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Trends in total factor productivity

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494 CROP YIELDS AND GLOBAL FOOD SECURITY

Key points

• Agricultural output growth is the sum of area expansion, input intensification and total factor productivity (TFP) growth.

• TFP measures the efficiency of production of agricultural goods relative to all inputs. Growth in TFP drives lower real prices.

• TFP has been steadily rising since the 1960s. This has especially been the case in the last two decades when world TFP increased at a rate of 1.75% p.a. with no sign of slowdown. However, TFP rise has been weaker in South Asia and Sub-Saharan Africa.

• Country cases discussed in this chapter reveal the importance of research and development (R&D) in TFP rise; good examples are reported for the United States of America (USA), Brazil, Uruguay and China. R&D lifts TFP by pushing out the technical frontier.

• However, there is little evidence of TFP growth resulting from improved technical efficiency of farmers; that is, their ability to close the gap between their current practice and the technical frontier.

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Trends in total factor productivity

12.1 Introduction

The primary theme of this book is crop yield considered as production per unit of land area (i.e. ‘land productivity’). This concept is expanded in Chapter 11 with the notion of production per unit of other consumable resources (e.g. ‘water productivity’). These measures are partial factor productivities. On the other hand, ‘total factor productivity’ (TFP) expresses production in relation to all measured inputs combined and aggregated according to their value share in production costs.

TFP is usually measured for agricultural production as a whole by aggregating individual outputs (also according to their value share). Thus, TFP is the ratio of an index of outputs and inputs, and hence is dimensionless. However, TFP is still valid for broad efficiency comparisons among industries, countries and years. Crop-specific estimates of TFP growth are sometimes available, but should be interpreted cautiously given that it is difficult to allocate fixed costs across crops and to account for interactions between enterprises in farming systems.

This chapter presents analysis of TFP change as a complement to previous analyses of yield increase of individual crops. Compared with yield increase, TFP increase is a better indicator of overall technical progress because unit production costs are the fundamental driver of food prices; yield increase driven solely by increased input is not necessarily technical progress or improved efficiency. For example, Linehan et al. (2013) demonstrated with partial global equilibrium modelling that a 10% increase in rate of TFP growth delivered three times the agricultural price decrease to 2050 (relative to the baseline price simulation for 2050) when compared to a 10% increase in yield .

Food prices are a critical indicator of success in feeding the world, and are especially important for people of lower socioeconomic status who spend a high proportion (sometimes >60%) of their income on food. As the largest food exporter, the USA strongly influences world grain price, which explains why the long decline in real grain prices to 2006 (see Figure 1.2) has closely tracked TFP growth in US agriculture (Alston et al. 2010a).


12.2 Explaining total factor productivity

In exploring TFP, this section explores the conceptual framework for components of agricultural output and TFP increase, and discusses measuring total factor productivity.

Conceptual frameworkComponents and measures of agricultural growth are represented diagrammatically in Figure 12.1. ‘Agricultural growth’ in real value is made up by changes in ‘real output growth’ and changes in real prices or ‘price effects’. The latter reflect differences in the rate of change in agricultural prices relative to overall price change in the economy. Not shown in Figure 12.1—but frequently used to measure agricultural growth—is ‘agricultural gross domestic product (GDP)’ or agricultural value-added, which is calculated as agricultural value minus all the costs of intermediate inputs such as fertiliser and fuel, but not the primary factors of land, labour and capital.

Inputintensification

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Figure 12.1 Components of agricultural growth and their major influences. TFP = total factor productivity. Source: adapted from Fuglie and Rada (2012)

497TRENDS IN TOTAL FACTOR PRODUCTIVITY

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As agricultural output is a physical measure of production, an index based on weighted price is needed in order to be able to aggregate across product outputs. In other words, a high-value product, in terms of dollars per kilogram ($/kg), will carry more weight in the index for a given quantity of output than a low-value one, and the output index is more than the sum of the mass of products.

