MEASURING PRODUCTIVITY GROWTH IN AUSTRALIAN BROADACRE AGRICULTURE

An important source of growth for Australian broadacre agriculture has been technical progress. We compare alternative measures of productivity growth including the traditional Tornqvist-Thiel total factor productivity index; variants of this approach that allow decreasing returns to scale; the Fisher ideal index; other nonparametric measures that do not impose particular functional forms and an econometric estimate from a translog industry cost function. The annual growth in productivity in broadacre agriculture over the period from 1953 to 1994 was in the range of 2.4 to 2.6 per cent and hence was quite robust to measurement technique.     

非粮化对粮食绿色全要素生产率的门槛效应研究

[目的]非粮化关乎国家粮食安全,研究非粮化对粮食绿色全要素生产率的门槛效应,有利于正确认识非粮化,也为制定差异化农业绿色发展政策提供理论依据。[方法]文章以2008—2018年我国26个省（市、自治区）为样本,在运用SBM-GML模型测算粮食绿色全要素生产率的基础上,结合面板门槛模型和耦合协调度模型,探究了非粮化与粮食绿色全要素生产率之间存在的非线性关系。[结果]（1）样本期内所考察地区的粮食绿色全要素生产率均实现破“1”增长,且相较于粮食主产区、产销平衡区以及全国平均值而言,粮食主销区的粮食绿色全要素生产率增长更为显著。（2）非粮化对粮食绿色全要素生产率的影响,确存在以种粮利润为门槛变量的单一门槛效应,其门槛值为7.638,且两者之间的负向关系在跨过门槛值之后将显著大幅提升。（3）分区域来看,各区域非粮化与种粮利润之间耦合协调度的变动趋势,与其粮食绿色全要素生产率的变动趋势基本一致。[结论]采取差异化的非粮化控制措施,可以最大程度上提升粮食绿色全要素生产率。低门槛地区可建立非粮化预警机制,而高门槛地区则应采取严格控制措施,但都须将种粮利润协同保护作为重要工具手段。     

Energy Productivity Growth in the Dutch Greenhouse Industry

Profitability of Dutch greenhouse firms is largely dependent on energy costs, and policy makers focus on reducing the use of energy by these firms. This article uses Russell measures of TE to develop indicators of energy productivity growth. Results show that energy productivity grew by 2.8% annually in the period 1976–95, with technical progress being the key factor driving the process of productivity growth. From this we conclude that policies aiming at enhancing the adoption of technological innovations are more effective, if due attention is paid to measures for improving TE.     

Bridging the Gap between Partial and Total Factor Productivity Measures Using Directional Distance Functions

Technical dependencies as well as data constraints limit our ability to allocate inputs across sectors and hence our ability to measure sectoral productivity. We adapt a directional measure of efficiency to the measurement of sector-specific productivity that does not require allocating all inputs across sectors. Applied to the agricultural sector of a group of countries, the results show important differences in livestock and crops productivity growth. Commonly used partial factor productivity measures for livestock and crops tend to overestimate productivity growth in most developing countries while underestimating it in European countries.     

中国农业全要素生产率的动态演进及其影响因素分析

[目的]通过测算中国农业全要素生产率,揭示其动态演进趋势及影响因素,从而为推动我国农业供给侧结构性改革,实现农业现代化提供科学的决策依据。[方法]文章使用SBM-Global Malmqusit生产率指数法测算了中国农业全要素生产率,在此基础上利用Kernel核密度估计方法刻画了中国农业TFP增长的动态演进,并通过建立面板数据模型对影响中国农业TFP增长的诸多因素进行了分析。[结果]中国农业全要素生产率增长的主要源泉是技术进步,而技术效率的下降是阻碍其增长的主要原因;核密度估计表明各省份之间的农业相对全要素生产率水平差距不断扩大,技术效率普遍恶化,而技术进步增长速度显著;农业金融发展水平、工业化水平、农业科技水平对中国农业TFP都产生了显著的促进作用,而农业产业结构调整、对外开放水平对中国农业TFP则产生了阻碍作用,农村人力资本则对农业TFP产生的正效应不显著。[结论]提高农业全要素生产率,实现农业现代化.各省份应加强农业科技创新,深化农业科技体制改革,完善农村金融服务体系,推动工业化和农业现代化深度融合,培育新型职业农民等措施。     

Productivity growth and convergence in crop, ruminant, and nonruminant production: measurement and forecasts

