Home Computer Science Technological Entrepreneurship: Technology-Driven vs Market-Driven Innovation
Global warming is considered to be a major cause of climate change which in turn is having a dramatic adverse impact on food production in areas such as East Africa. As a consequence increasing emphasis is being given to the exploitation of new technology to enhance agricultural productivity. These activities involve moving beyond simple rain-fed farming techniques and harnessing water resources for food production through investment in technologies to store water, measure and control flows for irrigation. One approach is known as ‘smart water management’ which focuses upon exploiting new technologies to enhance the effectiveness and efficiency of crop irrigation systems (Kay 2011).
Much of the pioneering in smart water management is being undertaken in developed nations such as the USA and Israel. This technology is often quite expensive and hence at the moment usage will tend to be restricted to farms generating a high value from crop production. As much as 50 % of the water applied to crops by farmers may be lost by evaporation, wind drift and run-off, or because too much water is applied and the water sinks below the level required by plants’ roots. To overcome these problems US irrigation equipment manufacturers such as Lindsay Corporation have developed smart irrigation systems such as overhead water sprinklers to reduce water loss. In the case of this firm’s own pivot system it claims to deliver 94 % of the applied water directly to the plant roots. To optimise performance the company supplies automated weather stations or soil moisture sensors linked to a software which can analyse the exact amount of water plants need. The latest innovation has been to combine the technology with smart in-field sensors and global positioning system (GPS) to create localised irrigation maps that allow farmers to control exactly how much water is applied, when and where with an accuracy down to three metres. These new maps provide a variable irrigation prescription for every area in the field together with information on levels of nitrogen and other nutrients that enable precise water and fertiliser application. The approach exploits the opportunities provided by cloud computing and smartphones to undertake data analysis and communicate operating instructions to a farm’s irrigation system. Exploiting such systems permits reduction in fertiliser usage and increases yields whilst reducing water and labour resource utilisation (O’Driscoll 2012).
For hundreds of years, one way of improving crop yields has been the modification of the genetic make-up of plants using techniques such as selective breeding and hybridisation. This has led to the creation of ‘superhybrids’ which has permitted seed companies to offer farmers the opportunity to achieve greater productivity. More recently advances in biotechnology have resulted in the creation of genetically modified (GM) crops using a laboratory process whereby the DNA of one species are extracted and artificially introduced into the genes of an unrelated plant. The foreign DNA may come from bacteria, viruses, insects, animals or even humans. One of the technological leaders in this field is the US company Monsanto (Qaim 2005).
The range of desirable crop traits that could potentially be developed using biotechnology is very wide, ranging from biotic and abiotic stress resistances, higher yields, better nutrient efficiency and the ability to farm new plants. As a consequence GM crops have been seen as beneficial not just in developed nations, but even more importantly as a vital way of upgrading food production in poorer nations across the world. So far, however, only very few GM crop strains have been commercialised. A key obstacle is that biotechnology research and the testing and approval procedures are expensive. This means large commercial markets are required to recover the initial investment. These tend to be restricted to major crops grown on farms in developed nations. The other obstacle to the expansion of its usage has been that concerns among the general public has led to restrictions or outright bans on the growing of GM crops or their use in the production of food products in some parts of the world such as the EU. The basis of these concerns is that certain methods used to transfer the genes of modified DNA of a GM plant are imprecise and unpredictable. This possibly may lead to unintended changes such as differences in a food’s nutritional values, toxic and allergic effects, lower crop yields and unforeseen harm to the environment that cannot be reversed (Legge Jr. and Durant 2010).
These factors mean that big multinationals have little incentive to develop GM crops for small or uncertain markets in developing nations or where poverty levels mean that farmers cannot afford to purchase GM seeds. As a consequence farmers in developing nations are usually reliant upon GM plant research being undertaken in projects funded by their own governments. One such example is China where the government has funded research using rice genomic information to assist the conventional breeding process and directly applying genetic engineering technology to create new varieties. Successfully developed transgenic rice traits are insect- and disease-resistant and are aimed at overcoming the acute problems stemming from overuse of and/or heavy reliance on pesticides (Shen 2010).
Despite the appeal of technological entrepreneurship in agricultural biotechnology to reduce the world’s food supply problems, it is important to note that the performance of transgenic crops in the developing world has varied widely, across farms and farmers, crop varieties, regions and seasons. Glover (2010) opined that the high degree of variability in outcomes points to possible issues of socio-economic differentiation in farmers’ capacities to exploit the technology to their advantage. He concluded that such variations indicate the crucial degree to which beneficial outcomes depend on a diverse range of technical and institutional factors. These include the performance and local adaptation of the background variety into which new genetic traits have been introduced, as well as local agro-ecological, socio-economic, political and institutional factors (Durant 2010).
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