Real agricultural output growth is driven in part by area expansion (which is easy to measure by summing across all crops), and in part through increased yield, where yield is simply the output index divided by crop area—in this context, a value-weighted aggregate farm yield (FY; see Chapter 2). Yield increase is in turn made up of two elements: (1) increases in the use of non-land inputs such as capital, labour, water and fertiliser per hectare—also known as input intensification, and (2) TFP growth which is driven by technical change, improved efficiency or changes in the product mix (Figure 12.1).

Technical change results from pushing out the production frontier by developing and adopting new technologies. It is closely related to gains in potential yields discussed in previous chapters (see also Box 2.2 on efficiencies, profit maximisation and TFP under technical change).

Efficiency gains are measured with respect to the way inputs are used or combined in the most cost-effective way when using existing technology; efficiency gain is a measure of the gap between the farm operation and the technical frontier. These gains can be further distinguished as those due to technical efficiency, allocative efficiency and scale efficiency. Technical efficiency gains can arise when better management of the same level and combination of inputs (for example, through better timing or placement of fertiliser) achieves a higher output, or when the same output is achieved using fewer inputs. Allocative efficiency gains occur when expenditure among input categories (between, for example, labour and fertiliser) is better allocated to achieve the same level of output at lower cost. Scale efficiency (inefficiency) may occur if total production cost per unit of output is negatively (positively) related to the scale of operation.

Changes in product mix occur as result of a move to a higher quality output of a given product (e.g. varieties that are preferred by consumers), or when higher value products become more important than others over time. Since consumers with rising incomes demand higher value products, changes in product mix generally contribute to TFP growth.

It is also important to note that yield increases can take place through input intensification without corresponding gains in TFP, so yield gains may not be closely related to TFP growth. As alluded to in Box 2.2, yield increase without TFP increase is more likely to occur in the early stages of input intensification. In agricultural regions where input use is already high, yield gains are likely to closely track TFP growth.

Policy and institutional factors shown in Figure 12.1 affect real output growth, although specific factors exert differing effects on area increase, input intensification and TFP growth. For TFP growth, a large general literature suggests that the primary driver


for technical change is investment in research and development (R&D).66 Crop genetic improvements can increase potential yield (PY; see Chapter 2) and this will directly translate into TFP growth through technical change if costs associated with using improved seed are low (as has been the case until recently).67 In low-income countries with scarce land resources, land-saving technologies such as crop genetic improvement and improved management practices that raise yields per unit of land are generally the primary source of TFP growth. In land-abundant countries, and countries with rapidly rising wages, increased labour productivity (output per unit of labour input, a technical change) through substitution of machinery for manual labour is often the major source of TFP growth.

Because improving efficiency is a major component in closing the yield gap—as discussed extensively in Chapter 8 ‘Yield gap closing’—extension, education, roads, communication, risk mitigation and (sometimes) farm size play important roles in TFP growth. In addition, institutional changes can also contribute—for example, China’s agricultural take-off from 1979 was largely caused by dismantling of collective farms and introducing the household responsibility reforms that provided large efficiency gains and initiated a period of rapid TFP growth (Fan 1991).

Measuring total factor productivityThere is a large general literature on how best to measure TFP in practice. Measurement problems (Alston et al. 2010b) arise from issues of the:

• method of indexing—i.e. weighting and adding of inputs and (separately) outputs—which in turn depends on assumptions about the shape of the underlying production function (relating outputs to inputs)

• method of accounting for changes in the quality of inputs and outputs

• coverage and level of disaggregation of inputs and outputs

• degree of spatial disaggregation of the input, output and price data.

A comprehensive discussion of methods and pitfalls in estimating TFP growth is provided by Alston et al. (1995). In general the more disaggregated the analysis (in terms of inputs and outputs), the better TFP growth can account for changes in quality of inputs and outputs—for example, rainfed vs. irrigated land as input, and rice of different qualities as output. Even then estimates will inevitably be incomplete for input

66 Agricultural R&D generally does not include expenditure on agricultural extension, although it is not easy to separate some R&D from extension, especially in the private sector. Where extension is included, it is commonly abbreviated RD&E.

