亚洲生产力组织（APO）

The Asian Productivity Organization (APO)is an intergovernmental organization thatpromotes productivity as a key enablerfor socioeconomic development andorganizational and enterprise growth. Itpromotesproductivity improvementtools, techniques, and methodologies;supportsthe national productivityorganizations of its members; conductsresearch on productivity trends; anddisseminates productivity information,analyses, and data.The APO was establishedin 1961 and comprises 21 members. APO Members Bangladesh, Cambodia, Republic of China,Fiji, Hong Kong, India, Indonesia, IslamicRepublic of Iran, Japan, Republic of Korea,Lao PDR, Malaysia, Mongolia, Nepal,Pakistan, Philippines, Singapore, Sri Lanka,Thailand, Turkiye, and Vietnam. AGRICULTURALPRODUCTIVITY IN ASIA Agricultural Productivity in Asia Written by C.J. O’Donnell and A. Peyrache. First edition published in Japanby the Asian Productivity Organization1-24-1 Hongo, Bunkyo-kuTokyo 113-0033, Japanwww.apo-tokyo.org © 2025 Asian Productivity Organization The views expressed in this publication do not necessarily reflect the official views ofthe Asian Productivity Organization (APO) or any APO member. All rights reserved. None of the contents of this publication may be used, reproduced,stored, or transferred in any form or by any means for commercial purposes withoutprior written permission from the APO. CONTENTS FOREWORDV INTRODUCTION1Trends in the Performance of the Agriculture Sector1Current Status of Agricultural Productivity Measurement2Current Explanations for Agricultural Productivity Change3 PRODUCTIVITY CONCEPTS AND ANALYTICAL METHODS5Production Technologies5Common Assumptions5Output Sets6ODFs6Managerial Behavior7Output Maximization7TFP Maximization7Measures of Efficiency8Output-Oriented Technical Efficiency (OTE)8TSME8Index Numbers9Output Indices9Input Indices9TFP Indices10Data Envelopment Analysis (DEA)10Estimating OTE10Estimating TSME11Decomposing TFP Change12Stochastic Frontier Analysis (SFA)12Output-Oriented Models12Maximum Likelihood (ML) Estimation13Bayesian Estimation13Decomposing TFP Change14 DATA AND ESTIMATION16 Data16Countries16Variables20Data Cleaning20Estimation21DEA21SFA22TFP23 PRODUCTIVITY CHANGE BY REGION24Africa24The Americas27Asia29Europe32 PRODUCTIVITY CHANGE BY COUNTRY35Australia35Bangladesh38Cambodia41China44Fiji47France50Germany53India56Indonesia59I.R. Iran62Japan65The ROK68Lao PDR71Malaysia74Mongolia77Nepal80Pakistan83The Philippines86Sri Lanka89Thailand92Turkiye95The UK98The USA101Vietnam104 MONITORING AGRICULTURAL PRODUCTIVITY CHANGE IN ASIA108Issues and Challenges108Recommendations109 LIST OF CONTRIBUTORS116 FOREWORD Agriculture has long been at the heart of Asia’s economic and social progress.It feeds the region, sustains rural livelihoods, and underpins many nationaleconomies. This report, Agricultural Productivity in Asia, takes a fresh and detailedlook at how productivity has changed over time, the forces shaping it, and theopportunities ahead. Written by Professor Christopher O’Donnell and AssociateProfessor Antonio Peyrache of the University of Queensland, it combines a clearanalytical framework with decades of carefully assembled international data. Buildingon an earlier 2019 study by the same authors,which covered 91countries(1961–2015)and used climate zone proxies for environmentaleffects,this updated analysis benefits from a richer dataset and improvedmethods.The study draws on harmonized data from the United StatesDepartmentof Agriculture,FAO,and WB,covering a 61-year span(1961–2021). In total, 143 countries are examined, including 18 APO members forwhom sufficient data are available. The scope is broad: Outputs range fromcropsand livestock to fish and greenhouse gas emissions, while inputs includeland, labor,fertilizer, and capital. By incorporating direct climate indicatorssuchas rainfallandtemperature,the analysis better captures climatevariability. To analyze productivity, the authors apply two complementary techniques: dataenvelopmentanalysis and stochastic frontier analysis.The stochastic frontieranalysis technique not only gauges performance, but also accounts for statisticalnoise, an important safeguard when dealing with large, complex datasets. ForAsian economies, improvements in output-oriented scale-mix efficiency were thepredominant driver of TFP growth, while technical progress played a smaller role;changes in technical efficiency and environmental conditions were comparativelylimited. A key recommendation is the call for stronger, more frequent farm-level datacollection. The authors emphasize collaboration with reputable statistical agenciesand national statistical offices to refine survey design and improve data quality,includingexpanded coverage of input–output quantities,better tracking ofenvironmentalfactors,and systematic documentation of farm managementtechnologies and practices. In an era marked by climate volatility, demographic shifts, and changing markets,evidence of this