copy the linklink copied!Chapter 3. Drivers of agricultural productivity and sustainability performance

This chapter summarises recent empirical evidence from the country reviews and related work on drivers of farm performance and policy impacts on agriculture, which support the role of innovation, structural change and natural resource use and climate change in driving farm productivity and sustainability improvements. This review of available evidence also sheds light on a number of important knowledge gaps.

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  • The theoretical pathways between innovation and productivity are backed-up by empirical evidence from both sector-level and farm-level estimates.

  • Adoption of innovation is the main driver of productivity growth and can improve sustainability if incentives to that effect are in place.

  • Increasing evidence confirms the role of economies of scale in improving farm-level productivity, illustrated by the generally higher productivity performance of larger farms.

  • The impact of structural adjustment on other performance aspects such as production, sustainability, animal welfare, and rural development is less obvious.

  • The sustainable use of natural resources contributes to improving both productivity and sustainability performance.

  • Improved sustainability performance of agriculture is partially driven by agriculture and economic development and societal demands.

  • Climate change is expected to become a significant driver of productivity and sustainability performance; it generates agricultural risks and uncertainty and exacerbates the need for agriculture to mitigate previously unaccounted for environmental externalities.

  • The drivers interact with each-other and are affected by other natural, market and policy factors.

The country reviews and recent OECD work on drivers of farm performance and policy impacts on agriculture contain empirical evidence supporting the role of innovation, structural change and natural resource use and climate change in driving farm productivity and sustainability improvements, and thus the relevance of the framework (Box 1.1). This review of available evidence also shed light on a number of important knowledge gaps.

In addition to policies, natural conditions, market incentives, and other considerations also affect these drivers, and thus productivity and sustainability achievements (OECD, 2015a). Innovation and adjustment in particular occur primarily in response to market-driven efforts to improve competitiveness, as long as policies do not impede them. Regarding natural resource use, however, markets often fail to provide signals reflecting societal demand, although some mechanisms exist (e.g. environmental labelling, emission trading mechanisms).

copy the linklink copied!Innovation

Evidence gathered in country reviews confirms that innovation is a major driver of productivity growth and sustainability in many cases. Examples of productivity-enhancing innovations include: genetic improvement and the adoption of high quality seeds and animal breeds (most countries); the adoption of input-saving technologies and practices — such as no-till cropping (Argentina, Australia, Canada, the United States), milking robots (Box 2.3 in the Estonian review, OECD, 2018), modern buildings allowing energy savings and higher animal welfare, climate-smart greenhouses (Box 2.2 in the Dutch review, OECD, 2015b); technologies and practices for precision agriculture and better management of risks (irrigation systems, GPS assisted tractors, satellite images and drones); and changes in production management and marketing practices, including the development of new products.1 The role of organisational, product, and marketing innovations is particularly emphasised in the Netherlands (OECD, 2015b).

To complement country reviews and strengthen the framework, several OECD reports analysed farm-level productivity pathways, and confirmed the role of innovation in generating productivity gains. Analysing farm-level innovation in Dutch dairy and arable farms, Sauer (2017) finds that innovations related to improving processes, farm organisation and marketing resulted in significant productivity gains. Estimations identify a number of farm-specific characteristics, which affect the size and success of innovations. These are own product- and process-related development activities, farm size, the age of the farm operator as well as confidence in business and sector developments. Moreover, regulations and standards can create a demand-pull for innovation; and the level of co-operation with knowledge producing institutions improves the likelihood of success.

In analysing developments in dairy farm Total Factor Productivity (TFP) in Estonia, the Netherlands and the United Kingdom, Kimura and Sauer (2015) identify several pathways to improving productivity, including technological adoption and extension, efficient management of inputs and structural change (Box 3.1). Initial results of an analysis of drivers of farm performance in EU country cases also provide evidence of a positive correlation between innovation and productivity at farm level in crop farming in Hungary, dairy farming in the Czech Republic and Denmark, pig farming in Denmark, and fruit production in Chile (OECD, 2019a).

copy the linklink copied! Structural change

Recent farm-level analyses, conducted at the OECD and in reviewed countries, confirm that larger farms are more productive than smaller ones. Indeed, they can better manage labour, and use scale-dependent technologies. These include larger combines and harvesters that allow cultivating more land with the same amount of labour, but also GPS-equipped precision agriculture technologies that allow reducing the amount of input use.2 Operators of larger farms also have better access to knowledge, because of higher human and financial capacity. Similarly, larger agri-food firms generally have more capacity to carry out innovation activities, acquire technology, deal with regulations, and access export markets.

Scale economies are, however, context-dependent: the extent and magnitude of scale economies depends on relative factor prices; in agriculture, on topography and on infrastructure (for example, quality roads to move large volumes of inputs and products). Scale economies are also limited: there are few natural oligopolies in agriculture, and most large-scale farms can be still fairly small businesses.