67 However, proprietary hybrids and genetically engineered (GE) seed can carry significant costs. One example of recent price increase has been observed in Iowa, USA, where seed costs for maize now amount to US$250/ha, more than the market price of 1 tonne of maize grain, vs. only US$64/ha in 2000.


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coverage, so TFP can be thought of as residual growth that is not explained by the basket of inputs included. For these reasons, economists increasingly refer to the measure as ‘multifactor productivity’, although for the purposes of discussion here, the conventional TFP nomenclature has been retained.

TFP estimates based on international statistics will be particularly prone to errors arising from aggregation and omission of variables, relative to estimates based on more disaggregated national statistics where such errors can be seen and dealt with by experienced national economists. In a few cases, primary data from farm surveys conducted over years are used to construct TFP estimates (e.g. Hughes et al. 2011, see below). This method is potentially more accurate if the same population of farms is sampled each time.

Because of different methods for measuring TFP and different sources of data, estimates of TFP growth may vary considerably from one author to another. Thus the TFP figures have been selected for discussion in Chapter 12 based on subjective assessment of the quality of underlying studies; in some cases, several sources are reported for the same country. According to the convention in the economics literature, TFP growth rate is expressed as exponential growth throughout this chapter.

12.3 Global and regional trends in total factor productivity

In recent years, there has been an upsurge in tracking long-run growth in TFP (Alston et al. 2010a; Fuglie et al. 2012b). At a global level, Fuglie (2012b) provided an up-to-date and comprehensive overview of TFP growth. Although this included important corrections to standardise for land quality and estimated factor shares specific to regions at different levels of development, Fuglie (2012b) recognised the inherent limitations of a global analysis.

Figure 12.2 provides an overview by time of the sources of agricultural growth for 1960–2009, following the framework of Figure 12.1 (Fuglie 2012b). Note that because of its importance, irrigation has been separated from other input intensification in Figures 12.2 and 12.3. Over the past 50 years, TFP has clearly become the dominant source of real agricultural output increase globally, accounting for 74% of growth in the past decade (2001–09) compared with <10% in the first decade (1961–70). At the same time, land area expansion and input intensification have fallen as sources of output increase (Figure 12.2). This is good news for real food prices and input use efficiencies, as was seen in Chapter 11.


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Figure 12.2 Sources of increase in global real agricultural output by decade from 1961 to 2009. TFP = total factor productivity. Source: Fuglie (2012b)

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Figure 12.3 Sources of growth in real agricultural output by region from 1960 to 2009. TFP = total factor productivity. Transitional = Eastern Europe and former Union of Soviet Socialist Republics (USSR). Source: K. Fuglie, pers. comm. (2012), based on Fuglie (2012b)


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As might be expected, there are major differences in sources of growth by region (Figure 12.3). Land expansion has been important in the relatively land-abundant regions of South-East Asia, South America and Sub-Saharan Africa, but cultivated area has fallen in developed and transitional regions. In Asia, irrigation and input intensification have made important contributions to output growth. TFP growth has been important in all regions except in Sub-Saharan Africa, and in the transitional region where overall real agricultural output growth was much slower than in other regions.

Similarly, marked regional differences are seen in trends in land and labour productivity growth, depending on relative endowments of land and labour, and stage of economic development (see Box 12.1).

The primary question for food security is, of course, whether the growth observed in TFP over the past 50 years is now slowing. Fortunately, at the global level, there is little evidence for this. Rather, global TFP growth rate has increased steadily from 0.2% p.a. in the 1960s to 1.8% p.a. in the most recent decade (Figure 12.2 and Figure 12.4a). TFP increase has notably accelerated in Asia (because of growth in East Asia and South-East Asia) and Latin America, while an observed acceleration of TFP growth in Africa (Figure 12.4b) and the transitional countries (Figure 12.4a) reflects mainly negative performance in earlier decades. Although TFP growth in East Asia (mainly China) has declined since 2000 (Figure 12.4c), growth remains high by almost any standard.