Projections of future productivity growth rates in agriculture are an essential input for a great variety of tasks, ranging from development of an outlook for global commodity markets to the analysis of interactions between land use, deforestation, and ecological diversity. Yet solid projections for these variables have proven elusive—particularly on a global basis. This is due, in no small part, to the difficulty of measuring historical total factor productivity growth. Consequently, most productivity projections are based on partial factor productivity measures that can be quite misleading. The purpose of this work is to provide worldwide forecasts of agricultural productivity growth till the year 2040 based on the latest time series evidence on total factor productivity growth for crops, ruminants, and nonruminant livestock. The results suggest that most regions in the sample are likely to experience larger productivity gains in livestock than in crops. Within livestock, the nonruminant sector is expected to continue to be more dynamic than the ruminant sector. Given the rapid rates of productivity growth observed recently, nonruminant and crop productivity in developing countries may be converging to the productivity levels of developed countries. For ruminants, the results show that productivity levels in developing countries are likely to be diverging from those in developed countries.     

Multifactor productivity growth and the Australian mining sector

A commonly used, but unadjusted, measure of Australian mining multifactor productivity (MFP) fell by about one‐third over the first decade of the mining boom, coinciding with very large increases in resource prices. Using growth accounting methods and our own adjustments, based on energy use and capital‐output lags to account for depletion effects we find (i) the Australian annual average MFP growth in mining was 2.5 per cent a year between 1985–1986 and 2009–2010 compared to ?0.65 per cent for the unadjusted measure and (ii) productivity growth was positive in the 2000s, albeit at a lower rate than in the 1990s. Our adjusted MFP growth measures at a state level and subsector level are greater than unadjusted productivity measures. In a complementary study using an econometric decomposition of mining MFP at a state level, we find no statistically significant effect of technological change on MFP growth in the sector, but positive and statistically significant effects of technical efficiency and scale over the period 1990–1991 to 2009–2010. Our results do not support specific policy interventions to increase productivity growth in the mining sector beyond appropriate incentives for resource exploration including the provision of precompetitive resource data.     

供给侧结构改革背景下的西南地区农业全要素生产率分析

［目的］探究在制度改革背景下的农业生产力发展水平和地区差异,有利于全面了解农业发展状况,为后期农业政策的制定、调整以及农业发展提供理论基础。［方法］文章利用DEA-Malmquist指数法对我国西南地区5个省（市）农业全要素生产率（TFP）水平变化发展趋势进行分析,并在此基础上对不同地区农业TFP差异变化和发展趋势进行预测。［结果］（1）Malmquist指数分析结果表明,我国西南地区农业TFP呈缓慢增长的趋势。通过对不同地区农业TFP结果进行比较,四川省农业TFP有先逐渐上升后下降的趋势,而技术进步是影响四川省农业TFP增长的原因;（2）重庆市农业TFP也是呈现波动式缓慢增加的趋势,年平均增长率为354%; 贵州省农业TFP近20年来增长速度最快,主要影响因素为技术进步; 云南和广西农业TFP也均呈缓慢增长的趋势,技术效率和技术进步在不同时期对农业TFP的影响有所差异。（3）收敛性分析结果表明,我国西南地区农业TFP差异有减小的趋势,并且不同地区农业TFP水平具有向各自稳定状态发展的可能性。［结论］我国西南地区农业TFP增长与技术进步具有较高的同步性,因此,制约我国西南地区农业全要素生产率增长的关键因素是农业科技进步。在供给侧结构性改革背景下,必须以市场需求为科研导向,加大科技创新投入力度,促进我国西南地区农业的可持续发展。     

Sources and Measurement of Agricultural Productivity and Efficiency in Canadian Provinces: Crops and Livestock

This paper measures and assesses the variation in total factor productivity (TFP) growth among Canadian provinces in crops and livestock production over the period 1940–2009. It also determines if agricultural productivity growth in Canada has recently slowed down as indicated by earlier studies. The paper uses the stochastic frontier approach that incorporates inefficiency to decompose TFP growth into technical change (TC), scale effect (SE), and technical efficiency change. The results indicate that productivity changes were mainly driven by TCs for crops, while the productivity changes in livestock was mainly driven by SEs and technical progress. Though change in technical efficiency is mainly positive (except for New Brunswick and Nova Scotia), its contribution to productivity growth was very little for the provinces. We also found that over the entire period, the productivity growth rates for the crop subsector are on average higher for the Prairie provinces than for the Eastern and Atlantic provinces. On the other hand, the productivity growth rates in the livestock subsector are on average higher in the Eastern and Atlantic provinces than in the Prairie region with the exception of Manitoba. Finally, we found that though there is some evidence of a recent decline in productivity growth for the crops subsector, there is no such evidence in the livestock subsector.     