The analysis of dairy farm productivity dynamics in four EU Member States illustrates the link between productivity growth and structural change (Box 3.1). An analysis of TFP trends for crop farms of selected EU Member States also shows that larger farms tend to be more productive than smaller farms (Bokusheva and Čechura, 2017). They appear to be in a better position to exploit economies of scale, although technical change may also have played a role. The analysis also found that there remains scope for improving economies of scales, and thus productivity among crop farms studied. Recent work on drivers of farm productivity also suggests that groups with larger farms tend to have higher productivity (OECD, 2019a), but more evidence is needed to better understand the relationship, which is probably not linear.

In the United States, pig industry developments illustrate the positive relationship between farm size and productivity performance, and the contribution of economies of scale to productivity improvement (OECD, 2016a). However, an ABARES analysis of Australian broadacre farms over the period 1977/78 to 2006/07 finds that while larger farms exhibit higher productivity than smaller ones, this is more likely due to differences in production technology than returns to scale (Sheng et al., 2014). Comparing productivity estimates in developed and developing countries, Rada and Fuglie (2018) suggest that the relationships between size and productivity may depend on context, and in particular development level.3

ABARES work reported in Box 1.1 in OECD (2015c) identifies resource reallocation, i.e. moving between farms of differing levels of productivity, as a significant driver of TFP growth in the broadacre industry, including when on-farm productivity growth was slower. Similarly, in a recent analysis of US grain-producing farms in the Corn Belt region discussing the contribution of structural change to productivity growth, Key (2018) estimates that the shift in production to larger, more productive farms explains one-sixth of aggregate productivity growth between 1982 and 2012, the reminder being attributed to average TFP growth in each farm-size category. Developments in the US pig industry also illustrate the connection between structural change and productivity growth, with the share of production in the larger, more productive farms increasing rapidly in the 1990s (OECD, 2016a).

Evidence on sustainability performance by farm size is more difficult to obtain and may be context-dependent. Initial results of the analysis of drivers of farm productivity mentioned above finds mixed evidence on the structural characteristics of more sustainable farms (OECD, 2919a). Countries with predominantly small farms often exhibit high nutrient surplus and emissions per ha (for example the Netherlands and Korea) due to intensive land practices. In the People’s Republic of China (hereafter “China”), the extremely limited size of many farms results in many farmers spending most of their time in off-farm labour. As a result, these farmers use a significant amount of — often subsidised — agricultural inputs to compensate for their limited labour time. In contrast, farmers operating on large land properties in countries like Australia, Brazil, Canada or the United States often prefer to rationalise and limit their use of inputs to what they consider the minimum necessary to limit the total variable cost they incur.

Yet, farm size may not be as important for sustainability as for productivity. For instance, in their international meta-analysis, Balmford et al. (2018) find that production systems occupying less land per unit of product (higher yields) tend to generate a lower level of negative environmental externalities per unit production (greenhouse gas-GHG, water use, nitrogen, phosphorus, and soil losses) than others, although data remains limited. Blandford and Hassapoyannes (2018) also find that per ha GHG emissions are much larger in countries with extensive livestock system than in more intensive ones.

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Box 3.1. Dynamics of dairy farm productivity growth in Estonia, Germany, the Netherlands and the United Kingdom

Recent OECD farm-level analysis found that improvement in labour productivity is the largest contributor to dairy sector-level productivity growth across countries. Structural change, characterised by a decline in the number of dairy farms and an increase in the average herd size per farm, led to lower labour input use associated with higher capital inputs, notably machinery and equipment.

Total Factor Productivity (TFP) growth before the phasing out of milk quotas is almost entirely driven by a decline in input use. However, the main driving force of TFP growth in the Dutch dairy farm sector became the expansion of milk output after the phasing out of milk quotas started.

Significant differences continue to exist in farm-level productivity within countries. In Germany, regional differences in average farm-level productivity persist. In Estonia, the evolution of sector-level productivity is largely driven by improvements in a small number of large farms, and the productivity difference between large and small farms increased overtime.

In the Netherlands and the United Kingdom, there has been a significant herd size expansion in all size classes of dairy farms and larger farms on average continue to achieve higher levels of productivity. However differences in productivity across farms have decreased over time due to the diffusion of technology across farms as well as the exit of less productive farms.

The analysis also shows that productive farms tend to be more intensive in some input use, such as higher stocking density, and use more purchased feed per cow. The direction of the impact of support payments on farm-level productivity is unclear as a whole, but the farms that obtain higher levels of non-farm income tend to have a lower productivity in the Netherlands and the United Kingdom. Those part-time farms may reduce the input intensity and may under-invest in productivity improving technology.

