The public R&D and productivity growth in Australia's broadacre agriculture: is there a link?

This paper investigates the dynamic relationships between research and development (R&D) expenditure and productivity growth in Australian broadacre agriculture using aggregate time series data for the period 1953 to 2009. The results show a cointegrating relationship between R&D and productivity growth and a unidirectional causality from R&D to TFP (total factor productivity) growth in Australian broadacre agriculture. Using the dynamic properties of the model, data from beyond the sample period are analysed by employing the variance decomposition and the impulse response function. The findings reveal that R&D can be readily linked to the variation in productivity growth beyond the sample period. Furthermore, the forecasting results indicate that a significant out‐of‐sample relationship exists between public R&D and productivity in broadacre agriculture.     

Nonparametric estimates of productivity and efficiency change in Australian Broadacre Agriculture

This paper computes and decomposes Färe‐Primont indexes of total factor productivity of Australian broadacre agriculture by estimating distance functions. Using state‐level data from 1990 to 2011, the empirical results show that TFP grew at an average rate of 1.36 per cent per annum in the broadacre agriculture over the period 1990–2011. There are variations of total factor productivity (TFP) growth across states and fluctuations over time within each state and territory. However, overall, there is a clear movement towards slower TFP growth across the sample period. Further decomposition of TFP growth shows that it is declining growth in technical possibilities (technological progress) that is the main driver of the declining trend in productivity growth in broadacre agriculture in Australia.     

THE RETURNS FROM RESEARCH IN AUSTRALIAN BROADACRE AGRICULTURE*

Many people share the view that too little is invested in R & D in agriculture. The relationship between several measures of productivity and research expenditure was estimated using data from ABARE's surveys of broadacre industries and a new data series on R & D expenditure for the period 1953 to 1988. The internal rate of return to research was estimated to be in the range of 15 to 40 percent which does not provide strong evidence that Australia is either under- or over-investing in public research.     

Nonparametric measures of the impact of public research expenditures on Australian broadacre agriculture

Nonparametric methods are used to measure the impact of public research expenditures on Australian broadacre agriculture over the 1953–94 period. Results using both unrestricted and 30-year lagged specifications of the research impacts on productivity suggest that while certain aspects of the nonparametric multi-input/output technologies are quite robust to alternative specifications (in particular, the associated Malmquist total factor productivity indexes), other aspects are less stable (in particular, the indexes on input and, to a lesser extent, output biased technical change). Internal rates of return to research expenditures on Australian broadacre agriculture are estimated to be in the 12 per cent to 20 per cent range.     

Input Substitution,Productivity Performance and Farm Size

This paper develops a theoretical model to examine the relationship between the input elasticity of (technical) substitution and both farm total factor productivity and size. In the presence of ongoing technical change and its factor bias, the ‘income effect’ arising from farms' cost minimising behaviour enables them to increase productivity by saving inputs or, through the dual equivalent, enlarging farm size. As such, farms with higher elasticities of substitution tend to grow larger and become more productive, which provides a new mechanism through which farm heterogeneity in productivity growth can be examined. Empirical evidence from Australian broadacre agriculture supports this theory and points to important policy implications.     

Resource reallocation and its contribution to productivity growth in Australian broadacre agriculture

This article uses farm survey data to measure the contribution of cross‐farm resource reallocation to industry‐level productivity growth in Australian broadacre agriculture. We show that resource reallocation between farms mainly occurred between incumbent farms and between farms with different productivity growth. Resource reallocation is estimated to account for around half of the industry‐level productivity growth that occurred between 1978 and 2010, and its contribution appears to have increased over time. Moreover, we also show that resource reallocation effects vary across different inputs, partly due to their different mobility. This analysis improves our understanding of how reforms targeting structural adjustment – and the resource reallocation this generates – can influence aggregate productivity growth.     

