IPI International Potash Institute
IPI International Potash Institute

Research Findings: e-ifc No. 14, December 2007

Yield potential and yield gaps of maize in Southeast Asia

Pasuquin, J.M.C.A., and C. Witt, IPNI-IPI Southeast Asia Program, Singapore

Quantifying the yield potential of maize at any given site is a key to understanding the existing yield gaps and identifying the most important constraints to achieving optimal yield and profit. Understanding the causes of these yield gaps allows farmers to prioritize their efforts in improving yield and profit in a sustainable and environmentally sound fashion. It also maximizes the return of investment in research and development (e.g. irrigation facilities). Yield gaps are analyzed stepwise by estimating the yield potential, the attainable yield, and the actual yield in farmers' fields (Fig. 1).

Fig. 1: Schematic overview of yield gap analysis.
Fig. 1: Schematic overview of yield gap analysis.

The yield potential (Yp) of a crop is defined as the theoretical maximum yield in any given season solely determined by climate and germplasm assuming ample supply of water, nutrients, or other yield building factors and the complete absence of yield reducing factors such as pests and diseases. Yp is commonly estimated using plant growth models.

The attainable yield (Yt) is defined as the yield achieved in farmers' fields with best management practices including water, pest, and general crop management where nutrients are not limiting. The attainable yield varies - like the yield potential - from season to season and year to year depending on climate. The optimal economic yield is often linked to the attainable yield. The maximum attainable yield (Yt1) in any given season could be close to the yield potential, if management is excellent and weather conditions are very favorable.

For the development of site-specific fertilizer recommendations, we recommend using the attainable yield of the last 3-5 years as the yield target. In favorable rain-fed and irrigated areas, the yield target is often about 80-90% of the yield potential. In less favorable areas or seasons, this value is somewhat lower (70- 80% of the yield potential).

The actual yield in farmers' fields (Ya) is often lower than the attainable yield due to constraints like water availability, pests and diseases, and poor crop and nutrient management practices.

Actual, attainable, and potential yield can be used to identify exploitable yield gaps (Fig. 1). A management objective of farmers should be to minimize yield gap 3, the difference between attainable and actual yield (Yt-Ya). To narrow this yield gap, farmers need to evaluate promising new technologies (e.g., planting density, nutrient management) that offer improvements in yield and/or productivity against current practices. Larger yield increases can be achieved when several constraints (e.g. pests and disease problems and inappropriate nutrient management) are overcome simultaneously.

Yield gap 2 is largely determined by factors that are difficult or impossible to control including the variation in climatic conditions. Best management practices such as the use of a leaf color chart (LCC) for fine tuning N management increase the likelihood of keeping yield gap 2 small.

Yield gap 1 provides important guidance in the identification of constraints. If yield gap 1 is large despite following best management practices, attainable yield must be limited by an unknown constraint. If yield gap 1 is small, there is no further room for yield improvement and efforts might focus on enhancing productivity. It is usually not economical to aim at fully reducing yield gap 1 because of the large amounts of inputs required and the high risk of crop failure and profit losses. This yield gap is smaller in seasons with very favorable weather conditions.

Estimating potential yield
The yield potential (Yp) is commonly estimated with crop growth simulation models. One such model is Hybrid-Maize developed by the University of Nebraska, Lincoln (Yang et al., 2006). It is designed to provide information and better understanding of maize yield potential and the interactive effects of crop management practices and climate on maize yields. The model can simulate the growth of a maize crop under non limiting or water-limited (rain-fed or irrigated) conditions based on daily weather data. Specifically, it allows users to:

  • assess the site yield potential and its variability based on historical weather data,
  • evaluate changes in attainable yield using different combinations of planting date, hybrid maturity, and plant density,
  • analyze yield in relation to the timing of silking and maturity in specific years,
  • assess soil moisture status and explore options for irrigation management, and
  • conduct in-season simulations to evaluate current crop status and predict final yield at maturity as a range of yield-outcome probabilities based on historical climate data for the remainder of the growing season.
Start up window of the Hybrid-Rice software (Yang et al., 2006).
Start up window of the Hybrid-Rice software (Yang et al., 2006).

Yield potential and actual yield of maize in Southeast Asia
The yield potential of maize at several locations in Indonesia, the Philippines, and Vietnam is depicted in Fig. 2. At a plant population of 65,000 plants/ha, the average potential yield varies from 10 to 16 mt/ha depending on site and date of planting. It should be noted that maize is grown under rain-fed conditions at all sites except for irrigated maize grown in Nueva Ecija in the Philippines so that it is not always possible to plant maize in a month that would promise the highest potential yield. By definition, potential yield is only determined by germplasm and climate without considering water availability. The Hybrid-Maize software offers a module to simulate water limited yield which requires a basic soil characterization including soil texture and the water content in the soil profile at the beginning of the season. However, estimates of potential yield provide a good benchmark for actual and attainable yields estimated in farmers' fields. The analysis of yield potential by planting date then offers additional information for optimizing planting dates and crop rotations in the favorable tropical environments where two to three crops including rice, maize, or wheat are grown annually.

Fig. 2: Yield potential of maize as simulated by the Hybrid-Maize model (click to enlarge).
Fig. 2: Yield potential of maize as simulated by the Hybrid-Maize model for three Asian countries sown at different planting dates with a plant population of 65,000 plants/ha. Error bars are standard deviation from different sites/years of weather data (Indonesia: Lampung, 2000-2005; Wonogiri, 1999-2004, Makassar, 1994-2003. Philippines: Laguna, 1994-2003; Nueva Ecija, 1994-2001; Zamboanga, 1990- 2004. Vietnam: An Giang, 1999-2005; Dak Lak, 1997-2005; Dong Nai, 1998-2005; Vinh Phuc, 1992-2002).
click to enlarge figure

A comparison of actual, attainable, and potential yield for selected sites in Southeast Asia suggests substantial opportunities for Asian maize farmers to increase yield and profit (Table 1). Their average actual yields (Ya) are considerably lower than the attainable yield (Yt) with optimal crop management and ample nutrient supply. The maximum attainable yield was often close to the crop's climatic-genetic yield potential. As a general rule, optimal yield targets should probably be within 70 to 80% of potential yield in favorable irrigated or rain-fed maize environments.

  Table 1. Actual, attainable, maximum attainable, and simulated yield potential of maize at selected sites in Southeast Asia, 2004-2007. Data are the average of five farms per site in at least three seasons. Attainable yield was estimated in treatments with ample supply of fertilizer N, P, and K. The maximum attainable yield is the single highest yield observed an NPK treatment at each site. The simulated potential yield was estimated with Hybrid-Maize model using actual planting densities at project sites. Data source: IPNI-IPI project on site-specific nutrient management for maize in Southeast Asia. Unpublished.  
  Site Average
  Wonogiri, Central Java, Indonesia 4.9 5.7 7.3 12-14  
  Lampung, Indonesia 7.2 9.2 13.7 12-14  
  Nueva Ecija, Philippines 7.9 9.0 14.2 15-18  
  An Giang, Vietnam 8.3 8.8 10.3 14-16  
  CuM’gar, Dak Lak, Vietnam 6.2 7.8 12.0 14-15  


  • Yang, H.S., Dobermann, A., Cassman, K.G., and D.T. Walters. 2006. Hybrid-Maize (ver. 2006). A simulation model for corn growth and yield. Nebraska Cooperative Extension CD 9 [online]. Available at http://www.hybridmaize.unl.edu/Index.htm (last update 2006; accessed 29 Nov. 2006). Lincoln, NE: University of Nebraska.

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