Box 12.1 Trends in land and labour productivityThe figure below, from Fuglie and Wang (2012), is an excellent depiction of trends in land and labour productivity in agriculture across key world regions and/or countries. In high-income countries, such as Europe, North America and the developed countries of East Asia, the rate of growth of labour productivity from 1961 to 2010 was much faster than the rate of growth of land productivity—hence the flatter trajectories shown in the figure (note the log scale). As expected due to a large endowment of moderate-quality farmland and to high wages, North America and Australia showed lower land productivity, but higher labour productivity than Europe and developed East Asia. In all these developed regions, rapidly rising labour productivity reflects efforts to save labour through mechanisation and increasing farm size.

Latin America, West Asia, northern Africa and the former USSR are relatively land-abundant regions, but these regions are intermediate on both land and labour productivity (see centre of figure). The short length of the line for the former USSR indicates overall low productivity increase there. South Asia,

Continued next page


South-East Asia and China are land-scarce regions with relatively high land productivity but low labour productivity—although China is clearly making strong gains in labour productivity. Sub-Saharan Africa is low on both measures, and labour productivity has hardly changed in nearly half a century—hence the near vertical trajectory in the figure.

The variations in the trajectories among regions reflect efforts by countries to save on their most scarce resource (land or labour), as well as overall success in increasing productivity. While this figure provides an interesting view of the relationship between land and labour productivity, caution is needed when translating these productivity measures into changes in TFP, since the simple two-way graphic shown does not take account of changes in other inputs that may have contributed to output growth.

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WORLD

Trajectory of agricultural land and labour productivity by region in 5-year averages between 1961–65 and 2006–10. Source: Fuglie and Wang (2012)

In the box figure, agricultural output is reported as FAO Agricultural Output in constant 2005 purchasing power parity (PPP) US dollars, and agricultural land (total cropland and permanent pasture) and labour (number of economically active adults employed in agriculture) are also from FAO. Each arrow represents a 5-year average value, starting with 1961–65, then 1966–70 and so on, ending with 2005–10. East Asia (developed) includes Japan, South Korea and Taiwan.

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Figure 12.4 Trends in total factor productivity (TFP) growth from 1961 to 2009 by (a) type of country, (b) world regions, and further breakdown to (c) regions of Asia. Source: Fuglie (2012b)


12.4 Brief case studies of total factor productivity trends by country

The global estimates of TFP growth presented above necessarily depend on statistics produced by the Food and Agriculture Organization of the United Nations (FAO), the only comprehensive global database on agricultural outputs and inputs. FAO statistics limit TFP inputs to highly aggregated and often heterogeneous categories, and frequently lack the country-level price data that are required to compute shares of production factors such as land, labour and capital. By comparison, country-level estimates of TFP growth generally use much more disaggregated statistics on input and output categories and local prices (often at the subnational level) and hence provide more precise pictures of TFP trends. In many cases, country-level estimates allow econometric analysis of the determinants of TFP growth. Some country cases are presented below.

United States of AmericaThere has been a long and rich tradition of estimating and analysing determinants of TFP growth in the USA (Alston et al. 2010b). Based on nearly 40 studies, and regardless of measures used, since World War II the long-term annual TFP growth rate in the USA has clustered within a range of 1.5–2.0% p.a. A comprehensive analysis by Alston et al. (2010b) is based on 58 categories of inputs and 74 outputs, all at the state level. Note that the major crops considered in this book—maize, soybean and wheat—make up only about one-quarter of the total value of agricultural production in the USA, so their weight in TFP trends is not high alongside animal products, fruit and vegetables.

Overall, Alston et al. (2010b) claimed a slowdown in TFP growth in the decade to 2002—the last year of the data series explored, with growth of 0.97% p.a. relative to rates over 2% p.a. in the previous 20 years. Consistent with yield progress data in Section 5.2 on maize and Section 6.2 on soybean, Iowa—a state in which maize and soybean predominate—was the best state performer in 1993–2002 (2.37% p.a.). By contrast, the major wheat producing state of Kansas (see Section 3.9) showed a sharp fall in TFP growth to 0.67% p.a. More recent estimates by the US Department of Agriculture (USDA) (using a slightly different methodology) have suggested only a modest slowdown (if any) in national TFP growth, which averaged 1.2% p.a. in 2000–09 (USDA 2013).

