A group of researchers and industry writers have constructed a narrative of technological triumph for Bt cotton in India, based on an empirical record of superior performance compared to conventional seed. Counterclaims of Bt cotton failure are attributed to mutually reinforcing interactions among non-governmental organisations which avoid rigorous comparisons. However, researchers and the biotechnology industry are also engaged in a similar authentication loop for generating, validating, and publicising such facts.
This article rebuts the argument that shortcomings in Bt cotton studies and divergence between yield gains and extent of adoption of Bt hybrids make it impossible to conclusively say anything about the impact of genetically modified seeds. Further, it points out that there have been numerous studies that have controlled for selection and cultivation bias, and concluded that Bt cotton has had statistically significant positive yield effects.
ABSTRACT. In the last decades, a growing scholarship has outlined the crucial role of social networks as a source of resilience. However, with regard to the Global South, the role of social networks for the resilience of rural communities remains an under- researched and underconceptualized issue, because research remains scattered between different strands and has rarely been integrated from a resilience perspective.
Genetically engineered (GE) foods apply new molecular technologies to Widely adopted in the United States, Brazil, and Argentina for the p corn, soybeans, and cotton, they are practically banned in Europe and tigh throughout the world. We have found that GE foods have significantly incr of corn, soybean, and cotton, and lowered their prices, thus improving food foods have already contributed to a reduction in the use of pesticides and
The Commission on Sustainable Agriculture Intensification (CoSAI) and the Foreign, Commonwealth and Development Office (FCDO) jointly commissioned a gap study to determine how far away innovation investment is from helping agri-food systems achieve zero hunger goals and the Paris Agreement while reducing impacts on water resources in the Global South. The results show that the world can come much closer with some well-placed investments.
Considering the new opportunities that ICT innovations bring to improve performance of financial and extension services, this study looks at the potential contribution of financial and extension services to the Sustainable Development Goals (SDGs). The approach used extends the standard Data Envelopment Analysis (DEA) model to include longer-term management goals and find a solution that balances the efficient use of innovation investments and the achievement of policy goals, making this approach well suited for the analysis of the SDGs.
The evidence base on agri-food systems is growing exponentially. The CoSAI-commissioned study, Mining the Gaps, applied artificial intelligence to mine more than 1.2 million publications for data, creating a clearer picture of what research has been conducted on small-scale farming and post-production systems from 2000 to the present, and where evidence gaps exist.
A range of approaches and financial instruments have been used to stimulate and support innovation in agriculture and resolve interlocking constraints for uptake at scale. These include innovation platforms, results-based payments, value chain approaches, grants and prizes, incubators, participatory work with farmer networks, and many more.
A huge increase in investment in innovation for agricultural systems is critical to meet the Sustainable Development Goals and Paris Climate Agreement. Most of this increase needs to come from reorienting existing funding for innovation. However, understanding whether an investment will fully promote environmentally sustainable and equitable agri-food systems can be difficult.
The study was designed to answer the following three key questions:
(1) What types of investment instruments have been tested to support innovation in agri-food systems in the Global South, and how can these be categorized into a working typology?
(2) What is the evidence on how well different instruments have supported SAI's multiple objectives (e.g. social equality and environmental) at scale and what contextual and design factors affect their success or failure in achieving these objectives (e.g. type of value chain, who participates)?