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Rai, M.

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Rai
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Rai, M.

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    Integration of gender considerations in climate-smart agriculture R4D in South Asia: useful research questions
    (2017) Farnworth, C.; Badstue, L.B.; Jat, M.L.; Rai, M.; Agarwal, T.
    Agriculture takes place in a social context. South Asia is a large and heterogeneous region of mixed farming systems and different cultures and social systems, which can enable or constrain opportunities and outcomes differently, for different social groups. Gender and other social relational factors have direct implications for who is able to learn about, try out, take up, and benefit from agricultural research for development (AR4D). Aimed at researchers working with climate-smart agriculture (CSA) in South Asia, this resource suggests a set of issues to consider in relation to the integration of gender in climate-smart agricultural research for development. CSA practices address production challenges through a variety of short- and long-term strategies that aim to increase resilience to extreme weather and decrease agriculture’s contribution to climate change, while meeting people’s food security needs. Conservation agriculture (CA) has received considerable attention as a cost-saving set of practices that can promote conservation of soil, energy, and labor in farming systems around the world. CA is also widely considered as having an important role to play in strategies contributing to global food security as well as improving resilience and adaptation to climate change. Built around a core set of interlocking soil and water conservation practices that help to create a closed and interdependent farming system, CA’s core practices include minimum tillage, soil cover (e.g., retention of residues), and crop diversification. However, current adoption rates in South Asia are often low and weakly sustained beyond the lifetimes of CA projects. Climate change often exacerbates the problems and inequities that poor rural women face. Their roles and livelihoods are highly dependent on the natural resources most threatened by climate change. The feminization of agriculture underscores the need to ensure that both men and women are able to learn about, try out, take up, and benefit from improved agricultural technologies, including climate-smart practices.
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    Greenhouse gas measurement from smallholder production systems: guidelines for static chamber method
    (CIMMYT, 2014) Sapkota, T.; Rai, M.; Singh, L.K.; Gathala, M.K.; Jat, M.L.; Sutaliya, J.M.; Bijarniya, D.; Jat, M.K.; Jat, R.K.; Parihar, C.M.; Kapoor, P.; Jat, H.S.; Dadarwal, R.S.; Sharma, P.C.; Sharma, D.K.
    Renewed interest in quantifying greenhouse gas emissions from soil has led to development and application of multitude of techniques. But, chamber-based flux measurement technique is most common and frequently used method for GHG flux measurement in smallholder production systems. Despite the apparent conceptual simplicity of chamber-based methods, chamber design, deployment, and data analyses can have marked effects on the quality of the flux data derived from chamber-based measurement. This also have implications on making comparisons of GHGs emissions from the studies by various researchers even within similar cropping systems and management practices. Therefore, harmonization of GHGs emission studies by chamber based method is necessary. This synthesis provides standard guidelines to scientists involved in GHG quantification by using chamber based methods as well as to facilitate inter study comparison. As any methodology or protocol, chamber methodology has also gone rigorous modification, refinement and improvement over time. Further, type of materials used, dimension, place and time of deployment, sampling time and frequency and analysis method differs slightly from location to location based on the systems being studied, resources availability and so on. Efforts have been made to summarize minimum requirement but also highlighting the need of site-specific consideration. Adoption of harmonized methods that is sensitive and unbiased will result into less error and allows accurate interpolation and extrapolation over time and space.
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