Many authors have also estimated the major determinants of TFP growth in the USA by econometric methods. Most recently, Wang et al. (2012) related 1980–2004 patterns of TFP growth trends across the 48 contiguous states to:

• expenditure on research, development and extension (RD&E)


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• density of transportation networks

• labour quality.

From this analysis, the estimated annual internal rate of return68 to investment in RD&E was 17–69%. This range reflects varying assumptions about technological spill-ins—that is, technology coming from research done outside the region of interest (in this case, other states)—and interactions of research with other variables such as factors influencing technology adoption. These findings supported those of Alston et al. (2011) who also reported (albeit with lower figures) that expenditure on R&D was the major determinant of TFP growth. Using longer research lags (benefits peaking at 24 years after the original R&D) and accounting for spill-ins from other states, Alston et al. (2011) estimated an internal rate of return of 24%.

The slowdown in TFP growth in the USA, to the extent that it exists, can largely be attributed to a slowdown in public investment in agricultural R&D. Annual public expenditure in agricultural R&D in the USA increased at a rate of 3.8% p.a. between 1950 and 1969, after which rates began to fall, and since 1990 expenditure has increased by only 1.1% p.a. In addition, a relatively smaller share of public expenditure on agricultural R&D has been allocated to productivity improvement—vs. other objectives such as human nutrition, food safety and environmental sustainability—to such a point that spending on field crops (including staple grains) has stagnated in real terms since 1990. By contrast, private expenditure on crop breeding and biotechnology has sharply accelerated. Private investment doubled between 1993 and 2005, and then almost doubled again to reach US$2.17 billion in 2010 (Fuglie et al. 2011)—a level similar to US public investment. Thus adding public and private investments, evidence suggests that US investment in R&D for crop improvement has improved overall since 2005.

AustraliaIt is challenging to assess trends in Australian agriculture, given the high variability in climatic events since the 1990s. Based on annual farm surveys of crop producers, Hughes et al. (2011) estimated climate-adjusted average annual TFP growth of 2.31% p.a. from 1977 to 2000, followed by a sharp decrease to an average of 0.54% p.a. from 2000 to 2007, a period of extreme drought. It is suggested here that the climate adjustment corrected for only some of the adverse effects of the drought. As was the case for much of the world (Figure 12.3), TFP growth in Australia accounted for an increasing share of growth in agricultural output (Hughes et al. 2011).

68 ‘Annual internal rate of return’ is a discount rate that equates the discounted value of future output with the discounted cost of the investment. Discounting takes into account that $1 spent on R&D early in the research cycle is worth more than $1 received from the extra output, say, 20 years from now.


The component contribution to TFP growth from technical change vs. gains in technical efficiency (see Section 12.2) was also determined by Hughes et al. (2011). Over the whole period 1997–2007, technical change rose strongly at a rate of 1.5% p.a., but technical efficiency fell at 0.3% p.a., suggesting an increasing divide in management skills within the farming population. Technical change also slowed in the most recent period (2000–07) to reach 0.4% p.a. The trend was partly attributed to a sharp slowdown in public expenditure on R&D without a corresponding boost in private expenditure.

Between 1953 and 1980, increase in public expenditure on R&D averaged 6.5% p.a. By comparison, since 1980, growth in expenditure has averaged only 0.6% p.a.69 As described for the USA, public expenditure in Australia has also shifted from productivity-orientated research toward research on food safety, the environment and climate change (Nossal and Sheng 2010).

Confounding the slowdown in R&D are concerns that adoption of available technologies is also slowing. This has been attributed to:

• ageing farmers

• insufficient skills for the more complex modern technologies

• risk aversion and financing constraints in the face of frequent droughts since 2000

• the demise of public extension and information services.

