In recent decades, numerous studies have focused on technical efficiency in rice farming, finding considerable variation in mean technical efficiency (MTE) estimates. We conducted a meta-regression analysis (MRA), using a random-effects meta-regression model, to understand the variation in MTE estimates due to study heterogeneity, heteroscedasticity and publication bias. We used 443 observations extracted from 175 primary studies published in English in the last three decades. The results show that MTE estimates are affected by study heterogeneity. Variable returns to scale specification yielded higher MTE scores than constant returns to scale ones. Panel data, secondary data and value data had lower MTE estimates than cross-sectional data, primary data and physical (quantity) data, respectively. Compared to Southeast Asia, countries in East and South Asia had higher MTE estimates, whereas African countries had lower MTE estimates. We suggest that practitioners and policy-makers should consider carefully estimation specifications, data types and geographical regions of empirical studies when comparing and interpreting empirical results. The average genuine (predicted) MTE score was 0.76 (range 0.54–0.89), indicating the potential to improve technical efficiency in global rice farming and the need for further research to bridge managerial ability gaps among farmers.

中文翻译：

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量化异质性、异方差性和发表偏倚对水稻种植技术效率估计的影响：元回归分析

近几十年来，许多研究都集中在水稻种植技术效率上，发现平均技术效率 (MTE) 估计值存在相当大的差异。我们使用随机效应元回归模型进行了元回归分析 (MRA)，以了解由于研究异质性、异方差性和发表偏倚导致的 MTE 估计值的变化。我们使用了从过去 30 年以英语发表的 175 项主要研究中提取的 443 项观察结果。结果表明，MTE 估计值受研究异质性的影响。规模收益的可变规范产生的 MTE 分数高于规模收益的恒定。面板数据、二级数据和价值数据的 MTE 估计值分别低于横截面数据、原始数据和物理（数量）数据。与东南亚相比，东亚和南亚国家的 MTE 估计值较高，而非洲国家的 MTE 估计值较低。我们建议从业者和政策制定者在比较和解释实证结果时应仔细考虑实证研究的估计规范、数据类型和地理区域。平均真实（预测）MTE 得分为 0.76（范围 0.54-0.89），表明提高全球水稻种植技术效率的潜力以及需要进一步研究以弥合农民之间的管理能力差距。