Case Study: United States

biofuels united states case study

Crop Yield Gaps: Their Importance, Magnitudes, and Causes

David B. Lobell, Kenneth G. Cassman and Christopher B. Field Annu.

Rev. Environ. Resour. 2009. 34:179–204

When assessing the impacts of biofuels on developing countries, why is it important to consider yields?

From Lobell et al. 2009: Demand for both food and energy is quickly rising and will continue to rise with increases in global population and average income. By 2030, global cereal demand for food and animal feed alone is expected to total 2.8 billion (B) tonnes per year, or 50% higher than in 2000(1). The additional demand from future biofuel consumption is less clear but could be considerable.

Future trajectories of food prices, food security, and cropland expansion are closely linked to future average crop yields in the major agricultural regions of the world. Because the maximum possible yields achieved in farmers’ fields might level off or even decline in many regions over the next few decades, reducing the gap between average and potential yields is critical.

We view an understanding of yield gaps as important for at least two reasons. First, it helps to inform projections of future crop yields for different regions and crops because close proximity of yields to their upper limits may indicate that growth rates are likely to slow in the future (10, 11). Second, knowledge of factors that contribute to the yield gap is useful for efficiently targeting efforts to increase production. Critical questions, for instance, are whether the smallest observed yield gaps in the world reflect a fundamental limit to yields, or whether it is possible with new technologies to achieve average yields even closer to potential. To answer these questions requires knowledge of which specific factors represent the largest constraints to productivity in the world’s major cropping systems.

What are yield gaps

From Lobell et al. 2009: Yield gaps are estimated by the difference between yield potential and average farmers’ yields over some specified spatial and temporal scale of interest.

The yield gap is a concept that rests on the definition and measurement of yield potential. We define yield potential as the yield of an adapted crop variety or hybrid when grown under favorable conditions without growth limitations from water, nutrients, pests, or diseases (9). For any given site and growing season, yield potential is determined by three factors: (a) solar radiation, (b) temperature, and (c) water supply.

We use the term yield potential for irrigated systems because it is assumed that an irrigated crop can be provided with adequate water supply throughout growth. In contrast, we refer to maximum possible yields under rainfed conditions as “water-limited yield potential” because most rainfed crops suffer at least short-term water deficits at some point during the growing season. All three environmental factors vary throughout the year, and therefore yield potential will depend not only on location but also on the crop-sowing date and maturity rating.

How are yield gaps measured

From Lobell et al. 2009: Yield potential is a concept, rather than a quantity, which makes estimation both challenging and complicated (3). By definition, yield potential is an idealized state in which a crop grows without any biophysical limitations other than uncontrollable factors, such as solar radiation, air temperature, and rainfall in rainfed systems. Therefore, to achieve yield potential requires perfection in the management of all other yield determining production factors (such as plant population; the supply and balance of 17 essential nutrients; and protection against losses from insects, weeds, and diseases) from sowing to maturity. Such perfection is impossible under field conditions, even in relatively small test plots let alone in large production fields. Thus, yield potential is sometimes estimated by crop models that assume perfect management and lack of all yield-reducing factors.

Three main techniques for assessing yield potential and yield gaps over relevant spatial scales include:

  • Model simulations: Crop models have been used to estimate crop yield potential at scales ranging from a specific field to a region or country.
  • Field experiments and yield contests: Direct measures of yield potential can be made in field experiments that utilize crop management practices designed to eliminate all yield-reducing factors, such as nutrient deficiencies or toxicities, damage from insect pests and diseases, and competition from weeds.
  • Maximum farmer yields: An alternative but less common approach to estimating yield potential is to observe the maximum yield achieved among a sizable sample of farmers in a region of interest.

Given the importance of yield potential and the limitations associated with the three most common methods discussed above, there is a need for continued innovation and evaluation of alternate techniques. Two approaches that appear deserving of more study are the use of crop yields across analogous climates and the use of productivity in the preexisting or neighboring natural ecosystems.

How wide are yield gaps?

From Lobell et al. 2009: A survey of the literature on wheat, rice, and maize cropping systems revealed a wide range of estimated yield gaps throughout the world (Table 2). For tropical maize in Africa, where biophysical and management conditions result in frequent nutrient, water, pest, and disease stresses, average yields are commonly less than 20% of yield potential. In contrast, average yields in irrigated wheat systems in northwest India can reach 80%of potential. The full range of values in Table 2 extends from 16% to 95%, although the true range is likely narrower owing to measurement errors that result in spuriously high or low values.We consider a range of 20% to 80% to include nearly all of the major cropping systems of the world.

