Investing in Livestock Development in Water-Scarce Semi-Arid Watersheds: Technological, Institutional and Policy Dimensions

Watershed Development Programmes (WDPs) in India were conceived as tools for correcting the regional imbalances in agricultural development created by Green Revolution, through investments in soil and water conservation (SWC) and natural resource management (NRM) in rainfed areas. Though the overall impact of WDPs has been positive and significant, with increase in physical and economic access to groundwater, landless and marginal households hardly benefited from watershed development. Recent evidence points out that in many watersheds inequities increased, since for non-land owning and -well owning households access to drinking water, grazing lands and other natural resources decreased. This paper is based on a research project carried out by the International water Management Institute, South Asia in India during 2002-2005, which attempted to document and understand the livestock-environment-livelihood interactions in watersheds in semi-arid India. This paper elucidates the important role of livestock in livelihoods of communities in water-scarce watersheds and demonstrates that unless livestock interventions are consciously and astutely planned, with due consideration to protecting livelihoods and environment, WDPs might not result in equitable benefits. Limited access to livestock-related institutional services and social organization does not contribute to sustainable livestock-livelihood-environment interactions. The paper highlights the major role that technological and institutional factors play in bringing about the livestock development that would impact positively on livelihoods and the accompanying policy changes that are necessary.watersheds, livestock, environment, livelihoods, markets, services, Livestock Production/Industries, O13, Q56, Z13,

Is Stack Overflow Overflowing With Questions and Tags

Programming question and answer (Q & A) websites, such as Quora, Stack Overflow, and Yahoo! Answer etc. helps us to understand the programming concepts easily and quickly in a way that has been tested and applied by many software developers. Stack Overflow is one of the most frequently used programming Q\&A website where the questions and answers posted are presently analyzed manually, which requires a huge amount of time and resource. To save the effort, we present a topic modeling based technique to analyze the words of the original texts to discover the themes that run through them. We also propose a method to automate the process of reviewing the quality of questions on Stack Overflow dataset in order to avoid ballooning the stack overflow with insignificant questions. The proposed method also recommends the appropriate tags for the new post, which averts the creation of unnecessary tags on Stack Overflow.Comment: 11 pages, 7 figures, 3 tables Presented at Third International Symposium on Women in Computing and Informatics (WCI-2015

Cramer Rao-Type Bounds for Sparse Bayesian Learning

In this paper, we derive Hybrid, Bayesian and Marginalized Cram\'{e}r-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compressible Student-t prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We extend the MCRB to the case where the compressible vector is distributed according to a general compressible prior distribution, of which the generalized Pareto distribution is a special case. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error (MSE) in the estimates. Further, we illustrate the tightness and utility of the bounds through simulations, by comparing them with the MSE performance of two popular SBL-based estimators. It is found that the MCRB is generally the tightest among the bounds derived and that the MSE performance of the Expectation-Maximization (EM) algorithm coincides with the MCRB for the compressible vector. Through simulations, we demonstrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector for several values of the number of observations and at different signal powers.Comment: Accepted for publication in the IEEE Transactions on Signal Processing, 11 pages, 10 figure

Gender and innovation processes in rice-based systems

This GRiSP report is based on the perspectives of women and men from three rice-growing villages in the Nueva Ecija province of the Philippines

Strengthening partnerships and networks in agricultural research for development.

Partnerships have been and are a cornerstone of ILRI’s implementation framework. ILRI has a partnership strategy to guide the implementation of ILRI’s activities. This module complements this strategy in terms of preparing our collaborating partners to effectively participate and contribute to multidisciplinary, multistakeholder interventions. This module is expected to have multiple uses. One, as a source material for trainings that could be organized at different levels, and two, as reference document to upgrade the knowledge of staff of partner organizations about partnership design and management in R4D projects. The design of the learning module includes guidance notes for potential trainers including learning purpose and objectives for each session; description of the session structure (including methods, techniques, time allocation to each activity); power point presentations, presentation text, exercise handouts, worksheets, and additional reading material. There are also evaluation forms and recommended bibliography for use by future facilitators. The session modules can each be downloaded separately by search in this repository