recent years, the idea of an international comparison of productivity developments has become a major challenge (ALSTON, BABCOCK AND PARDEY, 2010; EVENSON AND FUGLIE, 2010; FUGLIE, 2008; 2010C; FUGLIE AND SCHIMMELPFENNING, 2010; LUDENA ET AL., 2007). In addition to enormous data problems, some specific issues have to be addressed for international comparisons such as defining appropriate exchange rates or purchasing power parities (VAN BIESEBROECK, 2009). The international perspective has also generated some new and interesting questions. There is, e.g., an ongoing and still open debate on the possible convergence or divergence of productivity changes worldwide (ESPOSTI, 2011).

A new development in TFP analysis is the use of programming techniques like Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) (COELLI AND PRASADA RAO, 2005; HEADEY, ALAUDDIN AND PRASADA RAO, 2010). Both approaches have significantly contributed to the analysis of production and efficiency in recent years. There are pros and cons for the non-parametric DEA and the parametric SFA (ONDRICH AND RUGGIERO, 2001), whereas the SFA is generally considered to better handle ‘statistical noise’.

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A focus on TFP measurement in productivity analysis is straightforward if the perspective is on overall assessment of technological change. This is a key perspective for productivity analysis, but it is not the only one. Productivity measurement is a technical concept and yields technical indicators, but the choice of indicators first needs a careful consideration of the objectives pursued. In particular, such technical indicators do not replace an economic assessment of productivity levels and changes. Low yields, to give an example, do not necessarily indicate a low profitability of farming, and they do not automatically point to research and development (R&D) investment requirements. Hence, a careful economic discussion and interpretation has to follow productivity analysis before drawing firm and policy conclusions.

The concept of productivity in economics is close to, but should not be mixed up with the idea of intensity and intensification in the production process. While productivity relates output to input, intensity is defined as the ratio of one input to another. In general, two production inputs are considered like the ratio between capital and labor, which is the capital intensity (of labor). The idea of intensification in agriculture typically refers to the input use per hectare which could be fertilizer and/or pesticide use or the overall use of intermediate inputs or even the use of all other inputs including capital and labor per hectare. It is obvious that productivity and intensity indicators are related. Increased input intensity (of land) will certainly increase land productivity, whereas the impact on labor productivity or TFP needs to be analyzed. Increasing land productivity, on the other hand, may not indicate technological progress, but increasing capital intensity (with respect to land and/or labor). Hence, the relationship between productivity and intensity needs a careful interpretation if partial productivity indicators are considered, whereas TFP tries to sort out the technological progress effect, what is not a simple task.

A new relevance for partial productivity indicators may emerge in view of environmental and climate change focusing on scarce natural resources. Water resources, e.g., have become more and more scarce, and there are discussions on the water productivity of various crops and production processes (see, e.g., MOLDON ET AL., 2010; ZOEBL, 2006; ZWART ET AL., 2010). Also, biomass productivity of land in view of increasing bio-energy use has become a point of discussion (MATHEWS AND TAN, 2009; STERNER, 2010). These developments underline that new challenges and policy perspectives have an impact on productivity discussion and measurement and may change our traditional view on productivity focusing on main production factors like land and labor and neglecting scarce natural resources (see, e.g., POLLOCK, 2011; VEEMAN, 2008). Further productivity reflections with respect to energy use or greenhouse gas (GHG) emissions are conceivable. Such reflections could be interesting from a technical point of view, but again, such indicators alone do not yet suggest entrepreneurial or policy action.

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3 Development and comparison of productivity in European agriculture In this chapter, some productivity indicators are presented assessing the productivity level and productivity change in European agriculture and setting them in an international context. We focus on EU agriculture and on crops and start with partial productivity indicators, which have been very popular. Figure 2 shows today’s land productivity in cereal production in EU member states as given by total cereal production per hectare. The figure shows considerable land productivity differences in European agriculture. Total cereal yields are highest in central and northwestern EU member states, and there is a clear productivity gap compared to Mediterranean and eastern EU member states.

Figure 2.

Total cereal production per hectare in EU member states, 2008/2009, in t/ha

BE NL FR UK IE DE AT DK LU SI CZ IT HU EU SE SK MT EL BG FI PT LT PL ES LV RO EE CY 0

1

2

3

4

5

6

7

8

9

10

Source: Own figure based on FAO (2011C).

Figure 3 shows the development of land productivity in wheat production in selected EU member states from 1961 to 2009. The figure shows the high productivity levels and increases in Germany and France from 1961 until 2000.

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Figure 3.

9

Land productivity development in wheat production in selected EU member states, 1961-2009, in t/ha Germany

France

9

9

y = 2,8445e0,0259x

y = 3,1289e0,0227x

3

3

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

6

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

6

Italy

Spain

9

9

6

6

y = 2,0696e0,0119x

y = 1,0572e0,023x

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

3

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

3

Poland

Hungary

9

9

6

6

y = 1,794e0,0397x

3

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

3

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

y = 2,1813e0,0136x

Source: Own figure based on FAO (2011C).

The annual growth rates in productivity until 2000 were 2.3 percent in Germany and 2.6 percent in France, respectively. In the new millennium, however, we can observe a slowing down of productivity growth rates in these countries. Starting from a lower level, annual growth rates in productivity until 2009 was 1.2 percent in Italy and 2.3 percent in Spain. In Poland, the overall annual productivity growth

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until 2009 was 1.4 percent, and the growth rate was not affected by the transition process. In contrast, Hungary experienced a high growth rate in productivity until 1990 of almost 4.0 percent and has been severely affected since then under the transition process.

We get similar pictures for land productivity developments in maize and rapeseed production in selected EU member states which are presented in annexes 1 and 2.

Following considerable growth rates for maize until 2000 in Germany, France, Italy, and Spain, there has been a slowing down in growth rates since then. For rapeseed, there were continues growth rates until 2009 in Germany and France of 1.6 percent. The development has been different in Italy and Spain where no major growth of yields can be observed. In Poland and Hungary, however, growth rates of 1.2 percent for Poland and 1.3 percent for Hungary can be estimated. Hence, the overall picture is that growth rates of land productivity for major crops in EU member states have been rather heterogeneous and partly vary over time. No uniform picture can be drawn.

Figure 4 compares land productivity development in wheat, maize, and rapeseed production in the EU compared to the global development since 1961. Land productivity in the EU has generally been higher than the average productivity level in the world, and the same is commonly true for growth rates, with rapeseed being an exception. The pictures for wheat and maize for the EU reveal that there has been a decline of growth rates in recent years. The aggregated pictures for the world do not show this trend, but there is widespread consensus that the growth rates in land productivity have been slowing down in recent years in many regions of the world, especially in more developed countries (see, e.g., AHLEMEYER AND FRIEDT, 2011; AMBERGER, 2010; DE WIT, LONDO AND FAAIJ, 2011; FINGER, 2010; FISCHER AND EDMEADES, 2010; PARDEY AND PINGALI, 2010).

Land productivity development in wheat, maize and rapeseed production in major production regions of the world since 1961 is displayed in annexes 3 to 5. These figures reveal remarkable differences in land productivity levels and changes between crops and regions. Productivity growth for wheat has been rather low in Northern America and was practically non-existing in Oceania. Growth rates have been somewhat higher in South America and Africa starting from a low level, and the picture for Asia shows the ‘Green Revolution’ success story until the mid 1980s with growth rates having slowed down since then. A somewhat different picture can be observed for maize in annex 4. In this case, the remarkable levels and growth rates for Northern America are underlined, whereas growth rates for Oceania and Asia are also remarkable, yet on a different level. This development is contrasted by the stagnating situation in Africa. Finally, the developments for rapeseed are rather similar to the developments for wheat.

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Figure 4.

11

Land productivity development in wheat, maize and rapeseed production in the EU and the world, 1961-2009, in t/ha

Wheat European Union

World

60 000

60 000

y = 20406e0,0268x 40 000

40 000

30 000

30 000

20 000

20 000

10 000

10 000

0

0

y = 12241e0,0204x

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

50 000

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

50 000

Maize European Union

World

80 000

80 000

y = 24474e0,0272x 60 000

60 000

y = 20428e0,0195x

20 000

20 000

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

40 000

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

40 000

Rapeseed European Union 40 000

World 40 000

y = 16680e0,0137x

20 000

20 000

10 000

10 000

0

0

y = 6062,5e0,0243x

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

30 000

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

30 000

Source: Own figure based on FAO (2011C).

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The assessment of livestock productivity development is obvious in some cases and less straightforward in others. The productivity in milk production, e.g., can be easily measured and compared. Annex 6 shows milk yield developments in selected regions of the world from 1961 to 2009. The growth rate of 1.8 percent in the EU is remarkable, but the growth rate of 2.2 percent in Northern America is outstanding.

Levels and growth rates in other regions of the world have been rather low, with Oceania at the upper level and Africa at the lowest level having shown practically no development for decades. Productivity increases in meat production are typically measured in live weight gains per day. There is some information on such indicators (see, e.g., FAO, 2011A), but this does not allow for a meaningful international comparison. Some scientists like POLLOCK (2011), LUDENA ET AL. (2005), STEWARD, VEEMAN AND UNTERSCHULTZ (2009) and VEEMAN (2008) argue that increases in livestock productivity, especially with respect to ruminants, have been typically lower than in crop productivity.

Labor productivity is another partial productivity indicator widely used. In most cases, agricultural value added as given by the output value of the agricultural sector less the value of intermediate inputs is referred to agricultural work units per year. Figure 5 shows the gross value added in agriculture per annual work unit in EU member states in 2008/2009. We find high labor productivity levels in central and northwestern EU member states followed by southern EU member states. In contrast, most of the new EU member states are characterized by rather low labor productivity levels.

A disaggregated picture on agricultural labor productivity for EU regions is given in annex 7. The map shows the gross value added in agriculture per annual work unit in EU member states by NUTS 2 regions. While confirming the country perspective, the map reveals remarkable regional labor productivity differences within some EU member countries.

The development of agricultural labor productivity in selected EU member states from 1980 to 2009 is presented in annex 8. France is characterized by a high productivity level and growth rate of 5.7 percent followed by Italy, Spain, and Germany with growth rates of above and around 5 percent. For Poland and Hungary, the picture shows the low level and productivity growth in the transition and new EU member states.

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Figure 5.

13

Gross value added in agriculture per annual work unit in EU member states, 2008/2009, in EUR

NL BE FR DK UK DE LU ES IT SE AT MT FI EU27 CY EL IE CZ EE PT SK HU SI LT BG RO PL LV 0

5 000

10 000

15 000

20 000

25 000

30 000

35 000

40 000

45 000

Source: Own figure based on EUROPEAN COMMISSION (2011) and EUROPEAN COMMISSION (2010A).

Figure 6 compares the development of agricultural labor productivity in the EU and in the world since 1980 as given in real 2000 USD values. The figure shows the higher European labor productivity level, which has been accentuated over time.