Accordingly, cross-sectional analysis of grain farmers in Victoria for 2006–07 and 2007–08 found that TFP tracked innovation adoption, which in turn was related to farmer characteristics, especially education, farm size, labour availability, use of contract services and land-use intensity (Nossal and Lim 2011).

BrazilSince the 1990s, Brazil has been one of the world’s agricultural success stories. Overall, between 1970 and 2006, TFP grew by 2.2% p.a., and accounted for one-half of total real agricultural output growth (Gasques et al. 2010). Since other inputs (especially labour) have declined, the remaining share of output growth is explained by area expansion, which increased by two-thirds during this period.

Despite general success, there has been wide diversity in performance in Brazil (Gasques et al. 2010). In the state of São Paulo, where sugarcane dominates, TFP growth has been steady but slow. In Paraná, where wheat, maize, soybean and poultry are major products, TFP increase has been higher. Meanwhile in Mato Grosso, now the major soybean producing state, TFP increase rate has skyrocketed

69 Since 2005 there has been a lift in private expenditure on plant breeding in Australia, coincident with the privatisation of wheat breeding (Fischer 2012).


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to nearly 6% p.a., the highest recorded rate in the studies reviewed in this chapter. Factors in the transformation of Mato Grosso in Brazil’s Cerrado region (see Box 5.5 and Section 6.3) have included widespread adoption of genetically engineered (GE) soybean adapted to low latitudes, conservation farming, soil amendments and replacement of low-quality pasture with crops.

A major factor underlying such success has been Brazil’s consistent investment in R&D, especially through the Brazilian Agricultural Research Corporation—also known as Empresa Brasileira de Pesquisa Agropecuária (Embrapa). With a budget of over $US1 billion, Embrapa is the largest and most effective tropical and subtropical research institute in the world. The private sector has also played an important role. Seed companies and producer organisations have released GE soybean and maize varieties, and producer organisations have developed conservation farming technologies; there has also been significant technology spill-in from Argentina.

UruguayAgricultural GDP growth has accelerated in neighbouring Uruguay since 2000 to reach 4.4% p.a. This rate of growth is led by crop sector growth of >8% p.a. (Bervejillo et al. 2012) so that Uruguay is now a large exporter of soybean, wheat and rice, and achieves one of the highest rice yields in the world (7.7 t/ha over 160,000 ha, average 2008–10; FAOSTAT 2013).

TFP growth rate over the 30-year period from 1980 to 2010 has been estimated at 2.1% p.a. (Bervejillo et al. 2012). In the most recent decade (2001–10) the rate was even greater at 3.9% p.a. Bervejillo et al. (2012) showed that TFP increase is strongly related to public and producer expenditure on R&D, but only weakly related to private agribusiness R&D. Spill-ins of technology from Argentina and Brazil have undoubtedly made an important contribution to TFP growth in Uruguay, especially for maize and soybean.

The results for Uruguay are significant in that they indicate the success of a small country in organising a productive research system. Modelled on the Australian system of matching agricultural production levies with public funds, Uruguay has succeeded in organising a responsive and effective public R&D system. With a research intensity of 2.0%—that is, spending as a percentage of agricultural GDP—Uruguay has the highest expenditure in Latin America. Spending has accelerated in the past decade (Byerlee 2011).

IndiaThere is no conclusive evidence of current agricultural TFP trends in India, and this case study would greatly benefit from more recent analysis. For all agricultural production, Kumar et al. (2008) summarised various estimates from the 1960s to the late 1990s and showed TFP growth within the range of 1–2% p.a., but also indicated


a trend toward a slower rate of growth. Low labour productivity (in part due to lack of non-farm opportunities) has likely been one factor in the poor performance of TFP growth in India relative to China, discussed below. Further factors include repeated failure to reform input subsidy programs, and possible soil degradation.