Source: Kimura, S. and J. Sauer (2015), “Dynamics of dairy farm productivity growth: Cross-country comparison”,

The relationship between farm size and sustainability performance may also change above a threshold. In the US livestock sector, structural change and the closely connected set of innovations tied to it have led to major improvements in feed efficiency in hogs, dairy, and poultry, which by themselves reduce the amount of feed required per unit of meat or dairy production, and also reduce the amount of manure generated per unit of production. In turn, that reduces the environmental footprint of livestock agriculture (less land and chemicals needed for feed production). However, above a certain size, structural change has also led to the geographic consolidation of livestock and of manure, such that some locales now face excessive concentrations of manure (OECD, 2016a).

copy the linklink copied!Natural resource use and climate change

Natural resource use and climate change clearly influence the range of possible products and choice of adapted production practices, and thus performance. In OECD countries, in the period 1998 to 2012, production growth has generally been achieved while reducing pressure on natural resources (land and water) but not always consistently reducing environmental impacts (Table 2.2).

In many instances, intense natural resource constraints, which posed a challenge for productivity growth, have been the trigger for technological, institutional and/or policy responses that have improved their situation (Gruère et al., 2018). For example, responses to intense droughts have been a major driver of a series of policy changes leading to the development of a sophisticated water allocation system in the Murray-Darling Basin in Australia, which will help farmers and others in the region to cope with future variability in precipitation (ibid.). In the United States, the 2014 drought, which forced farmers to use groundwater intensively, led to a ground-breaking regulatory response in California with the goal of securing groundwater reserves (Cooley et al., 2016). Similarly, the risk of flooding has encouraged the Netherlands to undertake a series of major policy initiatives under the Delta Programme, which included agriculture (OECD, 2016b).

On the contrary, endogenous constraints, such as insufficient, imperfect or asymmetric information, income constraints and costs, and misaligned policy incentives can deter further efforts by farmers to adopt climate-friendly practices. Wreford et al. (2017) showed that there is a wide diversity of barriers to the adoption of agriculture practices contributing to GHG mitigation or adaptation. They find in particular, that barriers relating to the actual or perceived effects of these practices on performance, as well as information and awareness involved in climate change decision-making and risk management, play a primary role in decisions regarding the adoption of climate-friendly measures. They also show that several of the identified barriers are created by existing agriculture policies (Chapter 4).

A cross-comparison of resource and environmental constraints, and the response undertaken by reviewed countries, highlights an evolution in policy priorities along three main sustainability challenges.

  • The first major challenge is to use resources efficiently while continuing to develop agriculture. As the relative scarcity of natural resources increases, resource use cannot follow agriculture production growth, and therefore the sector is stimulated to innovate to improve agriculture productivity growth (producing more with fewer inputs).

  • The second major challenge relates to observed negative environmental externalities from agriculture production, affecting mainly other users of natural resources and responding to societal demand for environmentally friendly agriculture practices. This includes in particular diffuse water pollution, which remains an issue in most reviewed countries. This phenomenon has encouraged governments to undertake policies to redress externalities and preserve the provision of agri-environmental public goods.

  • The third and most recent challenge is associated with climate change. Governments’ increased understanding of climate change consequences has induced a more emphatic focus on risk resilience in agriculture, and encouraged efforts to mitigate GHG emissions in agriculture, an environmental externality that was invisible and therefore not considered in the past. Reducing GHG emissions is a global challenge, which raises the possibility of “carbon leakage” (increased GHG emission in a foreign country when tackled domestically) and competitiveness. GHG emissions in agriculture have been included in the overall mitigation strategy of most OECD countries; but mandatory targets and policies that provide strong incentives to mitigate agricultural emissions have only been implemented in some OECD countries.

While all countries reviewed have engaged in responding to these three challenges, their degree of response and priorities vary depending on a number of factors, including countries’ resource constraints, and their level of agriculture and economic development. For instance, while the Chinese government started to focus on water challenges in agriculture in the first decade of 2000, it only considered introducing agri-environmental policies in 2016, to accompany regulations to tackle environmental externalities. Brazil has made efforts to limit the impact of agriculture development on forests, stimulated in part by international agreements on climate change; however, it has not pursued efforts to reduce air or water pollution from agriculture with the same vigour. Turkey is currently focusing on means to increase water productivity as it continues to expand irrigation. In Australia, the main effort remains that of ensuring resilience of agriculture to water risks, but there are increasing societal demands to push towards sustainable development more generally. The Netherlands, facing land and environmental constraints to growth, has emphasised efforts towards higher productivity and lower environmental impacts. Korea and Japan have also increasingly engaged in efforts to lower the negative impacts of agriculture, while considering how to adapt to climate change. Estonia and Switzerland, not facing as strong resource and climatic constraints, have emphasised the environmental quality challenges, including mitigating GHG emissions, as a priority for their agriculture policies.

