Agricultural Productivity: Supporting the United Nation’s Sustainable Development Goals
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Productivity is generally defined as the ratio of the quantity of outputs produced divided by the quantity of inputs employed in the production process. Agricultural productivity is defined as the ratio of the quantity of agricultural outputs employed to produce an agricultural product to the quantity of agricultural inputs used in the production process. Agricultural productivity growth is of great interest as it is linked to economic growth, especially in developing countries where a significant share of GDP and a significant share of labor is in the agricultural sector. Thus, measuring agricultural productivity is the first step in determining which factor of production (inputs) are over- or underutilized and which measures need to be taken to improve agricultural productivity. This is mainly done by comparison across farms, regions, or countries for selected agricultural products or for the agricultural sector as a whole.
So called partial measures of agricultural productivity are often employed with the most commonly used one being labor productivity, which is defined as the ratio of the quantity of agricultural outputs produced to the quantity of agricultural labor input employed in the production of the agricultural output, and land productivity (or yield) which is the ratio of the quantity of agricultural outputs produced to the quantity of agricultural land employed in the production of the agricultural outputs.
Another measure of agricultural productivity is total factor productivity (TFP). TFP is an index-number methodology that calculates the ratio of an index of the quantities of agricultural outputs employed in the agricultural production process to an index of the quantities of agricultural inputs employed in the same agricultural process. This is the most widely accepted measure of what is termed “agricultural productivity” and has been studies extensively in the literature. Changes to TFP over time or across countries, i.e., changes to agricultural productivity, are usually attributed to technological improvements rather than farm sector, farm size, or farmers’ attributes (Coelli et al. 2005).
Agricultural productivity is directly linked to the Sustainable Development Goals as set by the United Nations. More specifically, the second Sustainable Development Goal (SDG 2): “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” requires agricultural productivity to achieve its aims. More specifically, according to Target 2.4, which aims to “ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production,” the keywords of “increased (agricultural) productivity and production explicitly state the connection between agricultural productivity and the SDG 2. Thus, measuring agricultural productivity, determining its causes, and increasing agricultural productivity will assist countries in achieving SDG 2.
365体育网站Agricultural productivity is the means by which we can measure how efficiently inputs into agriculture are turned into outputs. Inputs can include land, labor, capital, as well as more specific factors such as seeds, fertilizers, pesticides, machinery, technology, etc. Increase in agricultural productivity implies that farmers’ income increases, cutting extreme poverty and helping farmers fight against an ever-growing array of threats, including most recently, climate change. Thus, increasing agricultural productivity is in line with the Sustainable Development Goals, as defined by the United Nations, and more specifically, Sustainable Development Goal (SDG) 2: “End hunger, achieve food security and improved nutrition and promote sustainable agriculture.”
365体育网站The agricultural sector is the only economic or industrial sector that can cover the need for healthy, safe, and nutritious food for a growing worldwide population while at the same time feeding more farm animals and furnishing more fiber and fuel for industrial and energy use. At the same time, the agricultural sector must use natural resources sustainably to preserve available land, water, and biodiversity resources and to respond to environmental threats such as climatic change. To meet these challenges, measuring, assessing, and increasing agricultural productivity is key.
Agricultural productivity is the ratio of the quantity of agricultural outputs employed in an agricultural production process to the quantities of agricultural inputs employed in the agricultural production process, and it is used as a measure of the efficiency of agricultural production. Agricultural productivity is an important factor of the production performance of both agricultural holdings, regions and of whole economies. Thus, increasing agricultural productivity, or agricultural productivity growth, is important not only for the agricultural sector, as it increases farmers’ income, but for the whole economy, as it creates opportunities for economic growth and structural transformation, i.e., growth of the entire economy of a country. There are several definitions of productivity, most notably: partial productivity and total factor productivity (TFP).