Two examples of yield gap analysis further explore yield gaps, comparing simulated yield potential from crop models with average reported farmer yields.

U.S. maize yields. Here we draw upon recent simulations of rainfed and irrigated maize yield potential at 18 sites in the United States over three years using the Hybrid-Maize model (21).The average ratio of county yields to yield potential was 65% across all sites and years for rainfed maize, and 75% for irrigated maize. Although more detailed analysis is needed, the values of 65% and 75% for relative yields suggest that maize yields in this important system have relatively little room to grow before reaching the practical limit of observed yield gaps, which is about 80% of yield potential.

Asian rice yields. As another example of yield gap analysis to supplement the existing literature, we compared a recent gridded dataset of average rice yields circa 2000 (33) with model simulations of irrigated rice yield potential in Asia (Figure 4a) (42). Despite shortcomings in datasets, Figure 4 reveals clearly that in most environments in which nearly all rice is produced with irrigation, namely Japan, Korea, and southern China, average yields are frequently 75% or more of estimated yield potential.

What do current research efforts say about yield gaps?

From Lobell et al. 2009: Key points from our analysis of yield gaps include:

  • Improving crop yields at a pace commensurate with growth in food demand will likely require significant reductions in current yield gaps around the world.
  • Several methods exist to measure crop yield potential and associated yield gaps, each of which has distinct advantages and disadvantages. Estimates of yield potential can often differ by 50% or more, with estimation especially difficult for rainfed conditions.
  • A wide range of yield gaps are observed around the world, with average yields ranging from roughly 20% to 80% of yield potential.
  • Many irrigated cropping systems, including maize in the United States, wheat in South Asia and Mexico, and rice in Japan and Korea, have yields at or approaching 80% of yield potential. This implies that yield gains in these regions will be small in the near future, and yields may even decline if yield potential is reduced because of climate change. Many rainfed cropping systems, in contrast, appear to have relatively large yield gaps that could be closed with existing technologies but persist largely for economic reasons.
  • Raising average yields above 80% of yield potential appears possible but only with technologies that either substantially reduce the uncertainties farmers face in assessing soil and climatic conditions or dynamically respond to changes in these conditions (e.g., sensor-based nutrient and water management). Although these tools are more often discussed because of their ability to reduce costs and environmental impacts, their role in improving future crop yields may be just as important.
What are future research directions to better quantify and understand yield gaps?

From Lobell et al. 2009:

1.) Several questions that may improve quantification of yield gaps include:

  • Can historical records of average yields be disaggregated to finer spatial scales and by irrigated versus rainfed systems in order to aid comparison with simulated yield potentials?
  • What are the uncertainties surrounding modeled estimates of yield potential, particularly in rainfed systems with heterogeneous soil properties?
  • How well can yield potential of one crop (e.g., maize) be used to predict yield potential of another (e.g., switchgrass), and how well can the net primary productivity of native ecosystems predict yield potential of crops?
  • How well does the difference between maximum and average farmer yields, increasingly available from either remote sensing or ground-based surveys (47), represent the true yield gap in a region?

2.) Several questions that may improve understanding of yield gap causes include:

  • How do yield gaps differ when estimated on the basis of average yields over different timescales (i.e., are the highest yields always achieved on the same fields and with the same farmers)?
  • Are yield gaps bigger in cropping systems that experience wider ranges of variation in soil and climate? Do the ranges of farmer management decisions, such as input application rates or planting dates, provide a measure of the amount of uncertainty farmers face?
  • What do model simulations of farmer behavior with different levels of soil and climate uncertainty predict about the response of yield gaps to improved information technologies? How do average yields change as these technologies are increasingly adopted in actual farmer fields, and what impedes the adoption of these technologies?

With a more comprehensive effort that utilizes new remote sensing, geospatial analysis, simulation models, field experiments, and on-farm validation to assess yield gaps throughout the world, it should be possible to better understand the trajectory of the modern food economy and the key leverage points with which to most effectively improve both food production and environmental quality.