Labor productivity growth in the EU was particularly high in the 1980s, and the average annual growth rates since 1980 amount to 3.2 percent. In contrast, overall global labor productivity in agriculture stayed on a very low level during this period.

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Figure 6.

Value added in agriculture per worker in the EU and the world, 1980-2009, in real 2000 USD European Union

World 20 000

y = 6159,9e0,0325x

16 000

16 000

12 000

8 000

8 000

4 000

4 000

0

0 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

12 000

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

20 000

Source: Own figure based on WORLD BANK (2011).

An even more differentiated picture on the development of agricultural labor productivity in major regions of the world is given in annex 9. The EU has experienced a remarkable growth rate in agricultural labor productivity, but the level and the development in Northern America is outstanding: An average growth rate of 4.6 percent since 1980 has led to a current labor productivity level of almost 50 000 (real 2000) USD in 2009. In other world regions, the contrasting low labor productivity levels are visualized by the diverting scales of the pictures. In East Asia and Pacific and Latin America and the Caribbean, labor productivity growth has been remarkable with 2.7 percent and 2.2 percent, respectively, yet starting from a lower level. In South Asia, the labor productivity growth rate of 1.6 percent has rarely improved the income level. The situation in Sub-Saharan Africa has been serious with a current labor productivity level of less than 400 USD, which is about the same as 30 years ago.

Looking at the evolution of growth rates over time, there is some indication of a change in the recent decade though the figures in annex 9 rarely allow for an interpretation. Some scientists state that the growth in labor productivity has slowed down in the EU and some other developed countries, whereas rates have increased somewhat in some less developed countries over time (see, e.g., PIESSE AND THIRTLE, 2010B).

Labor productivity in agriculture has become an important productivity indicator throughout the world because income generation in the sector is a major policy issue in many countries. Comparing labor productivity in agriculture to labor productivity in the overall economy gives information on the relative income

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generation capacity of the agricultural sector. It is typical for many countries that a ‘disparity’ can be noticed for this sector. Another way of describing such an income disparity would be to say that the income or value added share of agriculture in the overall economy is less than its share in the work force. Let va and ve denote the value added in agriculture and in the overall economy, respectively, and la and le be the corresponding work forces, we get for the relative income level of the agricultural sector θ:

va va l v (5)   a  e . ve la le le Annex 10 shows the income gap and the disparity for the agricultural sector in EU member states as calculated by the relative gross value added of the agricultural sector compared to the overall economy. Though an income disparity can be found in all EU member states, the individual country situation is mixed and difficult to interpret. The Netherlands, e.g., show a rather small income gap, and so does Bulgaria or Estonia. In Bulgaria, this may be explained by a rather low general income level; hence, the agricultural income level is low, and so is the general income level in the economy. Germany can be found in the middle field of the EU member states. At the lower end, we can find Eastern European countries, but also countries like Austria, Sweden, Luxemburg or Finland. In these countries, the absolute agricultural income situation may be acceptable, but compared to the high income levels of the overall economy, the disparity of the agricultural sector is high.

Summarizing the overall development of land and labor productivity, PARDEY (2011), e.g., concludes that the EU and, especially, Western European countries have a high land and labor productivity level compared with other world regions, but productivity increases are slowing down in both cases (PARDEY AND PINGALI, 2010). However, agricultural productivity growth still seems to be higher in this sector compared to other sectors of the economy (CHAVAS, 2008; MULLEN, 2007; NOSSAL AND GOODAY, 2009). Looking at both land and labor productivity development in various world regions since 1961, annex 11 allows for an interesting comparison of development patterns. The development paths for the indicated regions show the relative labor productivity growth compared to land productivity growth. This relative labor productivity growth is higher the flatter the curves are.

Hence, relative labor productivity growth has been rather high in Western Europe with high productivity levels for both indicators. On the contrary, relative labor productivity growth has been lower for Australia and New Zealand, whereas absolute labor productivity levels have been higher and absolute land productivity level have been lower compared to Europe.

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Compared to partial productivity indicators, information on TFP indicators is rare.

There have been several calculations for specific countries (see, e.g., ALSTON, BABCOCK AND PARDEY, 2010; FUGLIE, 2008; NIN-PRATT, YU AND FAN, 2010; JIN ET AL., 2010; FUGLIE, 2010B). These analyses provide a mixed picture on the level and the development of TFP in international agriculture.

Recently, the international focus in TFP analysis has been accentuated in FUGLIE (2008). The calculations are summarized in figure 7. There is some indication that TFP changes have stagnated in recent years, but there is no evidence of a slowdown in global agricultural TFP growth. High growth rates have been calculated for emerging economies like Brazil and China for recent years. Nonetheless, there is a wide variation in productivity changes among regions and over time and no unique conclusion can be drawn for TFP changes from a global point of view (FUGLIE, 2008). FUGLIE (2010A) argues, however, that TFP accounts for a rising share of agricultural growth over time. This evolution is indicated in annex 12.

Interestingly, FUGLIE (2010B) provides new information on global TFP changes based on a new data set. The results are shown in annex 13. Whereas we get a mixed picture as before, the calculations now rather show a decline of TFP changes in the last decade for several regions and notably for industrial countries. It is difficult to assess and compare such results. They show that TFP calculations and international comparisons still have to be interpreted with caution in view of the enormous statistical and methodological requirements.

Productivity indicators with respect to non-typical production factors like natural resources or specific input use slowly emerge in the scientific discussion. The background for the new development certainly is that the focus in the productivity debate changes from the purely economic and traditional discussion to new topical issues. Water productivity is a point in case; it shows, e.g., the productivity of water use in agricultural production (see, e.g., DE FRAITURE AND WICHELNS, 2010; ZWART AND BASTIAANNSEN, 2004). High water productivity does not mean that a certain production is economically profitable (see, e.g., MOLDON ET AL., 2010; ZOEBL, 2006); it simply shows that a certain production process efficiently uses a scarce resource.

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Figure 7.

17

Total factor productivity growth by region, average annual growth rate, in percent

Average annual growth rate (%) by period Sub-Saharan Africa

TFP index 1970-1979

1980-1989

1990-1999

2000-2006

–0.37

0.94

1.47

0.61

0.61

1.30

2.38

2.48

–0.54

3.13

3.00

3.66

Middle East & North Africa

0.42

1.73

1.59

1.56

Northeast Asia, developed

1.86

2.22

2.55

3.08

Northeast Asia, developing

0.51

2.57

4.00

3.42

–0.19

2.47

3.78

3.22

Southeast Asia

2.01

0.97

1.60

2.16

South Asia

0.66

2.02

1.71

1.36

India

0.80

2.10

1.74

1.43

North America

1.46

1.36

2.13

1.75

Oceania

1.08

1.02

1.90

–0.25

Western Europe

1.46

1.65

1.97

1.39

Eastern Europe

0.58

0.33

1.03

0.58

–0.74

0.29

1.60

3.28

Developing countries

0.55

1.67

2.31

2.08

Developed countries

1.62

1.48

2.25

1.76

–0.46

0.27

1.59

2.10

0.60

0.94

1.60

1.55

Latin America & Carribean Brazil

China

USSR, former

USSR & Eastern Europe World Source: FUGLIE (2008).

There have been presented some calculations of water productivity in the literature (CHAPAGAIN AND HOECHSTRA, 2004; LIU ET AL., 2007; ZWART ET AL., 2010). Annex 14 shows water productivity in wheat production in various countries. The table visualizes that water productivity in wheat production is rather high in many EU member states, whereas, e.g., it is rather low in major wheat production regions of the world like USA, Russia, and Canada. In China, the scientific research results are somewhat contradictory showing rather low water productivity in two of the presented analyses, whereas CHAPAGAIN AND HOECHSTRA (2004) put China on rank 9 within the international comparison.

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The concept of energy productivity in agriculture has been proposed by FLUCK (1979) indicating the quantity of a given agricultural product per unit of energy required for its production. The concept is straightforward, but the definition and calculation of the ‘required energy’ for production is less obvious. A key question, e.g., is what kind of energy should be considered in the calculations. The use of manure means use of energy and can be compared to the use of mineral fertilizer to assess energy inputs of different production systems. However, the approach would be different if the question was to assess fossil energy use and GHG emissions of production systems. More generally, the measurement of energy productivity is site-specific and technology-specific and does not allow for a sound international comparison.

Little progress on energy productivity assessment has been made since the early work of FLUCK (1979) (see also KHAN AND HANJARA, 2009). Some more recent applications are divers with respect to the methodology applied and do not allow for a comprehensive picture (see, e.g., BAILEY ET AL., 2003; NONHEBEL, 2002; PERVANCHON, BOCKSTALLER AND GIRARDIN, 2002; PIMENTEL, 2009). The general view, of course, is that scarce fossil resources will increasingly limit production growth in agriculture and affect land and labor productivity.

4 Determinants of agricultural productivity and implications of productivity change In market economies, firms decide on the use of inputs to produce outputs for a given technology and given product and factor prices. The basic microeconomic theory of the firm explains the production process under perfect competition (see, e.g., PINDYCK AND RUBINFELD, 2009). Let ξ be the firm’s profit, p the product price and r the factor price, then we get: (6)   p  y  x   r  x .

To maximize profit, the marginal value product (MVP) of the factor use must equal the factor price. The MVP is the additional value of the output produced with the last factor unit used in production ( p  dy / dx ). Hence, the profit maximization condition can also be interpreted that in the optimum the ‘marginal benefit’ of the factor must equal its ‘marginal cost’ (factor price). The optimum condition can be visualized by figure 8 showing the MVP curve and the factor price line. For a given technology and a given factor price r1, the firm’s profit maximizing behavior determines input use x1 and output produced y1 (not shown in figure 8).

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Figure 8.

19

Profit maximization of a firm under perfect competition

MVP, r

p

dy  Marginal value product (MVP) dx

r  Factor price

r1

x1

x2

x

Source: Own figure.

Hence, the technology, product and factor prices determine output and input and, thus, productivity (see, e.g., STEWARD; VEEMAN AND UNTERSCHULTZ, 2009; VEEMAN, 2008). For a given technology, an increasing factor price will reduce input and output, whereas an increasing product price shifts the MVP curve to the right and increases output and input use. In figure 8, the input use increases from x1 to x2. An improved technology would have the same effect as a higher product price increasing MVP of factor use and shifting the MVP curve to the right. The increased factor use x2 in figure 8 can, thus, be the result of a higher product price and/or an improved technology.

There is a broad discussion in the literature on determinants of technology and technology changes. In a simplistic interpretation, technology is autonomous and technology changes just occur. This may rarely be the case when unprecedented inventions occur and drive technological change. In most cases, however, technology changes just do not happen, but are driven by various determinants.