Partial productivity measures are the ratio of the quantities of agricultural outputs produced during an agricultural production process to the quantities of a specific input employed in the agricultural production process. Most commonly in agriculture, the measures of partial productivity employed are agricultural labor productivity, which the ratio of the quantities of agricultural output produced to the quantities of agricultural labor input employed in the agricultural production process and agricultural land productivity, which is the ratio of the quantities of agricultural output produced to the quantities of agricultural land input employed in the production process. However, due to substitution effects, i.e., due to the fact that farmers may substitute one expensive or inefficient input for a less expensive or inefficient input, partial productivity measures cannot capture “true” productivity growth. For example, land productivity (or yield) may increase due to an increase in the use of fertilizers, resulting in a higher yield per hectare (higher land productivity) that is not captured in the partial productivity denominator that only measures one specific agricultural input, in this case land.
The most comprehensive productivity indicator is total factor productivity (TFP), which reflects the overall efficiency with which producers employ agricultural inputs to produce agricultural outputs (or final products). Total factor productivity is defined as the aggregate quantity of outputs produced by a single farm, a number of farms in a region, or the whole agricultural sector, divided by the aggregate quantity of inputs employed to produce the agricultural outputs. It measures the residual growth that cannot be accounted by the rate of change in the underlying inputs employed and is nowadays considered a rough measure of productivity. TFP is a “measure of our ignorance” (Abramovitz 1956), since it is a residual component of technical change and may include technical and operational innovation, but also measurement error, omitted variables, aggregation bias, and model misspecification (Hulten 2001). Thus, an increase of TFP in agriculture implies achieving higher output with the same amount of inputs or the same output level with fewer inputs.
Agricultural Productivity and Sustainable Development
An important facet of agricultural productivity relates to sustainable development and the achievement of the United Nations’ Sustainable Development Goals (SDGs). More specifically, under the second goal (SDG 2), “End hunger, achieve food security and improved nutrition and promote sustainable agriculture,” Target 2.4 attempts to “ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production,” providing a direct link between sustainable development and agricultural productivity (FAO 2018365体育网站). Sustainable development of the agricultural sector is specifically addressed in SDG indicator 2.4.1.
In addition, agri-environmental issues caused by severe overuse of the natural environment have reduced nature’s productive capacity. Agricultural productivity encompasses managing natural resources to ensure their long-term sustainability while also minimizing the negative impacts of agricultural production on the environment through pollution. Meeting the SDGs in the agricultural sector also requires taking actions to mitigate greenhouse gas emissions and adapt to climate change. Externalities from agricultural production and related environmental issues such as climate change, biodiversity, animal welfare, and food safety have received increasing attention in the discussion of agricultural policy (Alston et al. 1995).
“Sustainable agriculture” is defined as economic activity that is a source of livelihood and as provider and user of environmental services. As such, agricultural productivity acquires a new definition. Agricultural outputs do not only account for agricultural products, but also include “good” or “bad” outputs produced as by-products of the agricultural production process. These could include pollution “produced” by agriculture, a classic example of a “bad” output, which can be measured using agricultural productivity. In a similar fashion, agricultural inputs can also include environmental “inputs” that are employed during the agricultural production process. These could include the depletion of natural resources, if they are employed, directly or indirectly, in the agricultural production process.
There is, therefore, a holistic view of the three dimensions of sustainability: economic, social, and environmental that can be incorporated in measures of agricultural productivity. SDG indicator 2.4.1 is defined as the “percentage of agricultural area under productive and sustainable agriculture.” If a farm is not economically sound or not resilient to external shocks, or if the well-being of those working on a farm is not considered, then a farm cannot be sustainable (FAO 2018365体育网站). The UN highlights that this indicator “was developed through a multi-stakeholder process involving statisticians and technical experts from countries, international organizations, national statistical offices, civil society and the private sector. It brings together themes on productivity, profitability, resilience, land and water, decent work and well-being in order to capture the multidimensional nature of sustainable agriculture.” Sustainable agriculture, thus, encompasses agricultural productivity and makes it an element of the end goal, sustainable development.
Agricultural Productivity and Economic Growth
Increasing agricultural productivity, agricultural productivity growth, has been analyzed extensively over the last half century. This has been an important topic of research for development economists and for agricultural economists alike, as agricultural productivity growth is considered essential for economic growth. This is easy to understand when we consider developing nations where most of their gross domestic product is from the agricultural sector. An increase in agricultural productivity is equivalent to an increase in economic productivity as the agricultural sector is the most significant in their economies. However, agricultural productivity growth is important even for the most developed economies. Because increasing agricultural productivity frees up resources to be employed in other sectors of the economy, most significantly it frees up labor and to a lesser extent land.
