Daugherty Water for Food Global Institute

 

Date of this Version

2015

Document Type

Article

Citation

FAO and DWFI. 2015. Yield gap analysis of field crops – Methods and case studies, by Sadras, V.O., Cassman, K.G.G., Grassini, P., Hall, A.J., Bastiaanssen, W.G.M., Laborte, A.G., Milne, A.E., Sileshi , G., Steduto, P. FAO Water Reports No. 41, Rome, Italy.

Comments

© FAO and DWFI, 2015

Abstract

The challenges of global agriculture have been analysed exhaustively and the need has been established for sustainable improvement in agricultural production aimed at food security in a context of increasing pressure on natural resources. Whereas the importance of R&D investment in agriculture is increasingly recognised, better allocation of limited funding is essential to improve food production. In this context, the common and often large gap between actual and attainable yield is a critical target. Realistic solutions are required to close yield gaps in both small and large scale cropping systems worldwide; to make progress in this direction, we need (1) definitions and techniques to measure and model yield at different levels (actual, attainable, potential) and different scales in space (field, farm, region, global) and time (short, long term); (2) identification of the causes of gaps between yield levels; (3) management options to reduce the gaps where feasible and (4) policies to favour adoption of gap-closing technologies. The aim of this publication is to review the methods for yield gap analysis, and to use case studies to illustrate different approaches, hence addressing the first of these four requirements. Theoretical, potential, water-limited, and actual yield are defined. Yield gap is the difference between two levels of yield in this series. Depending on the objectives of the study, different yield gaps are relevant. The exploitable yield gap accounts for both the unlikely alignment of all factors required for achievement of potential or water limited yield and the economic, management and environmental constraints that preclude, for example, the use of fertiliser rates that maximise yield, when growers’ aim is often a compromise between maximising profit and minimising risk at the whole-farm scale, rather than maximising yield of individual crops. The gap between potential and water limited yield is an indication of yield gap that can be removed with irrigation. Spatial and temporal scales for the determination of yield gaps are discussed. Spatially, yield gaps have been quantified at levels of field, region, national or mega-environment and globally. Remote sensing techniques describes the spatial variability of crop yield, even up to individual plots. Time scales can be defined in order to either remove or capture the dynamic components of the environment (soil, climate, biotic components of ecosystems) and technology. Criteria to define scales in both space and time need to be made explicit, and should be consistent with the objectives of the analysis. Satellite measurements can complement in situ measurements. The accuracy of estimating yield gaps is determined by the weakest link, which in many cases is good quality, sub-national scale data on actual yields that farmers achieve. In addition, calculation and interpretation of yield gaps requires reliable weather data, additional agronomic information and transparent assumptions. The main types of methods used in yield benchmarking and gap analysis are outlined using selected case studies. The diversity of benchmarking methods outlined in this publication reflects the diversity of spatial and temporal scales, the questions asked, and the resources available to answer them. We grouped methods in four broad approaches.

Approach 1 compares actual yield with the best yield achieved in comparable environmental conditions, e.g. between neighbours with similar topography and soils. Comparisons of this type are spatially constrained by definition, and are an approximation to the gap between actual and attainable yield. With minimum input and greatest simplicity, this allows for limited but useful benchmarks; yield gaps can be primarily attributed to differences in management. This approach can be biased, however, where best management practices are not feasible; modelled yields provide more relevant benchmarks in these cases.

Approach 2 is a variation of approach 1, i.e. it is based on comparisons of actual yield, but instead of a single yield benchmark, yield is expressed as a function of one or few environmental drivers in simple models. In common with Approach 1, these methods do not necessarily capture best management practices. The French and Schultz model is the archetype in this approach; this method plots actual yield against seasonal water use, fits a boundary function representing the best yield for a given water use, and calculates yield gaps as the departure between actual yields and the boundary function. A boundary model fitted to the data provides a scaled benchmark, thus partially accounting for seasonal conditions. Boundary functions can be estimated with different statistical methods but it is recommended that the shape and parameters of boundary functions are also assessed on the basis of their biophysical meaning. Variants of this approach use nitrogen uptake or soil properties instead of water.

Approach 3 is based on modelling which may range from simple climatic indices to models of intermediate (e.g. AquaCrop) or high complexity (e.g. CERES-type models). More complex models are valuable agronomically because they capture some genetic features of the specific cultivar, and the critical interaction between water and nitrogen. On the other hand, more complex models have requirements of parameters and inputs that are not always available. “Best practice” approaches to model yield in gap analysis are outlined. Importantly, models to estimate potential yield require parameters that capture the physiology of unstressed crops.

Approach 4 benchmarking involves a range of approaches combining actual data, remote sensing, GIS and models of varying complexity. This approach is important for benchmarking at and above the regional scale. At these large scales, particular attention needs to be paid to weather data used in modelling yield because significant bias can accrue from inappropriate data sources. Studies that have used gridded weather databases to simulate potential and water-limited yields for a grid are rarely validated against simulated yields based on actual weather station data from locations within the same grid. This should be standard practice, particularly where global scale yield gaps are used for policy decisions or investment in R&D. Alternatively, point-based simulations of potential and water-limited yields, complemented with an appropriate up-scaling method, may be more appropriate for large scale yield gap analysis. Remote sensing applied to yield gap analysis has improved over the last years, mainly through pixel-based biomass production models. Site-specific yield validation, disaggregated in biomass radiation-use-efficiency and harvest index, remains necessary and need to be carried out every 5 to 10 years.

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