Chand et al. (2012) used farm survey data to compute crop-specific TFP for India (Table 12.1). A crop-specific approach enables more precise estimates of inputs (especially labour) but suffers from the assumption that crops are produced independently of one another. Chand et al. (2012) showed that crop-specific TFP increase rates were highest for cereals (especially wheat), modest for oilseeds and some pulses and negative for other pulses and sugarcane. Gains in TFP provided less than one-half of output growth, except in the case of wheat.

Table 12.1 Total factor productivity (TFP) performance, TFP share of output increase, and returns to research and development (R&D) in India from 1975 to 2005

Crop TFP increase rate (% p.a.)

TFP share of output increase (%)

Internal rate of return on R&D

(% p.a.)

Wheat 1.92 59 38

Cotton 1.41 32 39

Maize 1.39 17 28

Millet 1.04 28 31

Rapeseed 0.79 10 20

Peanut 0.77 27 18

Soybean 0.71 6 na

Rice 0.67 25 29

Sorghum 0.63 24 39

Mungbean 0.53 10 57

Chickpea 0.16 26 34

Sugarcane –0.41 negative negative

Pigeon pea –0.69 negative negative

na = not available

Source: Chand et al. (2012)

The Chand et al. (2012) results showed little discernible trend in TFP growth over time, but consistent differences in growth rates across crops and states. The best performance was shown in the north-west of India, which was first to benefit from the green revolution (see Chapter 1 ‘Introduction’), and some parts of the south. Annual


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internal rates of return to research were relatively high in all cases, except, of course, for sugarcane and pigeon pea where TFP decreased (Table 12.1). Since 1991, Indian expenditure in R&D has generally trended upward in real terms at a respectable rate of >6% p.a., but research intensity is still only <0.4% of agricultural GDP, among the lowest rates in the world (see also Section 13.2 on R&D investment).

More detailed studies for India are available at the subnational level. For example, based on state-level statistics of 20 categories of crop and livestock products and 12 categories of quality-adjusted inputs, Murgai (2001) estimated the TFP increase rate for agriculture in the state of Punjab to be 1.9% p.a. for 1961–94, with little discernible trend. However, when TFP estimates were corrected for factor-biased technical change—that is, a sharp increase in share of capital relative to labour—the estimates range from 2.9 to 4.8% p.a. (Murgai 2001). More importantly, the corrected TFP trends showed a marked slowdown since the green revolution. Murgai’s (2001) more detailed and careful study is probably a better representation of overall trends—as discussed in Sections 3.3 and 4.4—in the important rice–wheat system in northern India (including Punjab).

ChinaSince the household responsibility reforms that were introduced in 1979, estimated TFP increase rates for China have been strongly positive, and this has reversed the previous poor performance of TFP increase (<1% p.a.). Since 1979, the rate of TFP increase has been 3.4% p.a., accelerating to 3.9% p.a. for the period between 1991 and 2006.

As in India, farm surveys have been used to compute crop-specific TFP values. S. Jin et al. (2010) estimated generally lower TFP growth rates for specific crops than for agriculture as a whole (Figure 12.5), probably because crop-specific studies did not account for increasing cropping intensity and diversification to higher value crops as is included in aggregate measures of TFP. In 1985–94 the only crops with strong TFP increase were Indica rice and maize; this is very likely the effect of the first hybrid varieties. For all crops, TFP increase accelerated in the most recent period, 1995–2004, probably as investment in R&D picked up (Figure 12.5). Unlike India, there has been a general convergence in TFP growth across crops in China to a range of 1.5–3% p.a. Performance has been lowest for maize, a result consistent with the yield analysis discussed in Section 5.3.

The very positive TFP trends in China are likely explained by continuing reforms (such as liberalisation of prices), rural education and heavy investment in R&D, although less in extension. Spending on agricultural R&D accelerated between 1995 and 2008 to pass 0.5% of agricultural GDP (Beintema et al. 2012). China now spends more than $US4 billion annually (2005 purchasing power parity (PPP) dollars) on agricultural R&D. Without these research and policy-induced productivity gains, food prices in China (and the world) would undoubtedly be much higher.