More jobs in agriculture and the food supply chain upstream and downstream of the farm
More jobs or higher incomes in nonfarm economy as farmers and farm laborers spend their additional incomes
Increased jobs and incomes in the rural economy would allow better nutrition, better health and increased investment in education among the rural population. This leads directly to improved welfare and indirectly to higher labor productivity
365体育网站Generates more local tax revenues and demand for better infrastructure such as roads, power supplies, and communications. This leads to second-round effects promoting rural economy
Linkages in the production chain generate trust and information, build social capital, and facilitate nonfarm investment, and
365体育网站Reduced prices of food for rural inhabitants who buy in food net
365体育网站The effects on the national economy include (i) reduced prices of food and raw materials raise real wages of urban poor, reducing wage costs of nonfarm sectors; (ii) generation of savings and taxes from farming allows investment in nonfarm sector, creating jobs and incomes in other sectors; (iii) earning of foreign exchange allows import of capital goods and essential inputs for nonfarm production; (iv) release of farm labor allows production in other sectors.
Pingali and Heisey (1996) have categorized the technological transformation of crop production systems into three distinct phases: First, the land-augmentation phase. Second, the labor-substitution phase. Third, the knowledge- and management-intensity phase. The underlying assumption is that the drive of transformation from one phase to the next is the relative scarcity of each of the main inputs to agricultural production. The first phase is characterized by expansion in the area cultivated, land being the main source of output growth as was evident in several developing countries in the 1950s and 1960s. As cultivation intensity rose, this created pressure to more intensively use labor and mechanization, mostly in the 1970s and 1980s. Eventually, diminishing returns of further crop intensification led to an increase in the demand for better technical knowledge and management skills.
However, even after 50 years of empirical research, there is plenty of evidence for correlations between agricultural productivity increases and economic growth but little definitive evidence for a causal connection (Gollin 2010). Gollin (2010365体育网站) argues that agricultural productivity growth is “neither necessary nor sufficient condition for economic growth,” although in developing countries it is nevertheless an important first source of economic growth. We proceed by reviewing the theory and empirical evidence available to date. Due to the vast literature in the field, an effort is made to cover a somewhat representative selection of the literature drawing from both the economic growth and the agricultural development fields.
In the last decades, agricultural productivity improvements have resulted in significant growth of agricultural production; however, there still remain large differences in agricultural productivity among countries, regions and farms (Hayami and Ruttan 1970a). Improving farm productivity at the individual, regional, national, and global level remains a key challenge.
Worldwide, an estimated 40% of the total workforce are employed directly in agriculture (World Bank 2007), and they are a large employer especially in developing countries. Similarly, a large share of GDP comes from agriculture in many developing countries. One of the oldest stylized facts in the growth literature is the negative correlation between income per capita and share of agriculture in GDP, i.e., as an economy grows and income per capita grows, the share the agricultural sector as a percentage of the country’s GDP falls, although the agricultural sector in monetary terms also increases. This may sound counterintuitive, but it is due to the fact that the non-agricultural sectors, i.e., the manufacturing and the services sectors, are growing at a higher rate. This stylized fact is true in cross-country comparisons between poor and rich countries, as it is in time-series data for developed countries (Heston et al. 2006). However, even though agriculture’s share in both employment and GDP are higher in poor countries than in rich ones, the employment share is substantially higher than the GDP share suggesting that agricultural productivity is much lower than productivity in the other sectors of the economy. In fact, the World Bank have (2007) estimated that agricultural income per capita is below $1/day in some countries and below $2/day in several developing countries. By contrast, very few countries have non-agricultural output per capita of less than $2/day (Gollin 2010), highlighting the link between low agricultural productivity and poverty. Given the role of agriculture in providing nutrition, the importance of understanding the causes of agricultural productivity differences is quite important.