Research and development (R&D) expenditures certainly play a key role for technological change, what is underlined by many scientists (FUGLIE AND HEISEY, 2007; HUFFMAN, 2009; NOSSAL AND GOODAY, 2009; PARDEY AND PINGALI, 2010; SHENG, GRAY AND MULLEN, 2011). R&D expenditures have remarkably increased

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over time, but their level is very different for developed countries and developing countries. A distinction also has to be made with respect to private R&D expenditures and R&D expenditures of public institutions. About five percent of all global expenditures in R&D are currently attributed to agriculture (PARDEY AND PINGALI, 2010). A differentiated view for developed and developing countries as well as private and public R&D expenditures is given for 2000 in figure 9, which mark the latest available information (PARDEY AND PINGALI, 2010).

Figure 9.

Private and public agricultural R&D spending in developed and developing countries, 2000 in billion USD

Shares, in percent

Private

Public

Total

Private

Public

Developed countries

12.1

10.2

22.3

54.3

45.7

Developing countries

0.9

12.8

13.7

6.3

93.7

12.9

23.0

36.0

36.0

64.0

Total

Source: PARDEY, ALSTON AND PIGGOTT (2006).

According to figure 9, almost two thirds of overall agricultural R&D expenditures are spent in developed countries. Also the share of private and public research spending is nearly equal in developed countries, whereas in developing countries private R&D spending is as low as 6.3 percent with the public share being 93.7 percent. This indicates the high relevance of private R&D funding for productivity growth in developed countries while the importance of public funding for agricultural research in productivity increases is obvious. Simplification, however, should be avoided. It is equally true that developing countries may benefit from private and public research expenditures in developed countries by profiting from spillover effects or specifically addressing problems in the developing world. The FAO (2009A) provides figures for the same year 2000, which are slightly different with total R&D expenditures of 41 billion USD, of which 59 percent are public and 41 percent are private expenditures.

In real terms, global public spending for agricultural R&D has slowed down in recent years and, effectively, decreased by 0.6 percent per year in the 1990s (PARDEY AND PINGALI, 2010). While this was a specific problem in developing countries, no major reduction took place in developed countries during this time period (PIESSE AND THIRTLE, 2010B). In these countries, however, a change of priorities can be observed with reduced funding for applied agricultural research (LEAVER, 2010). More emphasis has been given to the research on environmental effects of agricultural production and food quality (PARDEY AND PINGALI, 2010)

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addressing dietary patterns, enhancing certain attributes to food and food production systems (PARDEY, ALSTON AND PIGGOTT, 2006) and addressing consumer and social values (VEEMAN, 2008). Less importance, on the other hand, has been given to productivity-oriented research. Hence, there has been a general shift from cost-reducing and productivity-enhancing research towards more basic research and public interest research (PIESSE AND THIRTLE, 2010A).

Private agricultural R&D expenditures have, traditionally, been more productivityoriented than public research spending (PARDEY, ALSTON AND PIGGOTT, 2006; CHAVAS, 2008). In this context, a more recent shift towards breeding and varietal technologies can be observed (PIESSE AND THIRTLE, 2010B) mainly driven by the development of genetically modified (GM) crops and more concrete by biotechnological developments such as tissue culture techniques, marker-assisted breeding and genomics, recombinant, cloning and transformation techniques (SUSLOW, THOMAS AND BRADFORD, 2002). Hence, private research has been shifting away from mechanical and chemicals research agendas with an increasing focus on breeding (PARDEY, ALSTON AND PIGGOTT, 2006; PIESSE AND THIRTLE, 2010B).

Figure 10 shows the shares of private R&D activities of major agro-technology firms in 2006. While chemicals research still holds the highest share with 29.4 percent, seeds research (including biotechnology-engineered seeds research) already accounts for 26.3 percent leaving a share of 44.3 percent for all other research activities.

Figure 10. Private sector firms and R&D expenditures by type of research activity, 2006 Other research (including machinery, animal health, fertilizer, animal feed, and animal genetics) (44.3%)

Chemicals research (29.4%)

Seeds research (including biotechnologyengineered seeds) (26.3%) Source: Own figure based on PIESSE AND THIRTLE (2010B).

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R&D and the corresponding technological change in agriculture is generally considered as ‘the’ driver of past productivity growth (CHAVAS, 2008; EWERT ET AL., 2005; RUNGSURIYAVIBOON AND LISSITSA, 2006; VEEMAN, 2008). Much of the land and labor productivity increases observed has resulted from crop nutrition (in particular nitrogen fertilizer), crop protection, and modern varieties (see, e.g., CHAVAS, 2008; HAFNER, 2003; JAGGARD, QI AND OBER, 2010).

In the crop breeding sector, the development of hybrids and semi dwarf varieties contributed a lot to productivity growth. Semi dwarf varieties (POLLOCK, 2011), especially in wheat and barley production (WEBB, 2010), e.g., contributed to a more efficient partitioning of dry matter in yield organs of a plant, thus, increasing the harvest index (SHEARMAN ET AL., 2005; FISCHER AND EDMEADES, 2010). The use of GM crops, e.g., improving insect resistance (Bt) or increasing herbicide tolerance (glyphosate) (HUFFMAN, 2009; POLLOCK, 2011) contributes to reduce production cost (BROOKES AND BARFOOT, 2008; KAPHENGST ET AL., 2011; PARK ET AL., 2011;) and, thus, to increase income (FEDEROFF ET AL., 2010) and also to positive yield effects (BROOKES AND BARFOOT, 2008; FISCHER, BYERLEE AND EDMEADES, 2009; GOMEZ-BARBERO AND RODRIQUEZ-CEREZO, 2006; KAPHENGST ET AL., 2011; PARK ET AL., 2011).

Non-breeding R&D activities, which can be summarized to agronomy activities, also have played an important role for agricultural productivity increases.

HUFFMAN (2009) and BERRY ET AL. (2010) highlight tilling practices, better farm management and equipment, pest control by herbicides and integrated pest management, higher rates of fertilizer application, better moisture management, improved crop rotations, and outsourcing possibilities. POLLOCK (2011) points to increased mechanization, improved precision of operation, and a wider range of options for pest and disease control. According to NOSSAL AND GOODAY (2009), machinery and equipment improvements have also contributed a lot to the recent productivity growth.

From a scientific point of view, the assessment of productivity effects of R&D expenditures and new technologies is important, but exaggerations should be avoided and analysis should be serious. Sometimes a partial look on productivity effects results in misleading high contributions of specific technologies neglecting a systemic view. To give an example: Recently, the contribution of nitrogen fertilization to the crop productivity level in Western Europe was estimated to be about 40 percent (KÜSTERS, 2009), whereas from a global point of view the nitrogen fertilizer use would contribute up to 50 percent of the production level (DAWSON AND HILTON, 2011). Crop protection products, on the other hand, were claimed to contribute up to 50 percent of production (SPINK ET AL., 2009; TECHNOLOGY STRATEGY BOARD, 2009); azole-based fungicides alone are estimated to contribute

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10-20 percent to the yields in EU member states (SCHMITZ ET AL., 2011). New machinery technologies and farm management practices have certainly contributed to current yield levels, too. Finally, it is argued that half of the productivity development in the past is attributed to genetics (HUFFMAN, 2009; POLLOCK, 2011); and MACKAY ET AL. (2009) even emphasize that over the last 25 years approximately 90 percent of the yield increases in cereals can be explained by new varieties.

Summing up such figures, we would end up with an overall aggregated contribution to crop yield levels and developments of well above 100 percent and, maybe, topping 200 percent! Apparently, a simple aggregation of such figures would be incorrect and misleading as such effects are all interrelated. Hence, the 90 percent yield impact of new varieties as mentioned above would not have worked without crop protection and fertilizers to achieve such an increase. A holistic view is necessary to assess partial contributions to an overall productivity impact.

With respect to livestock, HUFFMAN (2009) points to the importance of genetic improvements for productivity increases, e.g. cross-breeding. In addition, nonbreeding activities considerably contribute to productivity increases like improved and/or preventive disease control and management, artificial insemination, large units of stocks, and automated feeding and milking. THORNTON (2010) equally underlines the importance of cross-breeding and artificial insemination; furthermore, he points to a more focused selection on objective traits, more efficient statistical methods, more efficient drugs and vaccines, enzymes added to feed, improved diagnostic tools and health services. The relevance of bought-in feed is accentuated by POLLOCK (2011).

Closely related to R&D expenditures as an obvious determinant of technological change are the concepts of technical efficiency and economies of scale (LATRUFFE, 2010, VEEMAN, 2008). Productivity increases may not only occur due to new technologies and technological shifts, but also due to efficiency gains and economies of scale under a given technology. NOSSAL AND GOODAY (2009) argue that the adoption of new technologies crucially depends on the knowledge of farmers, the structure of the farming sector and, in particular, the exit of less productive farms (see also OMER, PASCUAL AND RUSSELL, 2007). LATRUFFE (2010) underlines efficiency increases in farming due to education, extension, and knowledge transfer; MULLEN (2007) also points to the importance of efficiency gains and scale economies, but in particular stresses education and infrastructure, microeconomic reforms, and spill-overs as key determinants for productivity increase. Knowledge and education besides capital is particularly addressed by POLLOCK (2011).

In contrast to changes in technical efficiency, economies of scale are less analyzed as determinants of productivity increases (see, e.g., LATRUFFE, 2010; STEWARD VEEMAN AND UNTERSCHULTZ, 2009). Generally, economies of scale are more

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expected in livestock than in crop production (CHAVAS, 2008; STEWARD, VEEMAN 2009; VEEMAN, 2008).

AND UNTERSCHULTZ,

Extension (HUFFMAN AND EVENSON, 2006) and education (POWLSON ET AL., 2011) have been highlighted as key factors to improve technical efficiency and use economies of scale. Both factors enhance information flows that are useful to adopt new technologies (see, e.g., NOSSAL AND GOODAY, 2009). SHENG, GRAY AND MULLEN (2011) argue that investment in extension has been more important for productivity development in agriculture than investment in R&D. Apart from technology, the socio-economic, cultural and political frameworks are key determinants for productivity increases, though the impact of these factors is less straightforward and visible, and analyses of such impacts are still limited.

Institutions count when economic change is considered, and the same is true for technological change and productivity increases. The importance of institutions and productivity shifts under the transition process in China is underlined by PARDEY (2011). LICKER ET AL. (2010), in particular, analyze the collapse of the Soviet system; and ALSTON AND PARDEY (2009) show that institutional changes in China, the former Soviet Union and Eastern Europe had substantial impacts on agricultural productivity; they call it a ‘one shot supply stimulating effect’ (see also DE WIT, LONDO AND FAAIJ, 2011).

