There is considerable interest in projections of future productivity growth in agriculture. Whether one is interested in the outlook for global commodity markets, future patterns of international trade, or the interactions between land use, deforestation and ecological diversity, the rate of productivity growth in agriculture is an essential input. Yet solid projections for this variable have proven elusive -particularly on a global basis. This is due, in no small part, to the difficulty in measuring historical productivity growth. The purpose of this paper is to report the latest time series evidence on total factor productivity growth for crops, ruminants and non-ruminant livestock, on a global basis. We then follow with tests for convergence amongst regions, providing forecasts for farm productivity growth to the year 2040. 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, non-ruminant and crop productivity in developing countries may be converging to the productivity levels of developed countries. For ruminants, the results show that productivity levels may be diverging between developed and developing countries.JEL Classification: D24, O13, O47, Q10

The conditional beta distribution is proposed as a parametric model of the probability distribution of agricultural output. A two‐stage maximum likelihood estimation procedure is shown to produce consistent, asymptotically efficient and normal estimates of maximum output and the parameters of the conditional distribution. Application of the procedure to data on corn yield response to fertilizers shows that fertilizers have a significant impact on each of the first three moments of the distribution of corn yield. Corn yield distributions are found to be negatively skewed, implying that above average yields are more probable than below average yields.

Most researchers examining poverty and multilateral trade liberalization have had to examine average, or per capita effects, suggesting that if per capita real income rises, poverty will fall. This inference can be misleading. Combining results from a new international cross-section consumption analysis with earnings data from household surveys, this article analyzes the implications of multilateral trade liberalization for poverty in Indonesia. It finds that the aggregate reduction in Indonesia's national poverty headcount following global trade liberalization masks a more complex set of impacts across groups. In the short run the poverty headcount rises slightly for selfemployed agricultural households, as agricultural profits fail to keep up with increases in consumer prices. In the long run the poverty headcount falls for all earnings strata, as increased demand for unskilled workers lifts incomes for the formerly self-employed, some of whom move into the wage labor market. A decomposition of the poverty changes in Indonesia associated with different countries' trade policies finds that reform in other countries leads to a reduction in poverty in Indonesia but that liberalization of Indonesia's trade policies leads to an increase. The method used here can be readily extended to any of the other 13 countries in the sample. Poverty reduction is an increasingly important consideration in the deliberations on multilateral trade liberalization, and it has been accorded an important position in the Doha Development Round of the World Trade Organization (WTO) negotiations. 1 Globkom and the World Bank sponsored a conference in Stockholm in October 2000 aimed at assessing the state of the art in

The supply chain in the food and agribusiness sector is characterized by long supply lead times combined with significant supply and demand uncertainties, and relatively thin margins. These challenges generate a need for management efficiency and the use of modern decision technology tools. We review some of the literature on applications of decision technology tools for a selected set of agribusiness problems and conclude by outlining what we see as some of the significant new problems facing the industry. It is our hope that we will stimulate interest in these problems and encourage researchers to work on solving them.applied optimization, agriculture, crop planning

The objective of this research was to use recent ractopamine research data to develop an updated mathematical model to describe the daily compositional growth of pigs fed ractopamine. Mean increases of 18.2, 23.1, and 25.0% for daily protein accretion were assumed for 5, 10, and 20 ppm of ractopamine for an overall gain of 40 kg of BW gain during the feeding period. The relative effect of ractopamine described the rapid increase and subsequent decrease in the effect of ractopamine as a function of BW gain or days on test and ractopamine concentration (RC, ppm). The reduction in ME intake produced by ractopamine was described as 0.036 x (RC/20)(0.7) multiplied by the ME intake for the first 20 kg of BW gain, and then increasing to 0.078 x (RC/20)(0.7) at 40 kg of BW gain feeding period. The ratio of fat-free muscle gain to protein accretion increased by 14 to 16% with the feeding of ractopamine, depending on the dietary lysine/essential AA levels. The ratio of carcass fat gain to empty body lipid gain was increased when lysine and essential AA requirements were met. Daily protein accretion and fat-free lean growth were described as functions of dietary lysine/essential AA intakes. The percentage of lysine in protein accretion increased with the feeding of ractopamine from 6.80 to 7.15%, depending on ractopamine concentration. Equations predicting carcass measurements, such as fat and longissimus muscle depths from carcass weight and composition, were modified to incorporate prediction biases produced by ractopamine. For the four concentrations of ractopamine (0, 5, 10, and 20 ppm, respectively) during a 78 to 110 kg of BW feeding period, the model predicted performance levels for ADG (1.03, 1.15, 1.16, and 1.16 kg/d), gain:feed (kg of ADG/kg of ADFI; 0.360, 0.401, 0.412, and 0.425), dressing percentage (75.1, 76.0, 76.3, and 76.4), percentage fat-free lean (48.7, 51.0, 51.5, and 52.2), longissimus muscle area (38.8,41.8,42.5, and 43.5 cm2), 10th-rib fat depth (22.1, 19.8, 19.3, and 18.7 mm), and fat-free lean gain (321, 446, 467, and 495 g/d), comparable to recent research data. The model allows the effect of ractopamine to be added to farm specific pig growth curves. It can be used to evaluate ways to optimize the use of ractopamine, including duration of ractopamine feeding, concentration of ractopamine, and dietary lysine concentration.