As aforementioned, the agricultural sector is generally characterized with low productivity compared to the rest of the economy, and this is especially true in developing countries, mainly due to differences in labor productivity (Caselli 2005; Restuccia et al. 2008). Because developing countries have most of their workforce employed in the agricultural sector, it is tantamount to understand why productivity differences in agriculture may be so large. A concern raised by some researchers regarded the quality of data in developing countries (Jerven 2013). Maybe the huge differences in agricultural labor productivity, and thus income, were just the result of poor-quality data or mismeasurement. However, Gollin et al. (2014) have recently demonstrated using physical measures of labor productivity that these differences are real, at least for the case of staple grains, and are not a result of mismeasurement or poor quality data.
The earliest methods of measuring agricultural productivity were index number approaches (Barton and Cooper 1948; Loomis and Barton 1961). These were usually ratios of an index of an aggregate output to a single input, typically labor or land. The single input, or partial productivity index, masked many of the factors accounting for observed productivity growth, such as the substitution among inputs (Capalbo and Vo 2015). This leads to the replacement of partial productivity measures by the total factor productivity measures in the late 1950s, when output and input indexes were constructed using either a linear aggregation with market prices as weights or a geometric aggregation with factor and revenue shares as weights (Capalbo and Vo 2015).
Research on agricultural productivity increased sometime in the late 1960s and early 1970s, coinciding with a slowdown in productivity in the industrial sector in the USA and the wider use of growth theory (Solow 1956) to explain differences in growth among different countries. Since the 1960s, agricultural output has far outstripped population growth, proving that there would be enough land and agricultural production to feed an ever-growing world population. The increase in agricultural output is mainly due to increases in agricultural productivity rather than increasing farming intensity, extending irrigation, or adding new land as agricultural fields.
Initially, the explanation of economic growth and agricultural productivity was largely a theoretical exercise. Christensen and Yee (1964) were two of the first to describe the mechanics of agricultural productivity and economic growth for an imaginary country named Hypothetica. Hypothetica was a developing country, similar to the ones in the real world, but with a big benefit: data availability. They demonstrated how a country is gradually shifting from subsistence to commercial agriculture in a simplified framework and how agricultural productivity contributes to economic growth. Hayami and Ruttan (1970) were the first to point out that agricultural productivity growth is essential if agricultural output is to grow at a sufficiently rapid rate to meet the demands for food and agricultural raw materials that typically accompany urbanization and industrialization. They stated that “failure to achieve rapid growth in agricultural productivity can result either in the drain of foreign exchange or in shifts in the internal terms of trade against industry, and thus seriously impede the growth of industrial production. Failure to achieve rapid growth in labour productivity in agriculture can also raise the cost of transferring labour, and other resources, from the agricultural to the non-agricultural sector as development proceeds” (Hayami and Ruttan 1970). Hayami and Ruttan (1970) have defined three broad categories of sources of agricultural productivity differences: (a) resource endowments; (b) technology, as embodied in fixed or working capital; and (c) human capital, broadly conceived to include the education, skill, knowledge, and capacity embodied in a country’s population. Moreover, they demonstrated empirically using a mixture of developing and developed countries that these three broad categories account for most (95%) of the differences in labor productivity in agriculture between what was then termed “less developed countries” and developing countries. Moreover, they demonstrated that these three categories are roughly of equal importance, each contributing by about one third to agricultural productivity differences among the studied countries. Furthermore in another study Hayami and Ruttan (1970) showed that countries can follow different paths to development, based on their respective comparative advantages in land, machinery, and fertilizers. Since their seminal work, a number of researchers have analyzed cross-country differences in agricultural productivity, among others Hayami and Ruttan (1970, 1971); Kawagoe and Hayami (1983, 1985); Kawagoe et al. (1985); Antle and Capalbo (1988); and Lau and Yotopoulos (1989365体育网站). Most of the research attributed the economic growth in equal proportions to three factors: input quality changes, economies of scale, and research and development.
Most early empirical analyses relied on time series or cross-country comparisons to determine links between agricultural productivity and economic growth. Even though a number of methodological approaches and data has been employed, there is little that meets contemporary standards of econometric identification (Gollin 2010). This results in correlations between agricultural productivity growth and economic growth that have been empirically demonstrated but where causal relationships aren’t always clear.