Still, more efforts are needed to tackle future climate and water risks in the reviewed countries. While amplitude of impact varies across scenarios, climate change projections suggest negative impact for agriculture in the reviewed countries. Ignaciuk and Mason-D’Croz (2014) find that the yields of maize, wheat and rice would decline by 10%, 7% and 6%, respectively on average in OECD countries, compared to a situation where the current climate conditions would prevail, in the absence of adaptation action. Yields could reduce by as much as 25% in the case of maize in North America or wheat in Australia. Furthermore, existing projections suggest that, unless a response is undertaken, future water risks will continue to affect all reviewed countries although their likelihood and type of risks vary significantly across countries (Figure 3.1). The agriculture productive regions of Northeast China and the Southwest United States could face particularly high water risks due to climatic water supply limitations and continued increased demand from non-agriculture water users (OECD, 2017a). Both climate and water risks were associated with expected commodity price increases at the time, affecting other actors in the market and potentially food security (Ignaciuk and Mason D’Croz, 2014; OECD, 2017).

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Figure 3.1. Future water risks in reviewed countries in the absence of adaptation action
Frequency of global water risk assessments identifying a country as subject to high or very high future water risks without further action
Figure 3.1. Future water risks in reviewed countries in the absence of adaptation action

Note: The figure indicates future water risks starting from current situation.

Source: OECD (2017), Water Risk Hotspots for Agriculture,, based on a review of 64 studies.


copy the linklink copied!Common drivers for agriculture productivity and sustainability

Quantitative analyses of sustainability and productivity are being developed, including using farm-level econometric estimations and modelling approaches. For instance, Lankoski et al. (2018) analysed policy coherence between climate change adaptation, mitigation and productivity, and applied their model in the case of Finland. Using farm level data, they simulated the projected impact of selected policies on TFP, climate change adaptation (modelled as reduced yield variability), GHG emissions, and nutrient balances, accounting for risks and risk behaviour. The analysis shows that policy choices generate inherent trade-offs between productivity, mitigation and adaptation (Chapter 7). An alternative approach combining qualitative and quantitative evidence is that of Fuzzy Set Qualitative Comparative Analysis. OECD (2019b) uses this method to investigate the possible impacts of specific agriculture policy and agriculture structure on productivity and environment.

copy the linklink copied!Main knowledge gaps

Despite continued efforts, the measurement of TFP remains challenging, with different methods and data limitations. These difficulties increase when attempting to incorporate environmental performance in TFP, as information quality and availability remains a limitation.

The diversity of situation also makes generalisation difficult unless evidence from a significant number of diverse sources concur, as is the case for linking innovation and productivity. This suggests the need for increased and concerted efforts across institutions and countries to improve understanding of the different pathways, and underlying conditions leading to improved performance. These are key to improving policy design.


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Bokusheva, R. and L. Čechura (2017), “Evaluating dynamics, sources and drivers of productivity growth at the farm level”, OECD Food, Agriculture and Fisheries Papers, No. 106, OECD Publishing, Paris,

Cooley, H., et al. (2016), “Water risk hotspots for agriculture: The case of the southwest United States”, OECD Food, Agriculture and Fisheries Papers, No. 96, OECD Publishing, Paris,

Gruère, G., C. Ashley and J. Cadilhon (2018), “Reforming water policies in agriculture: Lessons from past reforms”, OECD Food, Agriculture and Fisheries Papers, No. 113, OECD Publishing, Paris,

Ignaciuk, A. and D. Mason-D'Croz (2014), “Modelling Adaptation to Climate Change in Agriculture”, OECD Food, Agriculture and Fisheries Papers, No. 70, OECD Publishing, Paris,

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← 1. See examples of productivity-enhancing technologies in crop and dairy farming in the Estonian review (Box 2.3 and Box 2.4 in OECD, 2018). The US review (OECD, 2016a) reports that steady increases in corn yields resulted from a series of successive biological, mechanical, and chemical innovations resulting from public and private R&D, rather than from any single innovation, and that rapid diffusion of those innovations among farmers played a further important role in driving yield growth.

← 2. See Key (2018) for a discussion of the role of technologies in reducing input costs.

← 3. Rada and Fuglie (2018) report that larger farms have a higher TFP in Australia, Brazil and the United States. In Brazil, the relation between income size and TFP is not linear, but U-shaped, and smaller farms are more productive than medium-sized ones (based on income). Smaller farms are also more productive than medium-sized farms in the developing countries investigated in the article (Bengladesh, Malawi, Tanzania and Uganda).

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Chapter 3. Drivers of agricultural productivity and sustainability performance