LIO AND LIU (2008) particularly point to the importance of the policy framework and governance for improving productivity in agriculture. Also the relevance of markets and trade is emphasized. The positive effects of trade openness on farming efficiency and productivity are underlined by HASSINE AND KANDIL (2009). Equally, HEADEY, ALAUDDIN AND PRAJADA RAO, (2010) stress the importance of market interactions and investments driven by prices and competitiveness (see also FISHER, BYERLEE AND EDMEADES, 2009; NOSSAL AND GOODAY, 2009). The policy framework may also be responsible for slowing down productivity increases. Some agri-environmental measures have been claimed to reduce productivity increases (DARNHOFER AND SCHNEEBERGER, 2007; FINGER, 2010). Besides, subsidies in agriculture may have an impact on productivity. While the current (decoupled) direct payments under the Common Agricultural Policy (CAP) are not expected to have significant production or productivity effects, some authors argue that reduced subsidies might enhance the performance of farms and lead to higher productivity levels (see, e.g., ZHU AND LANSINK, 2010).

Finally, the regulatory framework probably has a considerable impact on productivity. PARDEY AND PINGALI (2010) argue that the regulatory framework generally has an influence on technology releases and uptakes by farmers, thus, influencing the productivity level. NOSSAL AND GOODAY (2009) claim that

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microeconomic reforms and the abolishment of production constraints have contributed to productivity shifts in Australian agriculture. The role of the regulatory framework for productivity increases in the EU agriculture is particularly discussed with respect to GM crops. Whereas a cost reduction effect of genetically modified crops is widely accepted (see, e.g., FRANKE ET AL., 2011; GOMEZ-BARBERO AND RODRIQUEZ-CEREZO, 2006; PARK ET AL., 2011), there is increasing evidence that positive impacts on yields are achieved (KAPHENGST ET AL., 2011). Hence, this regulatory framework most likely hampers productivity increases (HUFFMAN, 2009). FEDEROFF (2010) claims that the current GM policy in the EU remains restrictive and increases the costs of seed production and limits its use in agriculture. In this context, PARDEY AND ALSTON (2010) argue that market access costs may be high due to a restrictive regulatory framework. This may lower private R&D investments (PARDEY AND PINGALI, 2010).

The role of product and factor prices for technological change has been discussed in the literature with some widely accepted hypotheses on the one hand and some more vague hypotheses on the other hand. The idea of ‘induced innovation’ was examined and presented and by BINSWANGER (1974) and HAYAMI AND RUTTAN (1985) (see CHAVAS, 2008; LANGTHALER, 2008). Accordingly, technological change is driven by scarcity of factors and, thus, by high factor prices. If land is scarce and, hence, land prices are high, like in the historical Indonesian rice sector, technological change will lead to increased land productivity, and we will observe an increased capital and labor intensity with respect to land. This is the famous example of the terrace farming system on Bali. If, on the other hand, labor is expensive and land abundant, like in the historical Northern American context, the technological change induced will be labor saving and lead to higher labor productivity and higher capital intensity with respect to other factors. In such a context, the combined harvester has been introduced and the tomato harvester was invented.

The idea of ‘induced innovation’ can be discussed in the broader context of neutral or non-neutral technological change as introduced by HICKS (1932). Accordingly, neutral technological change leads to increased productivity leaving factor intensities constant, whereas non-neutral technological change leads to a change in factor intensities. In figure 11, the isoquant I shows the same production level for various land and labor combinations according to a given technology. In this case, the ratio of factor prices determines the factor intensity as indicated by tan(α).

Technological change shifts the isoquant to the origin, i.e. the same output quantity can be produced with less inputs. For neutral technological change, according to isoquant I‘, the factor intensity remains constant; in the case of labor saving technological change, according to isoquant I’’, technological change leads to

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increasing land intensity of labor. Hence, we observe an increasing land (and capital) intensity of labor in the agricultural sector.

Figure 11. Technological change and factor productivity Land

I’’

Isoquant I I’ Factor price ratio 



wage rent

 Labour

Source: Own figure.

While the role of factor prices for technological change seems to be rather straightforward, the role of product prices for technological change has been less examined though some hypotheses on (product) price-induced technological change are worth being discussed (see, e.g., KIRSCHKE AND SCHAPS, 1988). According to the classical ‘infant industry argument’ (CORDEN, 1997), a temporary protectionist policy and, hence, high prices may lead to increased human capital and knowledge about new technologies. There may be other ways to increase human capital in an economy, but such an impact of high prices is conceivable.

Another hypothesis suggests that high prices improve the profitability of R&D expenditures and/or investment in new technologies, thus, increasing incentives for innovation. It seems to be obvious, e.g., that high milk prices in the EU have stimulated innovations in the dairy sector leading to high-yielding dairy cows and machinery such as milking robots and rotary milking platforms. The opposite hypothesis is that high product prices allow for some reduced efficiency in production and lower the incentives for innovations. The high machinery equipment in West German agriculture even on small farms has often been blamed, but it may

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just reflect income potentials due to the protectionist price policy in the past.

According to this argument, high product prices could reduce productivity increases. In fact, there is little analysis and empirical evidence on the role of product prices in agricultural development.

A final hypothesis on the role of product prices for productivity changes relates to structural change in agriculture. Structural change may be hindered by either too high or too low product prices. In the case of high product prices, inefficient farms can survive in markets and keep land not allowing efficient farms to grow.

Hence, productivity change is lower due to a lower structural change. On the other hand, low product prices provide low incentives for efficient farms to grow and invest even when inefficient farms stop production. Structural adjustment is hampered and productivity increases of the sector are lower as compared to a smooth structural transition process with appropriate product prices. This is a tempting and plausible argument though it will be difficult to assess an appropriate product price level for agricultural structural change. The argument has been discussed for structural change in protectionist agricultural systems like in the EU (HENRICHSMEYER AND WITZKE, 1991), but empirical analysis and evidence is low.

In view of the various determinants of technological change and productivity increase, the slowing down in productivity growth as recently discussed may be attributed to some key developments. With respect to R&D expenditures, the general slowing down of public funding and its new orientation towards environmental objectives and addressing consumer and social values has reduced productivity-oriented research. Furthermore, the shift in private breeding activities towards GM crops has led to a certain neglect of other breeding. With respect to the policy and regulatory framework particularly in the EU, two main arguments are discussed: Specific agri-environmental measures constrain the use of inputs and, thus, productivity growth (DARNHOFER AND SCHNEEBERGER, 2007; FINGER, 2010), and the specific GM policy hinders the use of potentials of new varieties (HUFFMAN, 2009; FEDEROFF, 2010). Finally, from a global point of view, the limited land availability, the increasing cultivation of marginal land, and the problem of soil erosion have a negative impact on productivity growth (EICKHOUT ET AL., 2008; LAMBIN AND GEIST, 2006).

Productivity changes translate themselves into production and supply effects of firms and on markets. For profit maximizing farms in competitive markets, the product price equals marginal cost of production, and, hence, the supply curve on a market is given by the marginal cost curve of a firm or the aggregated marginal cost curve of all suppliers in a market. Productivity increases from technological change reduce unit and marginal cost of production and can be visualized as a

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supply curve shift (JECHLITSCHKA, KIRSCHKE AND SCHWARZ, 2007). In figure 12, technological change leads to a shift of the supply curve from S to S’.

The area under the supply curve is the (variable) cost of production. The technological change leads to a new level of cost of production as indicated by the two supply curves. To better understand the cost effect of technological change, this effect can be described in two steps. As a consequence of technological change, the same output quantity q1 could be produced with fewer resources yielding a costsaving effect equal to area A in the figure. Due to the lower marginal cost, the production becomes more profitable, and the optimal production output q2 after technological change requires additional resources and increases the cost of production compared to the output q1, which is indicated by the area B. Hence, the overall cost effect of technological change may be positive or negative depending on the ‘cost saving’ effect and the ‘production expansion’ effect.

Figure 12. Technological change, supply curve shift and production and cost effects Price

S p1

S'

D C A B

q1

q2

Quantity

Source: Own figure.

The income effect of technological change can be indicated in figure 12 using the producer surplus concept. Before technological change, producer surplus is given by the area C to the left of the supply curve S; technological change leads to an increase of producer surplus as given by the areas A and D to the left of supply

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curve S’. It is well-known from the Green Revolution discussion that high-yielding varieties may enhance the input use, e.g., in rice or wheat production.

The implications of technological change and a supply curve shift on a market depend on demand and the policy framework. In figure 12, a constant product price was assumed that could reflect a free trade market framework for a small country, for which the world market price is given and which is the domestic price in the country, or it could reflect a price policy in a country setting a constant product price. If we consider technological change from a global point of view, supply curve shifts lead to an interaction between supply and demand. Figure 13 shows a shift in the global supply curve from S to S’ and a given global demand curve D. In this case, technological change results in a higher quantity produced (and consumed) q2 and a lower (world) market price p2.

Figure 13. Technological change and global market effects Price

S p1 p2

A

S'

B C

D q1

q2

Quantity

Source: Own figure.

Under these market conditions, the production expansion effect and the corresponding cost effect is lower than in figure 12 due to the shape of the demand curve and the corresponding elasticity of demand. Depending on the demand elasticity, a price decreasing effect has to be taken into account for an overall assessment of market effects of technological change. Obviously, the price decreasing effect of technological change will be the higher, the lower the (absolute) demand elasticity

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is. Rather ‘inelastic’ demand is a particular feature for agricultural commodities; hence, such a price-decreasing effect of technological change is an important effect to be considered.

As a consequence of this price effect, technological change will definitely benefit the consumers. Consumer surplus as a (real) income indicator for consumers can be visualized as the area under the demand curve and the price line. The figure shows that consumer surplus grows as a consequence of technological change by the area A and B. This positive effect of technological change on consumer welfare has been widely discussed and documented in the literature.

From the producer point of view, the overall welfare impact is less obvious though technological change should improve producers’ income as discussed above.

However, the income-enhancing effect of reduced production costs is contrasted with an income-reducing effect of the lower product price. In figure 13, the loss of producer surplus compared to the situation before technological change is indicated by area A, whereas the gain in producer surplus is indicated by area C. The net effect needs to be calculated. This may be a surprising result, but simply shows that technological change leads to an overall welfare gain in an economy, whereas the distributional implication depends on demand and/or the policy framework (JECHLITSCHKA, KIRSCHKE AND SCHWARZ, 2007).

Looking at the producers in particular, we may distinguish between different groups such as ‘small’ or ‘large’ farmers or ‘adopters’ or ‘non-adopters’ of new technologies. In this case, the ‘adopters’ face the income-enhancing effect of technological change due to cost reduction and the income reducing effect due to the price decrease as discussed. On the other hand, the ‘non-adopters’ simply face an income loss due to the reduced price since their production technology does not improve.

This is an apparent result and needs to be considered when assessing new technologies since there will always be ‘losers’ due to technological change. There has been a long and controversial discussion on distributional effects of the Green Revolution, whereas the core distributional problems of new high-yielding varieties in this case or for any new technology in general are obvious. Just to underline the negative income effects of technological change or simply to deny them does not contribute much to the discussion. The question is not ‘that’ technological change may have negative income implications for some groups, but ‘how’ the implementation of technological change should be managed within an accompanying policy framework to avoid or reduce such negative effects.