Two-sector models were initially developed to explain the transition that takes place from a mainly agricultural-based economy to a mainly industrial-based economy. A number of papers have used two-sector models, including Gollin et al. (2002, 2007) to show that low agricultural productivity hinders economic growth. Low agricultural productivity might be due to poor technology, geoclimatic conditions, institutions, or a mix thereof. The results of this line of analysis suggest that efforts to increase agricultural productivity will lead to an increase in economic growth and should be prioritized. This view was first proposed by Schultz (1964). A key assumption of these models is that they assume a closed economy, that is, no food imports are allowed. This implies that if food cannot be imported and since food is essential for consumption, there is no alternative to growth other than agricultural development through increases in agricultural productivity. Matsuyama (1992) later highlighted this point using a two-sector model of endogenous growth in which the engine of growth is learning-by-doing in the manufacturing sector, preferences are non-homothetic and income elasticity of demand for the agricultural good is less than unitary. Matsuyama’s results indicated that in the case of a closed economy, there is a positive link between agricultural productivity and economic growth. However, in the case of an open economy, there is a negative link, suggesting that openness of an economy is also an important factor of growth that may counteract with agricultural productivity in predicting growth performance. This result is also highlighted by Hansen and Prescott (2002) with a single sector that undergoes a transformation from a traditional agriculture-based economy to a modern service-based economy in a dynamic setting. Using a different methodological approach, Vollrath (2009365体育网站) argues that agricultural productivity is related to fertility and the production of children. Vollrath presents the argument that increases in agricultural productivity will increase fertility and therefore the workforce in agriculture, resulting in reduced output per capita.
A number of papers have assessed the link between agricultural productivity growth and economic growth using cross-section or panel data. Self and Grabowski (2007) have regressed economic growth on a number of variables measuring, among other things, agricultural productivity. They find strong correlation between agricultural productivity and per capita income as well as between agricultural productivity and the human development index (HDI). However, they fail to account for possible endogeneity, an issue raised by the authors, who cannot identify any clear instruments to be employed in correcting for endogeneity. A number of studies examine the causality between agricultural productivity and poverty reduction. Although these studies are highly suggestive of causality, they do not offer convincingly identified causal links (Gollin 2010). Tsakok and Gardner (2007) in a review of the relevant literature argue that “our view is that economists will simply have to face the fact that econometric studies of country data will not be able to establish causality” between agricultural productivity growth and poverty reduction (from Gollin 2010).
An opposing view in the literature claims that agricultural productivity growth has, in fact, a negative effect on economic growth. The “agro-pessimism” theory states that “development policy has suffered from an overemphasis on agriculture, driven by underlying confusion about the causal relationship between agriculture and development” (Gollin 2010). Dercon (2009, 2013) argues that causation may in fact run the opposite way, from economic growth to agricultural productivity. He concludes that there is a tendency to overstate the role the agricultural sector plays in development, resulting in wrong policy design. Dercon argues, using evidence from sub-Saharan Africa, that agricultural growth is only crucial as an engine for growth in particular settings, more specifically in landlocked, resource-poor countries, which are often characterized by relatively low potential for agriculture. In coastal countries and those with richer endowments of natural resources, Dercon argues that countries might be better off by importing food. This view is also echoed by other researchers, among others Ellis and Harris (2004) and Collier (2008).
Agricultural Productivity in Developing Countries
Agricultural productivity growth has been an important aim not only for developing countries but also an important aim of agricultural policies in developing countries. The US Department of Agriculture (USDA) has provided estimates of agricultural productivity (measured as TFP) growth for most countries and regions throughout the world from the 1960s to date. In fact, TFP growth has replaced resource intensification as the primary source of growth in world agriculture (Fuglie and Rada 2017). Using growth accounting, changes in agricultural TFP over time are measured from data provided by FAOSTAT, the database of the United Nations Food and Agriculture Organization (FAO) and data input supplemented from national statistical sources. Furthermore, since higher agricultural productivity can imply reduced demand on resources such as land and water and on agricultural inputs such as fertilizers and pesticides, there are environmental benefits in increasing agricultural productivity. Indeed the European Union’s Common Agricultural Policy has set as the first of its five objectives “to support farmers and improve agricultural productivity, so that consumers have a stable supply of affordable food” (Grant 1997; Shucksmith et al. 2005).