Marginal abatement costs of greenhouse gas emissions: broadacre farming in the Great Southern Region of Western Australia

Broadacre agriculture is a major emitter of greenhouse gases (GHG). To improve efficiency of climate change policies, we need to know the marginal abatement costs of agricultural GHG. This article combines calculations of on‐farm GHG emissions with an input‐based distance function approach to estimate the marginal abatement costs for a broadacre farming system in the Great Southern Region of Western Australia. The results show that, in the study region, the average marginal abatement cost for the 1998–2005 periods was $29.3 per tonne CO₂‐e. Farms with higher crop output shares were found to have higher marginal abatement costs. Overall, our results indicate that broadacre agriculture is among the lowest cost sources of GHG mitigation.     

Role of communication technologies in broadacre agriculture in Australia: an empirical analysis using panel data

This paper examines the role of communication technologies (CTs) in Australian broadacre agricultural production using data over the period of 1990–2013. Allowing for cross‐sectional independence in the data, the pooled mean group and augmented mean group techniques are applied to estimate dynamic relationships among variables. The empirical results demonstrate that CTs affect agricultural output positively in the long run. The estimated elasticity is 0.237. This result suggests that government policies that lift investment in telecommunication facilities are shown to contribute to an increase of output in Australia's broadacre agriculture in the long run.     

Broadacre farm productivity and profitability in south‐western Australia

This paper examines broadacre farm performance in south‐western Australia. This region has experienced pronounced climate variability and volatile commodity prices since the late 1990s. Relationships between productivity and profitability are explored using panel data from 47 farms in the study region. The data are analysed using nonparametric methods. By applying the Fare‐Primont index method, components of farm productivity and profitability are measured over the period 1998–2008. Growth in productivity is found to be the main contributor of profitability. Gains in efficiency and technical change are identified as jointly and similarly important in their contribution to total factor productivity for the farm sample in the region from 1998 to 2008. However, across environments, efficiency gains play an increasingly important role in influencing productivity as growing season rainfall increases. We conclude that R,D&E that delivers further improvement in technical efficiency and technical change is needed to support the profitability of farms across the study region.     

PRODUCTIVITY GROWTH IN AGRICULTURE (REVISITED): A MEASUREMENT FRAMEWORK AND SOME EMPIRICAL RESULTS

This paper explores the methodology of sectoral productivity growth measurement within the framework of input-output analysis. A method which ensures that sectoral productivity growth rates are consistent with productivity growth rates for the economy as a whole (as well as allowing for external trade) is suggested. New estimates are presented on total factor productivity in UK agriculture using a Tornqvist index procedure. In addition, new estimates of agricultural labour productivity growth are presented, taking account not only of labour employed in agriculture but also of labour employed in ancillary industries on the supply side. The empirical results confirm that part of the labour previously employed on farms has shifted “upstream” to industries providing agriculture with fertilises, machinery and other inputs.     

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.     

Climate change and Australia's comparative advantage in broadacre agriculture1

Australia has long been a major exporter of the products of broadacre agriculture, a production system well suited to the economic and climatic conditions of the country. Presumably, it holds a comparative advantage in these products, among which grain crops and grazing livestock predominate. However, the future plausibility of this proposition is sensitive to the projected impacts of climate change. This article develops a framework to quantify the future patterns of comparative advantage in broadacre agriculture, given the projections of several global climate models. We find empirical support for the conventional wisdom, and note substantial resilience and robustness in Australia's comparative advantage under a number of scenarios.     

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。     

Increasing agricultural productivity while reducing greenhouse gas emissions in sub‐Saharan Africa: myth or reality?

The motivation for this study stems from two major concerns that are interlinked. The first is the decades long food insecurity crisis faced by sub‐Saharan African (SSA) countries which is still prevalent. The second is the negative impact greenhouse gas (GHG) emissions from agriculture may have on future food production and which is likely to worsen the food insecurity problem. The conundrum SSA farmers face is how to increase food output through productivity growth while minimizing GHG emissions. To measure changes in productivity growth and GHG emissions, this study evaluates the agricultural performance of 18 SSA countries by utilizing the Malmquist–Luenberger index to incorporate good and bad outputs for the years 1980–2012. The empirical evidence demonstrates that productivity is overestimated when bad outputs are not considered in the production model. The analysis provides a better understanding of the effectiveness of previous mitigation methods and which informs an appropriate course of action needed to achieve the twin objectives of increasing agriculture productivity while reducing GHG emissions.     