The cost and distributional implication of productivity changes are closely linked to the competitiveness of a firm in a market. Broadly speaking, a firm is competitive if its cost structure in production allows for profits and survival in a market. If the

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unit costs of production can be reduced due to technological change this will enhance competitiveness with respect to other firms who cannot benefit from productivity improvements. We will observe that the market share of such a firm with improved competitiveness will increase, whereas market shares of noncompetitive firms will decrease and firms will drop out of the market. In the long run, the cost level of the most competitive firms will drive the price level in a market and supply will be dominated by these firms (PINDYCK AND RUBINFELD, 2009).

The idea of the competitiveness of a firm may be translated to the competitiveness of a sector in a country compared to international competitors. Improved competitiveness, then, leads to an improvement in the trade situation either reducing imports (import substitution) or expanding exports (export enhancement). Such market or country level implications of improved productivity may be the result of firm activities to increase productivity and improve competitiveness or they may be driven by specific policies to strategically improve a county’s competitiveness in international markets.

5 Global perspectives and the role of productivity Productivity plays a major role for world agricultural markets. In the past, high productivity increases led to supply increases higher than demand increases resulting in a long-term trend of (real) price decrease (see, e.g., TYERS AND ANDERSON, 1992). This is the background against which the disparity problem in agriculture has been discussed and protectionist policies have been implemented for many developed countries like the EU leading to the Uruguay Round and the current negotiations in the World Trade Organization (WTO) context.

The situation, however, is about to change in the near future as indicated by the high world market price levels of 2007/08 and the recent price increase on agricultural markets. Figure 14 shows the evolution since 1990 as calculated by the FAO’s Food Price Index and the recent Food Commodity Price Indices. The Organization for Economic Co-operation and Development (OECD) and FAO as well as the Food and Agricultural Policy Research Institute (FAPRI) expect a continuously increasing price trend for many agricultural commodities in the future (OECD and FAO, 2011; FAPRI, 2011).

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Figure 14. Nominal prices on world agricultural markets, 1990-2011 FAO Food Price Index

Food Commodity Price Indices 2002-2004=100

2002-2004=100

250

425 Sugar

210 350 Oils & Fats

170 275

Cereals

130 Dairy 200 90 Meat 125

50

J FMAM J J A S OND J FMA

90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11

2011

2010

Source: Own figure based on FAO (2011B).

Figure 15 visualizes the so-called world food equation meaning that agricultural supply Qs has to meet four components of demand Qd: food, feed, fiber, and fuel. In the first decade of the new century, three major driving forces have been discussed in the literature, which are shown by numbers 1, 2 and 3 in the figure.

Figure 15. Major drivers on world food markets and the world food equation Demand in emerging markets

Productivity growth

2

1

S

Q

=

Q

D Food

4 Climate change

+

5 Carbon storage

Source: KIRSCHKE (2011A; B).

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D Feed

+ 3

Energy price

D Fiber

Q

+

Q

D Fuel

6 Bioenergy policy

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A first important force is demand in emerging economies, in particular in Asia.

Annex 15 shows the expected world population growth until 2050. Following the FAO projection more than 9 billion people will have to be fed by that year, and most of the population growth will take place in developing countries. Annex 16 shows the world income growth until 2050, and again, most of the income growth will take place in the developing world. The income growth rate is much higher in developing countries than in high-income developed countries; from a current level of more than 6 percent per year, the growth rate in this country group will only slowly decrease until an estimated 4 percent growth rate in 2050. Hence, both population growth and income growth in developing countries will drive the evolution on the world markets. FAO estimates that the additional food demand will be around 70 percent by 2050 compared to 2010 (FAO, 2009B).

Agricultural productivity growth will be a second major driver on world agricultural markets. Though a clear picture is still missing, there is a widespread fear that the high productivity increases of the past might have come down in recent decades (see the discussion above). The World Bank has initiated the debate by revealing in its 2008 World Development Report a decreasing trend of annual growth rates of yields for major cereals in developing countries from 1960 to 2005 as shown in annex 17. The figure illustrates the high growth rates of the 1960s, the Green Revolution effects and the slowdown of productivity increases since then. In the last decade, yield increases have come down to an annual growth rate of about 1.5 percent.

The apparent slowdown of land productivity growth in many developing countries as in many developed countries may reflect an increasing scarcity of land and water resources, but it may also reflect a decreasing capital intensity in world agriculture. In fact, investments in agriculture have rather been neglected in recent decades as discussed in the literature and above. Figure 16 shows the annual growth rates in agricultural R&D for various regions of the world since the 1970s. Accordingly, the high growth rates of the 1970s have come down in all the regions considered to less than 2 percent per year in developing countries and less than 1 percent per year in high-income developed countries.

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Figure 16. Annual growth rate in agricultural R&D, by geographic area, 1976/81-1991/2000

Annual growth rate, in percent

10 8 6 4 2 n.a.

0

Sub-Saharan Asia & Pacific Latin America West Asia & Africa & Caribbean North Africa

-2

1976-1981

1981-1991

Developing countries

High-income countries

1991-2000

Source: Own figure based on FAO (2009A).

The development on the supply and demand side of the world food equation is exacerbated by the energy price development. A high energy price increases cost of production and has a negative effect on supply, while it will enhance the demand for bioenergy on the demand side with a high oil price making the use of bioenergy more attractive. Both effects on supply and demand will lead to an increase of agricultural prices. In fact, a close correlation between energy and agricultural prices has been stated in the literature (see, e.g., ABBOTT, HURT AND TYNER, 2009; BALCOMBE, 2009) and is illustrated in annex 18.

The world food equation (see figure 15) can be translated to a simple formula as given in equation (7).

(7) T  Q

s

 p

 POP  Q d  p, y 

with T – technology factor, Qs – supply quantity, p – price, POP – population factor, Qd – demand quantity, and y – income.

Deriving the rates of change and solving for dp/p yields:

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(8)

dp 1  s   d p

35

dy   dT dPOP   T  POP   y   

with εs – supply elasticity, εd – demand elasticity, and η – income elasticity.

According to this equation the price change on the world market will depend on productivity change compared to population and income growth. The resulting net quantity change in brackets may be positive or negative bringing the world market price up or down. Due to the rather low price elasticities on agricultural markets, the price change effect will be higher than the quantity change effect, e.g. by the factor 2 for a supply elasticity of 0.25 and a demand elasticity of – 0.25.

The world food equation can be used to point to problems in future development as discussed above. A rather modest population growth of 1.0 percent and an income growth of just 3.0 percent, which is well below actual and future trends, would translate themselves, for an income elasticity of η = 0.5, to a demand growth of 2.5 percent. Hence, productivity would have to grow by 2.5 percent to keep the world market price level constant. For a productivity growth of 1.5 percent, there is a gap of 1.0 percent leading to a price increase by about 2.0 percent given the low price elasticities on agricultural markets. Such simple calculations can be used to illustrate structural changes on world agricultural markets and to assess the role of productivity changes in this context.

The world food equation has to be further elaborated today in view of the challenges ahead, which are shown by numbers 4, 5 and 6 in figure 15. Climate change may have an impact on agricultural supply, but the overall implications are difficult to assess based on the information available. A negative impact on agricultural production is postulated for many developing countries with marginal production conditions, whereas other regions like Europe and northern countries might actually benefit from more favorable climatic conditions (FISCHER ET AL., 2005; MÜLLER ET AL., 2009; NELSON ET AL., 2009; WORLD BANK, 2009). If the overall production effect is negative, climate change will result in a leftward shift of agricultural supply, and this would result in a corresponding assessment of price changes on world agricultural markets. Apart from such production effects, climate change is generally expected to result in higher price volatility on world markets (GILBERT AND MORGAN, 2010; NOLEPPA ET AL., 2010). However, it is difficult to assess potential impacts on production due to increased uncertainty.

Impacts on agricultural production may also occur due to higher land demands for environmental and climate protection. While the direct competition between food production and nature conservation is obvious, agriculture is not yet fully integrated in mitigation activities. For European agriculture, the GHG emission

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mostly results from methane and nitrous oxide and carbon dioxide from organic soils (LESSCHEN ET AL., 2011; WEF, 2010). Figure 17 gives an impression on methane and nitrous oxide emissions from agriculture in European regions. The figure shows a concentration of emissions following high-intensive agriculture and livestock concentration. A corresponding mitigation policy would, thus, have an impact on regional production structures in European agriculture and other regions of the world. To what extent a resulting production shift would occur for crops and other agricultural commodities remains an open question.

Figure 17. Emissions of CO2-eqivalents in EU agriculture

Source: LESSCHEN ET AL. (2011).

A specific challenge for the future will be the bioenergy policy in Europe and other world regions. Despite much criticism due to high mitigation costs in bioenergy production (WISSENSCHAFTLICHER BEIRAT AGRARPOLITIK, 2011; 2007), the political support has become a major perspective for agriculture. Annex 19 shows the expected world bioethanol and biodiesel projections until 2018. Compared to 2005, bioethanol production is expected to grow by 300 percent and biodiesel production even by 700 percent. Today, bioethanol demand accounts for 6 percent of world cereals demand (DBV, 2010). In Germany, electricity production from biogas installation receives particular support and has expanded drastically in recent years (HABERMANN AND BREUSTEDT, 2011).

Many bioenergy potential calculations can be found in the literature with results from a rather optimistic to a pessimistic view according to HABERL ET AL. (2010).

The key questions, of course, are what land use requirements bioenergy production

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will have in the future and what the conflicting implications might be for food production. Scientists from the Potsdam Institute for Climate Impact Research (PIK) have tried to condense the information available and to provide global bioenergy potentials by 2050 for various scenarios (BERINGER, LUCHT AND SCHAPHOFF, 2011). In particular, the corresponding land requirements are calculated and a high-level technology of the second generation is assumed to produce bioenergy on dedicated biomass plantations. The results are shown in figure 18.

Figure 18. Global bioenergy potentials and corresponding land requirements for dedicated biomass plantations in 2050 for various scenarios

Plantation area (Mha) 600

400

400

2050

Energy potential (EJ yr-1) 25

142 F1C1 26

50

75

52

F1C1 scenario: F1C2 scenario: F2C1 scenario: F2C2 scenario:

125

150

175 Rainfed Irrigated

334 F1C2

65

111

262 F2C1

68

105 116

454 F2C2 Range of predicted cropland expansion

100

Current global bioenergy supply

174 50% of year 2000 HANPP

Cropland expansion for food production by 120 M ha, higher nature conservation s.a., lower nature conservation No cropland extension, productivity increase by 1.2% p. year, higher nature conservation s.a., lower nature conservation

Source: Figure adapted from BERINGER, LUCHT AND SCHAPHOFF (2011).

The figure shows the current energy supply from biomass that amounts to 50 EJ per year or about 10 percent of overall primary energy use. This bioenergy contribution mostly comes from traditional bioenergy production like burning wood. By 2050, an additional area of some 400 million hectare could be used for food production, nature conservation and/or biomass plantations, whereas the world’s arable land area today is about 1 400 million hectare (FAO, 2011C). Four scenarios are considered. Under scenario F1C1, crop productivity would stagnate requiring cropland expansion for food production by 120 million hectare. With higher nature conservation, there is no potential for increased bioenergy production even under intensification using irrigated land.