The difficulty of estimating agricultural productivity can be illustrated using data for the EU, a developed economy, from two different data sources (Matthews 2014365体育网站). The EU’s Directorate General for Agriculture (DG-AGRI) using data provided by Eurostat estimated agricultural TFP growth for the EU-15 Member States averaging at 0.3% for the period 2002–2011, whereas the USDA using data from FAOSTAT estimated agricultural TFP growth at 2.3% for the period 2001–2005 and 3.5% for the period 2006–2010, clearly illustrating the difficulty of providing reliable estimates of agricultural productivity and agricultural productivity growth. This difference in estimates is even more pronounced when looking estimates for individual countries. During the period 2001–2010, according to the USDA estimates of TFP growth in agriculture, Italy, Portugal, the Netherlands, Germany, Spain, and Austria had a TFP growth of 3% or more, whereas according to the DG-AGRI estimates, Italy and Spain had negative TFP growth rates, Germany was near zero, and Portugal, the Netherlands, and Austria had between 1 and 2%. Even though several simplifying assumptions may have been made and thus the TFP estimates may not be exactly the same, such large differences are odd and question the validity of the methodologies followed.
These results from the European Union point out to an important fact of agricultural productivity measurement. Even though agricultural productivity is considered important and thus should be measured, it is very hard to do so for developed countries, and one can imagine that it is more so for developing countries, where quality data may be harder to obtain. Since productivity growth in agriculture has enabled farmers to produce a greater abundance of food lowering prices for consumers and increasing income for producers, measuring agricultural productivity worldwide will remain a hot topic for the future.
More recently, estimates suggest that the growth in agricultural productivity may have been slower and more volatile compared to previous years (FT 2019365体育网站). Data indicate that global malnourishment has been increasing for the last 3 years, mostly due to the effects of climate change. The UN Food and Agriculture Organization recently published a report that has estimated that each rise in global mean temperature by one degree Celsius will result in a decrease of average global cereal yields between 3 and 10 percent.
Moreover, two key findings of the report suggest, first, that there is very little evidence linking farm size with agricultural productivity, mainly because new innovations allow smaller landholders to achieve the same agricultural productivity improvements that large landholders can achieve. These include solar-powered water pumps for irrigation, mini-tractors for cultivation of marginal land, and the increase of access to leasing markets for agricultural machinery (FT 2019). Second, actual labor productivity in agriculture appears to have been underestimated in the past due to time spent by farmers in other activities and reported as farm labor. New micro-level data empirically showed that farmers would not necessarily earn a better wage by moving to other jobs available to them. The report suggested that more funding should be directed toward agricultural research and development and toward helping small farmers overcome the barriers to adopting new technologies.
Agricultural Productivity in Developed Countries
The European Commission has recently launched an ambitious program toward a resource efficient Europe in 2020. This has increased the interest in answering the question of whether agricultural productivity can continue to increase in the developed world. Agricultural productivity has increased over time in the EU, but at a slower rate than in the past, and was mainly driven by labor reduction (EU 2019). Productivity improvements were mainly estimated to be the result of the application of better technologies, more efficient management, either technical, allocative, or scale (Cechura et al. 2014). However, the most important factor determining agricultural productivity growth in the long run is innovation, which is driven by investments in agricultural research and development. Several studies have found a significant positive effect on agricultural productivity of investments in innovative technologies. Technologies such as Big Data, either open source or not, plant breeding technologies, multi-actor business models, precision farming, and others could shift the technology frontier upward (EU 2019).
365体育网站Regarding investments in agricultural research and development, countries such as the USA and China are investing ever-increasing amounts. However, at the same time, countries in sub-Saharan Africa and in Southeast Asia are reducing funding for agricultural research and development, even though agricultural research and development has a much higher return than general research and development due to under-investment in the agricultural sector.
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