Comparing the technical efficiency of rice farms in urban and rural areas: a case study from Nepal

Abstract: This study focuses on comparing the technical efficiency of rice farms in two locations with different level of urbanization. A production function using maximum likelihood method is estimated and efficiency score of individual household is calculated using stochastic frontier analysis. The efficiency scores are regressed on variables including farm-household characteristics and degree of output market commercialization. The empirical evidence suggests that the elasticity of production to land size and biological inputs like chemical fertilizer, pesticide, fungicide and seed is positive and statistically significant. The average efficiency scores in two sample districts indicate that the production can be increased by 26-33% through improving efficiency in a given technological condition. The result suggests that the degree of commercialization has positive effect on technical efficiency. Furthermore, household characteristics like education, age, share of agriculture in total household income, sharecropping also have a significant effect on technical efficiency.

Full paper

http://scialert.net/qredirect.php?doi=tae.2012.48.60&linkid=pdf

suj

Energy for all

Global Hunger Index 2011

IFPRI has recently published
Global Hunger Index
The challenge of hunger: Taming price spikes and excessive food price volatility

A very important book from IFPRI. It deals about why price hike in food, what are the effects and how to tackle it.
Following is the pdf link

http://www.ifpri.org/publication/2011-global-hunger-index

Sources of Agricultural Productivity in Low and Lower Middle Income Countries

Sources of Agricultural Productivity in Low and Lower Middle Income Countries Sujan PIYA1) and Akira KIMINAMI2) 1) Corresponding Author, Graduate School of Agricultural and Life Sciences, The University of Tokyo 2) Professor, Graduate School of Agricultural and Life Sciences, The University of Tokyo (Received February 18, 2010) (Accepted December 5, 2010) Abstract Assessing and quantifying the sources of agricultural productivity across developing countries is the prime objective of this paper. Thirty-one low and lower middle income countries from the Asian and African continents were selected for the study. The results show that variations in land productivity are well explained by variations in labor and fertilizer resources. However, variations in tractor and livestock inputs have low impacts on the performance of land productivity. The average annual malmquist factor productivity index is positive. Factor productivity was further divided into two components; technical change and efficiency change indices. The results show the technical change index is higher than the technical efficiency change index. The trends in productivity factors indicate convergence across developing countries after 1990. for full paper http://www.jstage.jst.go.jp/article/srs/41/1/41_77/_article

The 9 billion-people question

The world’s population will grow from almost 7 billion now to over 9 billion in 2050.

A special report on feeding the world

from Economist

http://www.economist.com/node/18200618

 

Evaluating the impact of geographic concentration on Nepalese agricultural export

1. Evaluating the impact of geographic concentration on Nepalese agricultural export
http://sae.sagepub.com/content/11/2/207.abstract
Abstract:

This study observed and analyzed the behaviour of Nepalese agricultural export using vector error correction model (VECM). The first part of the analysis focused on the dynamic relationship among agricultural export, geographic concentration and total agricultural production. Results of the study revealed that Nepalese agricultural export was very much dependent on the Indian markets and had positive relation with the geographic concentration index in the long run. The significant and higher error correction term indicated that the short-run fluctuations in geographic concentration and agricultural production were promptly adjusted to its long-run trend. The long-run effect of agricultural production increment on export was positive. The second part of the analysis focused on the export of niche products. The analysis in vector auto regression (VAR) form indicated that the relationship between export of niche products and geographic concentration was insignificant. Granger causality test revealed that the production granger causes export and export granger causes geographic concentration.

Full paper
http://sae.sagepub.com/content/11/2/207.full.pdf+html


2. The effect of price and non- price factors on agricultural production in Nepal: A cointegration analysis 
http://www.aesjapan.sakura.ne.jp/wp/wp-content/uploads/2010/05/ronbunshu2009_e.pdf

Some free software

1. to calculate water productivity- http://save-the-rain.com/world-bank/
2. land use calculator- http://www.lianpinkoh.com/luc/
3. Immigrants moving money- http://www.torre.nl/migrantsmovingmoney/