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Lower nature conservation as under scenario F1C2 or a (land) productivity increase by 1.2 percent (scenario F2C1), which corresponds to the productivity increases observed today, allows for higher bioenergy production potentials.

According to the figure, this bioenergy potential could double compared to today.

The most optimistic scenario from a point of energy production would require a 1.2 percent productivity increase per year and lower nature conservation (scenario F2C2). In this case, bioenergy production could triple to about 150 EJ per year by 2050 which would be 50 percent of today’s bioenergy production and cover only about 15 percent of the 2050 energy demand (IEA, 2009). The message is clear: Increased bioenergy production, even by most efficient technologies, has high opportunity cost of food production and nature conservation, whereas productivity increases would reduce, but not abolish this conflict.

Integrating climate change, environmental and climate protection and bioenergy production into the world food equation would affect land use and the equilibrium condition. A ‘new’ world food equation can be defined as follows: (9) T  Q s  p  / CC / ECP  POP  Q d  p, y   BE with CC – climate change (production effect), ECP – environmental and climate protection (production) effect, and BE – bioenergy policy (production) effect.

Now, the rate of change for the world market price can be calculated as follows: (10)

dp p



dY dCC dECP dBE   dT dPOP        POP Y CC ECP BE  s  d  T 1

According to this equation, climate change and/or environmental and climate protection and/or bioenergy policy would result in a leftward shift of supply and/or rightward shift of demand. This would result in a corresponding price increase. To give an example: If world bioethanol production amounts to 6 percent of current world cereals’ supply, this translates itself to a (structural) price increase of about 12 percent using price elasticities as assumed before. If, furthermore, the US corn bioethanol program uses 30 percent of the US maize production, which is 40 percent of world supply, the additional demand shift of this program will amount to 12 percent of the world corn market, and the corresponding price increase would be about 25 percent. It is not surprising that new requirements or constraints for biomass production would affect the world food equation and that the overall effect of the challenges discussed will necessarily lead to a more accentuated conflict between food and bioenergy production and/or nature conservation. Again, productivity increases can help reduce this conflict.

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The new perspectives on world food markets have been analyzed in recent years providing a rather comprehensive view on the challenges ahead. The picture is straightforward and underlines that increasing prices will set the framework for world agricultural markets in the future (see, e.g., FAPRI, 2011; OECD and FAO, 2011; ROSEGRANT ET AL., 2009; TWEETEN AND THOMPSON, 2008) and that productivity increases and technological innovations will have to be assessed in this new global framework. The new competition for land with respect to food, energy and climate change is well-characterized by the term ‘trilemma’ (HARVEY AND PILGRIM, 2011; TILMAN ET AL., 2009).

The key message is simple: To keep prices constant, future production growth has to meet the future growth of demand and today’s productivity growth does not fill the gap (JAGGARD, QI AND OBER, 2010; ROYAL SOCIETY, 2009). Some authors argue that an additional global land productivity growth of 0.3-0.4 percent would meet this challenge (FISCHER, BYERLEE AND EDMEADES, 2009; GLOBAL HARVEST INITIATIVE, 2010). Certainly, this is an ambitious challenge and would require ‘breakthrough innovations’ (JAGGARD, QI AND OBER, 2010).

6 Technological options for productivity increases Technological progress played a key role to match agricultural supply and demand in the past and will have to do so in the future. In chapter 4, we have discussed the key determinants for productivity growth and we have underlined the role of R&D. R&D has contributed to a large extent to improved breeding such as high-yield varieties and to improved agronomic practices and farm management. The availability of biotechnologies, herbicides, low-cost fertilizers and reduced or no-till farming practices have been identified as major technology improvements in this respect.

Future technological progress and productivity increases, too, are widely expected and demanded, respectively, (see, e.g., FISCHER, BYERLEE AND EDMEADES, 2009; FORESIGHT, 2011; LEAVER, 2010) and are attributed to improved breeding activities and improved agronomic practices and farm management. When discussing technological options, e.g., ROYAL SOCIETY (2009) distinguishes genetic from phenotypic research and technology, i.e. technologies targeting the genome or the biochemical, physiological or morphological characteristics of agricultural species, respectively.

Also, SPINK ET AL. (2009) differentiate genetic improvements from improved management practices; and so do HUFFMAN (2009) by emphasizing ‘genetic’ and ‘nongenetic’ factors and JAGGARD, QI AND OBER (2010) by highlighting further develop-

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ments in genetics and agronomy. EWERT ET AL. (2005), FISCHER AND EDMEADES (2010) and FINGER (2010) argue similarly.

Following this discussion, we have tried to condense the recent scientific discussion on technological options in agriculture by grouping the numerous proposals under breeding (genetic) options and agronomic (non-genetic) options. Figure 19 summarizes frequently quoted technological options which are expected to contribute to major productivity increases in the future. The box is divided into sub-boxes trying to distinguish some technology clusters in more detail. The differentiation is certainly arbitrary as technologies cannot always be considered independently and clusters are not always definite, but should help to get a better comprehensive view.

Accordingly, we distinguish options ‘mainly related to conventional breeding’ from options ‘mainly related to GM breeding’ under breeding (genetic) options and options ‘mainly related to nutrition’ from options ‘mainly related to pests and diseases’ and options ‘mainly related to soil management’ under agronomic (nongenetic) options.

In particular, new breeding technologies are expected to offer an effective approach for enhancing agricultural productivity (TESTER AND LANGRIDGE, 2010). FISCHER, BYERLEE AND EDMEADES (2009), FAO (2009B), TECHNOLOGY STRATEGY BOARD (2009), and WEBB (2010) believe that breeding will play the most important role for future productivity developments, and this is true both for conventional and GM breeding activities though a clear distinction sometimes cannot be made. The development of GM crops is certainly an emerging technology for crop breeding with the potential to significantly increase agricultural productivity in the future (HUFFMAN, 2009; GODFRAY ET AL., 2010, TESTER AND LANGRIDGE, 2010). PHILLIPS (2010), to take one example, argues that corn tolerant to drought due to transgenic technology offers 10 percent higher yields under stress. More traditional and currently widely used and accepted biotechnological breeding methods, however, may also provide promising options offering substantial productivity growth rates.

For instance, if the heterosis effect could be used in wheat and other selfpollinating species, yield increases of 10-20 percent and more may occur (BUENO AND LAFARGE, 2009; FISCHER, BYERLEE AND EDMEADES, 2009; SILES ET AL., 2004, TESTER AND LANGRIDGE, 2010). LEGE (2010) already reports first positive results in wheat hybrid development.

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Figure 19. Breeding and agronomic options for future agricultural productivity increases Breeding (genetic) options

Agronomic (non-genetic) options

Mainly related to conventional breeding

Mainly related to nutrition

 Genome sequencing

         

 Marker technologies (MAS, MARS, genetic mapping and QTL etc.)  Gene pyramiding  Genome-wide/genomic selection  High-throughput genotyping  High-throughput analysis of gene expression  Metagenomics  Complex trait dissection  Precision phenotyping  High-throughput phenotyping platforms  Heterosis for further inbreeding/ self-pollinating species  Expansion of germplasm base in breeding programs  Vegetative/Micro-Propagation  Embryo techniques

   

Delayed release fertilizers Novel formulations and forms of fertilizers High throughput analysis of small molecules Isotopic analysis Upgrading biochemical pathways Molecular biology Molecular engineering for novel enzymes Biosensoring Biochemical engineering Modern biotechnology-based amino acids, vitamins, enzymes etc.

Synthetic biology and metabolic engineering Microbial genomics of the rumen Design of novel feed additives and specifically formulated feed Improved irrigation and intensive hydroponic systems

 Molecular genetics

Mainly related to pests and diseases

 Cloning

 Novel (bioactive) crop protection chemicals

 In vitro sperm production

Mainly related to GM breeding  Perennial crops  Apomixis  (Nutritional) biofortification  Artificial chromosomes  RNA interference  Identification and introduction of novel genes  Targeted gene replacement  Genetic engineering, e.g. approaching traits of drought, high temperature and salinity tolerance and novel parasite derived resistance  De novo synthesis and variations  GM-based conversion to transfer C3 to C4 metabolism  Use of genetic drive techniques

 Modern biotechnology-based diagnostic  New IPM approaches/strategies  Precision farming/controlled release of biological and chemical crop protection agents  Improved (rapid) responses to pests / diseases  Improved control of sporadic pests / diseases  Improved weed detection  DNA fingerprinting for surveillance  Large scale sequencing of environmental samples

Mainly related to soil management  Biological, chemical and optical sensoring and monitoring  Novel rotation and companion planting  Further developed reduced tillage practices  Intercropping  Utilization of biochar to retain nutrients and water

Source: Own compilation based on CABOCHE (2009), FISCHER AND EDMEADES (2010), FISCHER, BYERLEE AND EDMEADES (2009), FORESIGHT (2011), GLOVER ET AL. (2010), GODFRAY ET AL. (2010), HUFFMAN (2009), MIFLIN (2000), PAPATRYFON ET AL. (2008), PENNISI (2010), PHILLIPS (2010), POLLOCK (2011), POWLSON ET AL. (2011), ROYAL SOCIETY (2009), SPINK ET AL. (2009), TECHNOLOGY STRATEGY BOARD (2009), TESTER AND LANGRIDGE (2010), and THORNTON, (2010).

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Agronomic technological options are also seen to have a high potential for future productivity growth in agriculture. SPINK ET AL. (2009) argue that an improved attention to the management and timing of inputs alone could increase wheat yields by 10 percent and oilseed rape yields by 15 percent within 10 years; improved irrigation systems may add additional 5 percent. BIOSCIENCES KTN (2010) state, to take a crop protection example, that novel nematicides are predicted to offset yield losses on treated areas between 20-30 percent and may contribute to reduce production cost and associated negative environmental impacts.

The compilation in figure 19 is neither complete nor indicating priorities and we do not want to speculate about real chances of new technologies and future productivity effects. In fact, the examples for technological options discussed above address specific technologies, but it should be clear that partial effects of a new technology always have to be considered in a system context. In chapter 4, we have discussed that productivity increases can seldom be attributed to a single technology, but rather to a technological evolution of a system. In a similar way, future technological changes will transform the overall agricultural production systems and productivity increases may be induced by partial technology changes, but essentially reflect the overall change of the system. In this context, the overall embedding of technological progress in the ongoing information technology revolution is emphasized.

Against this background, there is no need to say that speculations about future technological changes and productivity increases must be vague. The literature review shows that new technologies with high productivity potentials are in the pipeline, but nobody knows when exactly new technological options will be available or whether they will be available at all. As Niels Bohr said: ‘Predictions can be very difficult – especially about the future’ (see ROSOVSKY, 1991).