生产性服务业对农业全要素生产率的影响研究基于中介效应的影响路径分析

目的 在农业高质量发展的背景下,探讨农业生产性服务业发展对农业全要素生产率的切实影响及其作用机制。方法 文章基于DEA-Malmquist指数测算了2001—2019年全国农业全要素生产率增长变动情况,探究农业生产性服务业发展对农业全要素生产率的影响贡献,并通过中介效应模型对其影响路径进行实证检验。结果 农业生产性服务业发展水平与农业全要素生产率间呈现“倒U型”关系,生产性服务业发展对生产效率的促进作用部分通过农业生产专业化分工程度的提高以及经营规模的扩大得以实现;不同阶段、不同发展水平的生产性服务业对农业全要素生产率的促进作用和影响路径存在差异,生产性服务业发展初期主要通过促进农业生产分工和专业化水平提升来推动生产效率增长,后续则表现为规模效应和专业化效应的共同影响。结论 农业生产性服务业发展有助于提升农业全要素生产率,未来应持续推进农业生产性服务体系建设,引导农业生产性服务业适度发展,助力农业现代化转型。     

中国苹果绿色全要素生产率测算与产区差异

目的 厘清苹果产业绿色全要素生产率增长的动态变化及产区差异,是促进苹果产业高质量发展的关键。方法 文章基于2004—2018年我国8个苹果主产区面板数据,利用超效率SBM模型和全局ML指数,从静态、动态不同角度,对我国苹果环境技术效率和绿色全要素生产率进行测算,并分析不同产区之间的差异。结果 （1）我国及各主产区苹果环境技术效率均未达到最优状态,区域差异明显,黄土高原主产区高于环渤海湾主产区,与不考虑非期望产出相比,环境技术效率明显偏低;（2）规模效率是当前我国苹果生产技术效率主要来源,黄土高原主产区纯技术效率略高于规模效率,环渤海湾主产区纯技术效率偏低;（3）苹果绿色全要素生产率整体提高,区域间增速差异较大,黄土高原主产区增速高于环渤海湾主产区;（4）技术效率是我国苹果绿色全要素生产率主要增长源泉,技术进步贡献度相对较低。结论 我国苹果绿色全要素生产率存在较大提升空间,各产区要通过因地制宜制定苹果产业发展战略、充分借助农业科研院所创新优势、加强果农高素质培训等方式提高苹果绿色全要素生产率。     

Using a Programming Approach to Measure International Agricultural Efficiency and Productivity

Multifactor agricultural productivity for seventy countries is calculated using a programming method. Productivity measures are divided into indices that measure technical efficiency and technical change. Agriculture in many developing countries is technically inefficient but technical change has had a greater impact on agricultural productivity. Multifactor productivity is declining in many developing countries where both agricultural output and the use of some agricultural inputs has rapidly grown. The level of education in a country and research services are factors which can explain differences in agricultural productivity growth between countries.     

The Creation and Spread of Technology and Total Factor Productivity in China's Agriculture

The overall goal of this study is to create a framework for assessing the trends of China's national and international investment in agricultural research and to measure its impact on total factor productivity. The main methodological contribution is to provide more convincing measures of crop–specific technologies from China's national research program and of those imported from the international agricultural research system. Our results find that from 1980 to 1995, China's total factor productivity for rice, wheat, and maize grew rapidly and new technology accounts for most of the productivity growth.     

Productivity growth and inter-sector spill-over in Dutch horticulture, 1976–1995

This article calculates nonparametric measures of total factor productivity growth on Dutch horticultural firms in the period 1976–1995. Individual components of total factor productivity growth, i.e., efficiency change and technical change are regressed on socioeconomic factors reflecting the effects of the world oil crises, household and demographic characteristics, location, and investment in physical capital. Also, the article investigates the presence of inter‐sector spill‐overs between groups of firms with different specializations in Dutch horticulture.     

US TENDANCES DE LA PRODUCTIVITE TOTALE DANS L'AGRICULTURE: CANADA, ONTARIO, QUEBEC, 1926–64

This paper measures and analyses the changes in total productivity of Canadian, Ontario and Quebec agriculture for the period 1926 to 1964. The rate of growth in productivity from 1945 to 1964 has been about the same for Canadian and Ontario agriculture while that of Quebec has been higher than either by one per cent a year. Total productivity gains correspond to decreases in the average costs of production at the farm level. Quebec's farmers have therefore improved their relative position as competitors. These productivity gains have important and far reaching implications not only for fanners and the agricultural industry but also for the provinces and the entire Canadian economy.     