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.     

Measuring productivity growth when technological change is biased–a new index and an application to UK agriculture

Productivity growth is conventionally measured by indices representing discreet approximations of the Divisia TEP index under the assumption that technological change is Hicks-neutral. When this assumption is violated, these indices are no longer meaningful because they conflate the effects of factor accumulation and technological change. We propose a way of adjusting the conventional TFP index that solves this problem. The method adopts a latent variable approach to the measurement of technical change biases that provides a simple means of correcting product and factor shares in the standard Tornqvist-Theil TFP index. An application to UK agriculture over the period 1953–2000 demonstrates that technical progress is strongly biased. The implications of that bias for productivity measurement are shown to be very large, with the conventional TFP index severely underestimating productivity growth. The result is explained primarily by the fact that technological change has favoured the rapidly accumulating factors against labour, the factor leaving the sector.     

成渝经济区生态农业对区域经济影响分析

[目的]区域经济发展反映经济各个方面的综合发展状况,探讨其影响因素以确定区域未来经济的发展方向,为进一步规划发展提供科学依据。[方法]先构建生态农业评价指标体系再采用层析分析法确定权重,结合线性加权和函数计算生态农业评价指数,通过相关分析法分析生态农业各个指标与区域经济的关系,最终确定生态农业对成渝经济区经济的影响程度。[结果]生态农业指标中评价指数大于0.9是森林覆盖率,介于0.8~0.7的包括投入产出比、土地生产率、农业科技贡献率、万人拥有的科技人员、劳动生产率、化肥施用量。且生态农业中各个指标与GDP都正相关,其中森林覆盖率、人均收入增长率、农业科技贡献率和万人拥有的科技人员与GDP显著相关,对区域经济的影响最大。[结论]成渝经济区经济受生态农业的影响,进一步选取更多生态农业相关因素,对于全面促进区域经济持续发展具有十分的重要现实意义。     

成本收益视角下中国设施蔬菜生产效率研究以黄瓜、番茄栽培为例

目的 文章以番茄与黄瓜为例，以设施、露地蔬菜栽培宏微观数据为支撑，以纬度和降雨为划分区域依据，从成本收益及效率评价视角出发，回答中国设施农业发展方向的问题。方法 运用比较分析以及SFA-Malmquist方法，测算与拆解全要素生产率指标。结果 （1）从中国设施现状来看：中国设施蔬菜具“高产量、高收益、高价格”的“三高”优势和“收益率优势不显著、劳动生产率低”的“双重”劣势；中国设施蔬菜产业仍属“劳动密集型”产业；设施蔬菜经济投入相对较高，影响设施蔬菜的竞争力水平。（2）从设施栽培全要素生产率来看：中国设施农业全要素生产率稳定增长，但生产效率徘徊不前，栽培品种间、区域间全要素生产率增长的主要驱动因素不同。（3）对比设施和露地效率：全要素生产率的变动基本一致；生产效率表现为“设施>露地”，露地栽培种间效率差异大；技术进步增长表现为“设施<露地”，区域特征显著。结论 中国设施蔬菜全要素生产率稳定递增，属于“技术进步诱导+技术效率推动”的增长模式。     

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。     

The Source of Productivity Growth in Dutch Agriculture: A Perspective from Finance

In corporate finance, the impact of capital structure on firm performance has been widely studied. This article extends the capital structure study to the situation in agriculture, explicitly addressing the difference between family farms and corporate firms. We use the Malmquist productivity growth index as a proxy for performance to study the impact of capital structure (debt) on farm performance. We compare the results with those from the traditional performance model that uses profitability (e.g., return on equity (ROE)) as performance measure. Using data from Dutch arable farms, results show that debt has no effect on ROE, whereas it has a positive effect on productivity growth.