Finally, the time horizon and the time lag of R&D activities have to be faced.

Breeding hybrid corn has taken 40 years, semi dwarf wheat 30 years, and semi dwarf rice 25 years in the past to show its productivity effects on farm (PARDEY AND PINGALI, 2010). Generally, technological innovations in agriculture are said to take at least nine years (HUFFMAN, 2009) or approximately 25 years on average according to ALSTON, BABCOCK, AND PARDEY (2010). Private R&D investments are said to start to generate returns after 8-15 years, whereas public R&D spending does so after 15-25 years (CHAVAS, 2008).

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7 Policy implications and options The global development of agricultural supply and demand is setting a new and challenging agenda for future policy-making. We have emphasized that an emerging gap between supply and demand has to be faced squeezing agricultural markets and leading to food price increases. Having in mind agricultural policy priorities and a neglect of productivity orientation in the past, agricultural policymaking for the future will have to rediscover productivity as a key driver and important policy objective in international and European agriculture. Technological improvements and productivity increases are asked for and may help to reduce the agricultural squeeze, and a supportive policy framework is needed for productivity growth. The new policy paradigm does not mean coming back to Malthus (MALTHUS, 1798), but coming to the end of Cochrane’s agricultural treadmill (COCHRANE, 1958).

Global food security will have a high priority on the future agricultural policy agenda. A supportive framework for productivity growth has been emphasized, but there may be more demands on policy-making reducing the gap between supply and demand. All policies reducing agricultural supply and enhancing agricultural demand should be carefully re-evaluated in view of the challenges ahead. This refers, e.g., to the design of agri-environmental policies, the role of feed and animal production, the potential integration of agriculture in mitigation policies and the current bioenergy policy in the EU. An integrative agricultural policy concept is needed to meet diverging policy demands and handle the policy ‘trilemma’.

There is a long-lasting and ongoing debate on ‘intensification vs. extensification’ in agriculture; but neither intensification alone nor simple extensification will meet the challenges ahead and it does not really help to emotionalize and polarize. In view of diverging agricultural policy objectives, conflicts have to be faced anyway.

An appropriate and responsible policy framework should take such conflicts into account and develop a comprehensive and integrated approach minimizing potential negative impacts. The challenge is to ‘do more with less’ (LEAVER 2010; GLOBAL HARVEST INITIATIVE, 2010), e.g., increase production and productivity with no additional or less environmentally negative impacts. The vision is to develop a policy framework for ‘intelligent and sustainable intensification and productivity increases’. This is certainly still a rather vague concept, but the need for such a concept is increasingly being underlined and acknowledged (see, e.g., GODFRAY ET AL., 2010; HARVEY AND PILGRIM, 2011; POLLOCK, 2010; ROYAL SOCIETY, 2009; VEEMAN, 2008).

A particular point for future policy-making is R&D policy. It has been criticized that public R&D expenditures have rather been neglected, and this is in particular

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true for productivity- and technology-oriented research areas. In view of the importance of public R&D expenditures in agriculture and the challenges ahead, there is a definite need to re-evaluate priorities and to redirect this development (see, e.g., NOSSAL AND GOODAY, 2009; VEEMAN, 2008). There are some indications that such a reversal of priorities already occurs in some high-income countries (PARDEY, 2011). It goes without saying that these countries have a particular responsibility for the global agricultural development.

In addition to public expenditure, private agricultural R&D has increasingly gained importance. Multinational firms, e.g., dominate R&D activities related to GM technologies and the public sector is almost excluded from this research area (FEDEROFF ET AL., 2010; NASEEM, SPIELMAN AND OMANO, 2010). This sector development can partly be related to the high costs of developing a commercially viable GM variety and to high and in many cases insurmountable regulatory costs on markets, especially in the EU. Despite, the private investment perspectives are generally positive: According to MCDOUGALL (2010), private firms plan for 2012 to invest 26.4 percent more into agricultural R&D compared to 2007.

Such private activities need a reliable and supportive policy framework. There have been complaints that the regulatory framework in the EU is rather strict and needs adjustment. This would suggest a general re-assessment of this framework for attracting additional private sources in agricultural R&D, especially with respect to emerging technologies such as GM crops. FEDEROFF ET AL. (2010), e.g., call for a re-evaluation and science-based assessment of the regulatory framework in view of accumulated evidence and experiences, and VON BRAUN (2010), particularly, argues that the risk of growing GM crops should be compared with the risk of not growing them. Accordingly, GM crops should neither be privileged nor automatically be dismissed (GODFRAY ET AL., 2010). This would certainly require an improved dialogue between policy-makers, farmers, private R&D actors, and the public (NOSSAL AND GOODAY, 2009).

In view of the prospective global food balance, the EU responsibility has to be addressed. Agricultural productivity increases were high and, in a protectionist policy setting, the view of abundant food and land availability is still widespread.

As a consequence, global agricultural supply and demand developments are rarely considered when discussing European policy issues (KIRSCHKE, 2011A). Today, European and international land use implications of policy changes are rather neglected, e.g., with respect to potential environmental priority areas or with respect to biofuels policies. There is a definite need to raise the awareness on the EU’s international responsibility in agricultural policy-making, in general, and for balancing global agricultural supply and demand and supporting productivity

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growth, in particular. The current situation is astonishing at least in view of the EU being a key global player on agricultural markets.

Looking finally at the CAP, this policy field was only slowly adjusted and integrated into the international framework in the past when the perspective was liberalization, and there is no vision yet for the future policy in view of increasing world market prices and the challenges ahead. The CAP is still struggling with direct payments and the greening of subsidies, but, though policy-makers are talking on future challenges, there is no real effort for a re-orientation. The new policy requirements could be well addressed under the second pillar of the Common Agricultural Policy, and the new perspective for supporting productivity growth should be addressed explicitly.

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ALSTON, J.M.; BABCOCK, B.A.; PARDEY, P.G. (2010): Shifting patterns of global agricultural productivity: Synthesis and conclusion. In: Alston, J.M.; Babcock, B.A.; Pardey, P.G. (eds.) (2010): The shifting patterns of global agricultural production and productivity worldwide. Iowa State University: The Midwest Agribusiness Trade Research and Information Center: 449-482. In: http://www.card.iastate.edu/books/shifting_patterns/ (18.05.11).

ALSTON, J.M.; PARDEY, P.G. (2009): Theme overview: Agricultural productivity and global food security in the long run. In: Choices 24 (4).

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BAILEY, A.P.; BASFORD, W.D.; PENLINGTON, N.; PARK, J.R.; KEATINGE, J.D.H.; REHMAN, T.; TRANTER, R.B.; YATES, C.M. (2003): A comparison of energy use in conventional and integrated arable farming system in the UK. In: Agriculture, Ecosystems and Environment 97: 241-253.

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BALL, V.E.; BUTAULT, J.-P.; SAN JUAN, C.; MORA, R. (2010): Productivity and international competitiveness of agriculture in the European Union and the United States. In: Agricultural Economics 41: 611-627.

BERINGER, T.; LUCHT, W.; SCHAPHOFF, S. (2011): Bioenergy production potential of global biomass plantations under environmental and agricultural constraints.

In: GCB Bioenergy (doi: 10.1111/j.1757-1707.2010.01088.x). In: http://onlinelibrary.wiley.com/doi/10.1111/j.1757-1707.2010.01088.x/pdf (01.02.11).

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Annexes Annex 1.

Land productivity development in maize production in selected EU member states, 1961-2009, in t/ha ...................................... 60

Annex 2.

Land productivity development in rapeseed production in selected EU member states, 1961-2009, in t/ha ...................................... 61

Annex 3.

Land productivity development in wheat production in selected regions of the world, 1961-2009, in t/ha .................................... 62

Annex 4.

Land productivity development in maize production in selected regions of the world, 1961-2009, in t/ha .................................... 63

Annex 5.

Land productivity development in rapeseed production in selected regions of the world, 1961-2009, in t/ha .................................... 64

Annex 6.

Milk yields in selected regions of the world, 1961-2009, in kg/cow/year................................................................................................ 65

Annex 7.

Gross value added in agriculture per annual work unit in EU member states by NUTS 2 regions, 2007, in EUR ........................... 66

Annex 8.

Value added in agriculture per worker in selected EU member states, 1980-2009, in real 2000 USD ....................................................... 67

Annex 9.

Value added in agriculture per worker in selected world regions, 1980-2009, in real 2000 USD ..................................................... 68

Annex 10. Relative gross value added of the agricultural sector compared to the overall economy in EU member states, 2008/2009, in percent ............................................................................... 69 Annex 11. Global land and labor productivity patterns, 1961-2008........................ 70 Annex 12. Total Factor Productivity accounts for a rising share of agricultural growth over time .................................................................. 71 Annex 13. Agricultural output and productivity growth for global regions by decade, average annual growth rate, in percent ................................ 72 Annex 14. Water productivity in wheat production in various countries ............... 73 Annex 15. World population growth, 1950-2050 ...................................................... 74 Annex 16. World income growth, 1950-2050 ............................................................ 74 Annex 17. Annual growth rate of yields for major cereals in developing countries, 1960-2005, in percent ........................................... 75 Annex 18. World market price developments of agricultural commodities and crude oil, 2005-2011, January 2005=100 ......................................... 75 Annex 19. World ethanol and biodiesel production projections, 2005-2018 ............ 76

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y = 3,5183e0,0238x

12

Source: Own figure based on FAO (2011C).

HFFA Working Paper 02/2011 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

12

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Annex 1.

6 6

3 3

0 0

9 9

6 6

3 3

0 0

12

Poland 12

9 9

6

y = 2,3844e0,0211x 6

3 3

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

60 Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

Land productivity development in maize production in selected EU member states, 1961-2009, in t/ha Germany 12

France

9

y = 3,1948e0,0271x

Italy 12

Spain

y = 3,4015e0,0287x y = 2,1847e0,0376x

Hungary

y = 2,2608e0,0443x

4

3

y = 1,7885e0,0157x

5

4

y= 1,4898e0,0117x 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

5

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Annex 2.

Germany

3 3

2 2

1 1

0 0

5

Italy 5

4 4

3 3

2 2

1 1

0 0

Poland

2 2

1 1

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

4

61

Land productivity development in rapeseed production in selected EU member states, 1961-2009, in t/ha

5

France

y = 1,6656e0,0162x

Spain

5

Hungary

4

3

y = 1,1359e0,0128x

Source: Own figure based on FAO (2011C).

HFFA Working Paper 02/2011

3

4

Source: Own figure based on FAO (2011C).

HFFA Working Paper 02/2011 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

6

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Annex 3.

