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The yield gap (Yg ) concept is based on definition and measurement of yield potential. Yield gap may be defined as the difference between yield potential or water-limited yield and actual yields (Van Ittersum et al. 2012; Lobell, Cassman, and Fields, 2009); but Mueller et al. (2012) and Titonell and Giller (2013) define yield gap as the difference between ‘attainable yields’ and landscape-level observed yields. Examining yield gaps is important as policy makers work toward ensuring food security at the micro and macro level in a sustainable manner. Due to favorable climate, soil quality, and access to irrigation, some regions may have greater potential for sustainable agricultural intensification. To understand the concept of yield gap, we will define some key components in the calculation:
- Yield potential (Yp ) – is the yield of a crop cultivar grown with no limitation of water and nutrients and biotic stress effectively controlled (Evans, 1996; Van Ittersum and Rabbinge, 1997). Therefore for a given site when a crop is grown to achieve yield potential, growth rate is determined by; solar radiation, temperature, water supply, and genetic traits that govern length of growing period. Yield potential is defined for irrigated systems where crops are given adequate water through the growth process.
- Water-limited potential yield (Yw ) for rain-fed crops – is similar to yield potential but crop growth is limited by water supply. It is therefore influenced by soil type (water holding capacity and rooting depth) and field topography (runoff) (Van Ittersum et al. 2012).
- Locally attainable yield (Yl ) is the maximum yield achievable by resource endowed farmers in the most productive fields. These yields are more conservative than absolute biophysical potential yields because are achieved using current technology and management techniques (Titonell and Giller, 2012; Mueller et al. 2012).
- Average yields (Ya ) is the actual yield achieved in a farmers’ field.
Yield gap can be estimated as the difference between and Ya and Ya or Yl or . Lobell et al. (2009) identify 4 methods of estimating yield gap at local level that can be used to obtain the yield potential (maximum attainable yield) and these include: 1) crop model simulations, 2) maximum farmers’ yield based on surveys, 3) yield contest, 4) field experiments. In this case we are going to assume that yield ceiling will be obtained from crop modelling or field experiments.
Yield potential is estimated mainly using crop models that assume perfect management using lack of all yield reducing factors. A short coming of crop models is that they lack the sensitivity to short term abiotic stress and leads to higher estimates of potential yields than would occur in the field. As for field experiments, the difficulty of achieving perfect yield conditions increases with plot size and with year to year climate variation, which may be large at a location therefore requiring a time series of experimental data to ensure a mean estimate that reflects a range of climates. A combination of crop model simulation and field experiments is recommended to provide more robust estimates. The use of maximum farmer yields may be used to estimate yield potential in locations where farmers intensively manage a crop with the possibility of achieving the yield potential. Actual yield estimates in the field (i.e. crop cuts) are recommended to complement farmer reported values (Lobell, Cassman, and Fields, 2009). Crop production capacity under rain-fed and irrigated conditions can be analyzed by estimating yield potential and water limited yields as a benchmark. This is essential for sustainable agricultural intensification.
How to operationalize the metric
Method of data collection and data needed to compute the method:
To estimate yield potential, data on the actual yield of the farmer or scientist should be collected. This can be done using the methods that are used to estimate “Yield” in the crop productivity indicator above. Yield estimates can be obtained either through farmer recall or through actual measurements, such as crop cuts, of experimental or farmer managed plots. In this example, we assume that to obtain the yield potential, the scientists used crop modelled yield potential that is calibrated for the given cultivar and the biophysical conditions of the area. For the data needed to calibrate a model such as Decision Support System Agrotechnology Transfer (DSSAT) consult (Jones et al., 2003) and for Agricultural Production System sIMulator (APSIM) consult (Keating et al., 2003). One may use the help of a crop modeler to obtain these estimates or obtain this data through available secondary sources. See also the crop modeling method for yield estimates detailed above.
Unit of analysis:
Once this data has been obtained we can calculate the yield gap in kilograms per hectare. Where crop modelling is used to determine the potential yield, there is need to adjust this yield to reflect the ‘actual’ farm conditions. For example, at the field experiment or in the crop model, there is timely application and management of inputs which may not be the case in most farmers’ fields. Lobell et al. (2009), have indicated in their study that there is no area that has achieved a yield above 80% of the modelled yield potential (Lobell, et al. 2009) and we suggest that in most studies, an adjustment factor should be used. To calculate the yield gap (kg/ha) is calculated as;
Limitations regarding estimating and interpreting:
Yield gaps tend to be difficult to compare across location and studies because of inconsistent terminology, concepts, and methods. Measured yields at field experimental fields may be biased because stations are often situated on soils that have suitable topography making it poorly representative of surrounding topography. Yield potential requires perfection in management of all other determining factors from sowing (plant population, supply and balance of 17 essential nutrients, and protection against loses from insects, weeds, and disease) to maturity.