Explaining rice yields and yield gaps in Central Luzon, Philippines: An application of stochastic frontier analysis and crop modelling

Joao Nunes Vieira da Silva*, Pytrik Reidsma, Alice G. Laborte, Martin K. Van Ittersum

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

75 Citations (Scopus)


Explaining yield gaps is crucial to understand the main technical constraints faced by farmers to increaseland productivity. The objective of this study is to decompose the yield gap into efficiency, resource andtechnology yield gaps for irrigated lowland rice-based farming systems in Central Luzon, Philippines, andto explain those yield gaps using data related to crop management, biophysical constraints and availabletechnologies.Stochastic frontier analysis was used to quantify and explain the efficiency and resource yield gaps anda crop growth model (ORYZA v3) was used to compute the technology yield gap. We combined these twomethodologies into a theoretical framework to explain rice yield gaps in farmers’ fields included in theCentral Luzon Loop Survey, an unbalanced panel dataset of about 100 households, collected every fourto five years during the period 1966–2012.The mean yield gap estimated for the period 1979–2012 was 3.2 ton ha−1in the wet season (WS)and 4.8 ton ha−1in the dry season (DS). An average efficiency yield gap of 1.3 ton ha−1was estimatedand partly explained by untimely application of mineral fertilizers and biotic control factors. The meanresource yield gap was small in both seasons but somewhat larger in the DS (1.3 ton ha−1) than in theWS (1.0 ton ha−1). This can be partly explained by the greater N, P and K use in the highest yielding fieldsthan in lowest yielding fields which was observed in the DS but not in the WS. The technology yield gapwas on average less than 1.0 ton ha−1during the WS prior to 2003 and ca. 1.6 ton ha−1from 2003 to 2012while in the DS it has been consistently large with a mean of 2.2 ton ha−1. Varietal shift and sub-optimalapplication of inputs (e.g. quantity of irrigation water and N) are the most plausible explanations for thisyield gap during the WS and DS, respectively.We conclude that the technology yield gap explains nearly half of the difference between potentialand actual yields while the efficiency and resource yield gaps explain each a quarter of that differencein the DS. As for the WS, particular attention should be given to the efficiency yield gap which, althoughdecreasing with time, still accounted for nearly 40% of the overall yield gap.
Original languageEnglish
Pages (from-to)223-241
JournalEuropean Journal of Agronomy
Publication statusPublished - 2017


  • Crop modelling
  • Oryza sativa L.
  • Philippines
  • Rice
  • Stochastic frontier analysis
  • Yield gap
  • Yield variability


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