European Union

5 5

4 4

3 3

2 2

1 1

0 0

6

Oceania 6

5 5

4 4

2 2

1 1

0 0

6

Asia 6

5 5

3 3

2 2

1 1

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

62 Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

Land productivity development in wheat production in selected regions of the world, 1961-2009, in t/ha

6

Northern America

y = 2,0406e0,0268x

y = 1,6464e0,0114x

South America

y = 1,1667e0,0079x 3

y = 1,095e0,0172x

Africa

y = 0,9056e0,0273x 4

y = 0,7257e0,0246x

9

12

9

6

y = 1,2357e0,0287x 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

12

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Annex 4.

European Union

6 6

3 3

0 0

Oceania

y = 1,1914e0,0243x

6 6

3 3

0 0

12

Asia 12

9 9

3 3

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

63

Land productivity development in maize production in selected regions of the world, 1961-2009, in t/ha

12

Northern America y = 4,2102e0,0177x

9

y = 2,4474e0,0272x

South America

12

y = 2,2754e0,0253x 9

Africa

6

y = 1,097e0,0106x

Source: Own figure based on FAO (2011C).

HFFA Working Paper 02/2011

Source: Own figure based on FAO (2011C).

HFFA Working Paper 02/2011 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

4

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Annex 5.

European Union

3 3

2 2

1 1

0 0

y = 1,0856e0,0112x

4

Oceania 4

3 3

2 2

1 1

0 0

4

Asia 4

3 3

2 2

1 1

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

64 Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

Land productivity development in rapeseed production in selected regions of the world, 1961-2009, in t/ha

4

Northern America

y = 1,668e0,0137x

y = 0,8653e0,0136x

South America

y = 0,7119e0,0127x

Africa

y = 0,3474e0,0317x

y = 0,4169e0,0273x

2 000

y = 2616,6e0,0176x

5 000

y = 574,22e0,0198x 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

6 000

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Annex 6.

y = 3322,3e0,022x

10 000

European Union 10 000

8 000 8 000

4 000 4 000

2 000 2 000

0 0

Oceania

4 000 4 000

3 000 3 000

2 000 2 000

1 000 1 000

0 0

5 000

Asia 5 000

4 000 4 000

3 000 3 000

1 000 1 000

0

0

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

65

Milk yields in selected regions of the world, 1961-2009, in kg/cow/year Northern America

6 000

5 000

South America

y = 2359,3e0,0123x

y = 889,09e0,0105x

Africa

2 000

y = 441,33e0,0011x

Source: Own figure based on FAO (2011A).

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Annex 7.

Gross value added in agriculture per annual work unit in EU member states by NUTS 2 regions, 2007, in EUR

Source: EUROPEAN COMMISSION (2010B)

HFFA Working Paper 02/2011

30 000

y = 7326,4e0,0493x

40 000

30 000

y = 6183,7e0,0557x 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

40 000

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Annex 8.

y = 10718e0,0574x

60 000

Germany 60 000

50 000 50 000

20 000 20 000

10 000 10 000

0 0

60 000

Italy 60 000

50 000 50 000

20 000 20 000

10 000 10 000

0 0

60 000

Poland 60 000

50 000 50 000

40 000 40 000

30 000 30 000

20 000 20 000

10 000 10 000

0

0

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

67

Value added in agriculture per worker in selected EU member states, 1980-2009, in real 2000 USD France

40 000

30 000

Spain

40 000

30 000

y = 4718e0,0549x

Hungary

Source: Own figure based on WORLD BANK (2011).

HFFA Working Paper 02/2011

y = 6159,9e0,0325x

1 000

y = 236,17e0,0273x

0

1 000

0

Source: Own figure based on WORLD BANK (2011).

HFFA Working Paper 02/2011 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

20 000

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Annex 9.

50 000

European Union 50 000

40 000 40 000

30 000 30 000

10 000 10 000

0 0

y = 1718,7e0,022x

4 000

East Asia & Pacific 4 000

3 000 3 000

2 000 2 000

4 000

South Asia 4 000

3 000 3 000

2 000 2 000

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

68 Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

Value added in agriculture per worker in selected world regions, 1980-2009, in real 2000 USD Northern America y = 12212e0,0461x

20 000

Latin America & Caribbean

1 000

0

Sub-Saharan Africa

y = 307,5e0,0163x 1 000

0

Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

69

Annex 10. Relative gross value added of the agricultural sector compared to the overall economy in EU member states, 2008/2009, in percent BG MT ES HU NL FR CY IT UK BE EE DE LT DK EU27 EL CZ RO SK AT SE PL LU LV FI IE SI PT 0

10

20

30

40

50

60

70

Source: Own figure based on EUROPEAN COMMISSION (2010A) and EUROPEAN COMMISSION (2011).

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Annex 11. Global land and labor productivity patterns, 1961-2008

Source: PARDEY (2011).

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Annex 12. Total Factor Productivity accounts for a rising share of agricultural growth over time

Source: FUGLIE (2010A).

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Annex 13. Agricultural output and productivity growth for global regions by decade, average annual growth rate, in percent

Source: FUGLIE (2010).

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Annex 14. Water productivity in wheat production in various countries Country Ireland Uruguay France Chile Netherlands United Kingdom Germany Denmark Switzerland Belgium Sweden Egypt Italy Argentina Poland Serbia Australia Slovakia Austria Hungary Czech Republic Portugal India Bulgaria Mexico Greece Bosnia-Herzegovina Croatia Romania Tunisia Macedonia Spain Moldova Ukraine Georgia Azerbaijan China Morocco Pakistan USA Algeria Russian Federation Syria Tajikistan Canada Turkey Saudi Arabia Lebanon Armenia Turkmenistan Kyrgyzstan Jordan Uzbekistan Kazakhstan Iran

ZWART ET AL. kg/m3 Rank 1.45 1 1.44 2 1.42 3 1.42 3 1.39 5 1.36 6 1.35 7 1.35 7 1.28 9 1.27 10 1.23 11 1.22 12 1.21 13 1.16 14 1.16 14 1.15 16 1.12 17 1.10 18 1.10 18 1.09 20 1.08 21 1.07 22 1.06 23 1.05 24 1.05 24 1.05 24 1.04 27 0.98 28 0.97 29 0.95 30 0.94 31 0.91 32 0.90 33 0.88 34 0.86 35 0.83 36 0.82 37 0.82 37 0.80 39 0.79 40 0.72 41 0.69 42 0.67 43 0.65 44 0.64 45 0.64 45 0.62 47 0.62 47 0.61 49 0.53 50 0.52 51 0.51 52 0.45 53 0.39 54 0.38 55

LIU ET AL. kg/m3 Rank 1.89 1 0.59 36 1.45 8 0.80 25 1.52 6 1.80 2 1.47 7 1.73 3 1.11 10 1.70 5 1.73 3 1.18 9 1.09 11 0.53 44 1.04 13 0.87 20 0.65 31 0.74 28 1.02 14 0.88 19 0.95 16 0.39 53 0.89 18 0.71 29 0.98 15 0.54 43 0.62 33 0.80 25 0.66 30 0.41 51 0.55 40 0.84 23 0.44 48 0.56 37 0.55 40 0.61 35 0.79 27 0.42 50 0.91 17 0.81 24 0.32 54 0.62 33 0.56 37 0.41 51 0.86 21 0.65 31 1.07 12 0.48 47 0.49 45 0.55 40 0.44 48 0.30 55 0.86 21 0.49 45 0.56 37

CHAPAGAIN/HOECHSTRA kg/m3 Rank 1.95 4 1.11 20 1.12 19 0.69 30 1.62 6 2.00 2 1.33 13 1.50 7 1.34 12 0.86 25 1.28 15 1.08 21 0.49 42 1.36 11 1.99 3 1.46 8 0.63 36 2.15 1 1.02 22 1.80 5 0.85 26 0.47 43 0.65 34 1.22 16 0.94 24 0.82 28 0.43 46 0.60 39 1.32 14 0.36 49 1.00 23 0.82 27 1.22 16 1.39 10 0.69 30 0.68 32 1.45 9 0.22 54 0.30 52 1.18 18 0.37 48 0.42 47 0.45 44 0.15 55 0.67 33 0.65 34 0.51 41 0.61 38 0.63 36 0.52 40 0.31 51 0.28 53 0.73 29 0.45 44 0.34 50

Source: Own figure based on ZWART ET AL. (2010), LIU ET AL. (2007), CHAPAGAIN AND HOECHSTRA (2004).

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Annex 15. World population growth, 1950-2050

Source: FAO (2009B).

Annex 16. World income growth, 1950-2050

Source: FAO (2009B).

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Kirschke, D.; H채ger, A.; Noleppa, S. | Rediscovering productivity in European agriculture

Annex 17. Annual growth rate of yields for major cereals in developing countries, 1960-2005, in percent

Source: WORLD BANK (2007).

Annex 18.

World market price developments of agricultural commodities and crude oil, 2005-2011, January 2005=100

350 300 250 200 150 100 50

2005 Jan Mar May Jul Sep Nov 2006 Jan Mar May Jul Sep Nov 2007 Jan Mar May Jul Sep Nov 2008 Jan Mar May Jul Sep Nov 2009 Jan Mar May Jul Sep Nov 2010 Jan Mar May Jul Sep Nov 2011 Jan Mar

0

2005

2006

Crude Oil

2007

Wheat

2008

2009

Corn

2010

2011

Soybeans

Source: Own figure based on INDEX MUNDI (2011).

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Annex 19. World ethanol and biodiesel production projections, 2005-2018

Source: FAO (2009B).

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Authors

Prof. Dr. Dr. h.c. Dieter Kirschke Dr. Astrid Häger Humboldt University of Berlin Faculty of Agriculture and Horticulture Department of Agricultural Economics Unter den Linden 6 10099 Berlin, Germany phone: +49 – 30 – 2093 6256 fax: +49 – 30 – 2093 6301 e-mail: dieter.kirschke@agrar.hu-berlin.de web: http://www.agrar.hu-berlin.de/struktur/institute/wisola/fg/apol/

Dr. Steffen Noleppa agripol – network for policy advice GbR Schivelbeiner Str. 21 10439 Berlin, Germany phone: +49 – 171 – 2679 114 e-mail: noleppa@agripol.net web: http://www.agripol.net

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The HFFA Working Paper Series: 1

Der süße Sang der Sirenen Zur Bedeutung des Schutzes intellektueller Eigentumsrechte in der Pflanzenzüchtung: Eine ökonomische Analyse Harald von Witzke, Steffen Noleppa (May 2011)

2

Rediscovering productivity in European agriculture Theoretical background, trends, global perspectives, and policy options Dieter Kirschke, Astrid Häger, Steffen Noleppa (June 2011)

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HFFA Working Paper 02/2011

Imprint Rediscovering productivity in European agriculture Theoretical background, trends, global perspectives, and policy options

Dieter Kirschke, Astrid H채ger, Steffen Noleppa Berlin, Juni 2011

Humboldt Forum for Food and Agriculture (HFFA) e.V. c/o Prof. Dr. Dr. h.c. Harald von Witzke Baseler Str. 44 12205 Berlin, Germany E-Mail: office@hffa.info Web: www.hffa.info

HFFA Working Paper 02/2011