Productivity improvement in Korean rice farming: parametric and non‐parametric analysis†

The published empirical literature on frontier production functions is dominated by two broadly defined estimation approaches – parametric and non‐parametric. Using panel data on Korean rice production, parametric and non‐parametric production frontiers are estimated and compared with estimated productivity. The non‐parametric approach employs two alternative measures based on the Malmquist index and the Luenberger indicator, while the parametric approach is closely related to the time‐variant efficiency model. Productivity measures differ considerably between these approaches. It is discovered that measures of efficiency change are more sensitive to the choice of the model than are measures of technical change. Both approaches reveal that the main sources of growth in Korean rice farming have been technical change and productivity improvements in regions of the country that have been associated with low efficiency.     

海南天然橡胶长势及产胶潜力遥感监测系统研发

[目的]为准确及时地监测海南天然橡胶长势变化、估算天然橡胶产胶潜力,为相关部门提供决策依据,并实现风云三号气象卫星资料在天然橡胶产业进行气象服务的应用示范作用。[方法]文章基于风云三号气象卫星资料、同期气象观测数据以及海南天然橡胶种植信息等,采用不同时间天然橡胶NDVI的对比、与历史同期常年平均值的对比分析等方法进行橡胶长势监测,并基于CASA模型构建天然橡胶净初级生产力估算模型,再结合天然橡胶干物质分配率构建了天然橡胶产胶潜力估算模型,在此基础上,利用Visual Stdio.net 2010、MeteoInfo平台采用C#语言设计开发了海南天然橡胶长势及产胶潜力遥感监测系统。[结果]系统主要包括基础数据库、天然橡胶长势监测、天然橡胶产胶潜力估算、专题图制作等功能。[结论]通过天然橡胶长势监测技术方法和天然橡胶产胶潜力估算方法的应用以及监测系统的建立,可及时了解天然橡胶的长势规律及产胶潜力变化,快速掌握气象灾害对天然橡胶生长造成的影响,为提高干胶产量提供了技术和软件支撑。     

Spillovers

Interstate and international spillovers from public agricultural research and development (R&D) investments account for a significant share of agricultural productivity growth. Hence, spillovers of agricultural R&D results across geopolitical boundaries have implications for measures of research impacts on productivity, and the implied rates of return to research, as well as for state, national and international agricultural research policy. In studies of aggregate state or national agricultural productivity, interstate or international R&D spillovers might account for half or more of the total measured productivity growth. Similarly, results from studies of particular crop technologies indicate that international technology spillovers, and multinational impacts of technologies from international centres, were important elements in the total picture of agricultural development in the 20th Century. Within countries, funding institutions have been developed to address spatial spillovers of agricultural technologies. The fact that corresponding institutions have not been developed for international spillovers has contributed to a global underinvestment in certain types of agricultural research.     

Asian Agricultural Productivity and Convergence

This paper measures agricultural total factor productivity (TFP), for eighteen Asian countries, from 1965 to 1996. TFP is measured by calculating the Malmquist index with respect to the sequential frontier, which is appropriate when the cross section is relatively small. The results show that half the countries have experienced negative productivity growth, due to losses in technical efficiency combined with stagnation in technological progress. Both cross‐section and time series tests show that there is no evidence of convergence in agricultural productivity for these countries. The less productive countries are falling further behind, rather than catching up. Finally, comparisons with Africa show that although Asia has had faster TFP growth than Africa, three of the five African regions (East, Central and Southern) have grown faster than any of the Asian regions.     

甘肃省农业环境效率及碳排放约束下农业全要素生产率测算研究

[目的]考虑碳排放约束,分析甘肃省农业全要素生产率整体特征和区域特征,以期为提升甘肃省农业可持续发展能力提供参考。[方法]文章基于2005—2016年甘肃省14市州面板数据,以农业碳排放作为非期望产出指标,运用非径向、非角度的SBM模型和Malmquist—Luenberger生产率指数法,对甘肃省农业环境效率和碳排放约束下农业全要素生产率指数进行测算。[结果](1) 2005—2016年甘肃省农业环境效率值总体呈现下降—上升—趋于平缓的变化趋势,且均小于1;(2)碳排放约束下甘肃省农业全要素生产率总体呈现波动增长的趋势,年均值为1.010,年均增长率为1%,且主要来源于技术进步;(3)甘肃省农业全要素生产率增长存在显著区域差异,其中中部地区增长最快,陇东南地区次之,河西地区增长最慢。[结论]忽视碳排放将不利于农业可持续发展,建议各区域依据自身资源禀赋,采取差异化的发展思路,提高农业技术成果转化率是提升甘肃省农业全要素生产率的关键。