–0.5

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Figure 12.5 Rates of total factor productivity (TFP) increase in China by crop, for the period 1984–94 compared with 1994–2005. Source: S. Jin et al. (2010)

Sub-Saharan AfricaAll estimates—averaging only ~0.5–1.0% p.a. since 1990—indicate that TFP increase in African agriculture lags the rest of the world. However, TFP increase since 1990 represents an improvement over the negative TFP rates seen in the 1970s (Figure 12.4b). The improved performance has been attributed to complex factors (Nin-Pratt 2011; Fuglie and Rada 2012) including:

• pay-offs to investments in CGIAR from 1977 to 200570

• more favourable price policies

• reduction in conflict

• investment in national agricultural research systems.

Even so, TFP increase has again fallen, from 1.15% p.a. in 1991–2000 to 0.43% p.a. in 2001–09. Real agricultural output growth has been maintained at 3.16% and 2.61% p.a., respectively, largely due to continuing land area growth of 1.88% and 1.77% p.a., respectively (Fuglie 2012b).

70 There was little CGIAR activity in Africa before 1975.


12

As expected in a large and diverse continent, TFP performance has been heterogeneous. This reflects a wide variety of factors, including underlying resource endowments, policy environments and conflicts. Data quality has, however, generally been inadequate to explain patterns of TFP growth across countries and regions. In eastern Africa, Kenya has been a consistently better performer. In western Africa, coastal countries have performed better than inland countries (Fuglie 2012b), no doubt due to better access to inputs.

Differences in TFP growth across countries and over time have been explained by expenditure in agricultural R&D (both national and international), road infrastructure, conflict resolution and control of human diseases (Fuglie and Rada 2012). These same authors found strong evidence of economies of size in R&D spending in Africa, with highest returns resulting from investment by CGIAR and investment in large countries. In many countries, volatility of expenditure on R&D is especially problematic, in no small part due to heavy reliance on international donor support (Stads 2011).

12.5 Synthesis of trends in total factor productivity

The results of this TFP review generally support those from the yield analyses presented in earlier chapters of this book. The key finding is that a slowdown in TFP increase is not apparent at the global level (Figure 12.4), which is good news for food prices. There is no significant slowdown in any region, but TFP increase appears to be low in India and was never really high in Sub-Saharan Africa. Certainly, TFP growth in China appears to have slowed somewhat during 2000–09 relative to previous periods, but it remains high at 3.0% p.a.

The surge in food prices from 2008 to 2012 may relate as much to a surge in demand (especially for biofuels) and higher input prices, as to any slowdown in TFP growth. As expected, a higher share of real agricultural output increase almost everywhere outside of Sub-Saharan Africa is coming from TFP increase rather than input intensification and area expansion. In developed countries, the share of output increase from TFP increase is now >100%, as input use is declining.

The laggards for TFP growth are India (pending an update of national TFP estimates) and Sub-Saharan Africa. The major constraints for India appear to relate to distorted incentives from input subsidy and product price support policies, which have impeded diversification and fostered the mining of natural resources such as groundwater. Performance in Sub-Saharan Africa generally continues to lag behind other regions due to poor infrastructure, low investment in R&D and conflict (Fuglie and Rada 2012).


Finally, the results presented above demonstrate the key role that investment in agricultural R&D can play in driving TFP increase. This is evident when agricultural TFP rates and R&D investment are compared between countries, and is amply supported by bottom-up studies of individual technologies, as well as econometric analyses where the data are available (e.g. for the USA). Another econometric analysis (Fuglie 2012b) demonstrated that agricultural TFP growth across 87 developing countries over the past 20 years—ranging from zero to >3% p.a.—was significantly and positively related to a surrogate measure of R&D investment termed ‘invention and innovation’, and also showed a positive, albeit weaker, relationship to investment in agricultural extension and education.

Notwithstanding these positive results, R&D is not a sufficient condition for rapid TFP growth. Chapter 13 ‘Policies and people’ provides more evidence on the need for investments in agricultural R&D, but adds consideration of the other areas of policy that play an essential role in productivity growth.

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