Next-generation computational frameworks can examine the complex N interactions in crop systems to inform management, prioritize research, and increase understanding of the complexities. These computational frameworks include statistical models, process-based mechanistic simulation models, and hybrids of the two . Such decision-support tools explore all facets of N in the soil-crop interface—from gene expression, crop physiology, and phenology to soil processes and predictions of N behavior. For example, cropping systems modeling frameworks consider critical N concentration for crop growth in the context of genetic, environment, and management factors that control interactions among soil N availability, crop phenology, and crop N partitioning and yield , including BNF . Cropping systems models are integrated assemblies of individual component models that address specific biophysical components . They can be used to develop hypotheses, test hypotheses, and generate management-focused decision support tools that improve productivity, profitability,hydroponic bucket and environmental quality. Although statistical models are relatively easy-to-use and well-suited for decision support tools, unlike process-based models, they cannot extrapolate beyond the G×E×M context in which they were developed.
Hence, they cannot predict the response of NUE to unobserved combinations of G×E×M. Yet, such capacity is critical for two reasons. First, experiments alone are insufficient to address the many potential G×E×M combinations that arise from interactions between farmer decisions and weather. In a given field and year, cropping systems outcomes result from billions of potential combinations of hundreds of variables.Some of these are chosen by the farmer while others are subject to variations in weather and climate. Second, new N fertilizer and cropping systems management strategies may be addressed in silico, to increase the efficiency of field experiments and to prioritize research based on sensitivity analysis that reveals scenarios with major impact. Simultaneously, field experiments will find and fill knowledge gaps of the models.Historically, NUE in agricultural systems has shifted from high NUE in low-input, low-output systems through low NUE in high-input, high-output systems, to moderate NUE in moderate-input, high-output systems . In fact, some existing low-input, low-output systems, e.g., in Benin, exhibit NUE >1, signifying net N extraction and soil fertility decline . While many countries experience a dramatic decline in agricultural NUE as N fertilizers are adopted and overused , it has been argued that this is not inevitable and that countries experiencing a downward trend in NUE could learn from those that have been able to “bend” their NUE curve toward higher NUE , through government policy, education, careful management, etc. .
As the historical trajectory shows, simply increasing NUE alone will not be adequate if it leads to low-output systems and food insecurity amongst the growing world population. Thus, we are faced with a complex, multi-objective problem, which is further complicated by dynamic economic and environmental factors. Profitability can be relatively insensitive to N fertilizer rate. For example, in Midwest US maize, budgets based on return on investment to N fertilizer demonstrate that the economic optimum N rate varies by as much as 50 kg N ha−1 based only on realistic differences in N fertilizer: grain price ratios . Fifty kg N ha−1 is ∼30% of the mean economic optimum N rate for these systems. Hence, while there is economic incentive to optimize N fertilizer rates, the optimum N rate is highly dependent on grain and fertilizer markets. Together, these challenges demand a robust, interdisciplinary approach to increase NUE using multi-objective optimization that considers social and biophysical sciences. Multi-objective optimization is a computational framework that searches for optimal solutions and takes into account trade offs among potentially-conflicting objectives, such as minimizing N inputs while maximizing outputs. Such trade-offs are captured by cropping systems simulations, which are powerful integrators for using multi-objective optimization techniques. While the simulations could be used only to maximize the NUE ratio, instead yield and economics can be maximized and N losses minimized simultaneously. Multi-objective methods have been used to optimize parametrization of a maize system simulation to match empirical results , but could also be applied to optimize the objectives for NUE. At the regional scale, these optimization methods have been used to allocate rainfed and irrigation areas in order to maximize yield and minimize environmental impact , so similar concepts could be used to maximize NUE across regions or the globe.
Trade-offs in objectives have also been identified in crop breeding, such as between total grain yield and concentration of N in grain, but recent work with multitrait genomic selection offers a path forward . Therefore, we propose that explicit consideration of multiple objectives in optimization frameworks is crucial for future progress to increase NUE while meeting food security and economic needs. THE number of people on Earth is expected to increase from the current 6.7 billion to 9 billion by 2050. To accommodate the increased demand for food, world agricultural production needs to rise by 50% by 2030 . Because the amount of arable land is limited and what is left is being lost to urbanization, salinization, desertification, and environmental degradation, it no longer possible to simply open up more undeveloped land for cultivation to meet production needs. Another challenge is that water systems are under severe strain in many parts of the world. The fresh water available per person has decreased fourfold in the past 60 years . Of the water that is available for use, 70% is already used for agriculture . Many rivers no longer flow all the way to the sea; 50% of the world’s wetlands have disappeared, and major groundwater aquifers are being mined unsustainably, with water tables in parts of Mexico, India, China, and North Africa declining by as much as 1 m/year . Thus, increased food production must largely take place on the same land area while using less water. Compounding the challenges facing agricultural production are the predicted effects of climate change . As the sea level rises and glaciers melt, low-lying croplands will be submerged and river systems will experience shorter and more intense seasonal flows, as well as more flooding . Yields of our most important food, feed, and fiber crops decline precipitously at temperatures much .30 , so heat and drought will increasingly limit crop production . In addition to these environmental stresses, losses to pests and diseases are also expected to increase. Much of the losses caused by these abiotic and biotic stresses,stackable planters which already result in 30–60% yield reductions globally each year, occur after the plants are fully grown: a point at which most or all of the land and water required to grow a crop has been invested . For this reason, a reduction in losses to pests, pathogens, and environmental stresses is equivalent to creating more land and more water. Thus, an important goal for genetic improvement of agricultural crops is to adapt our existing food crops to increasing temperatures, decreased water availability in some places and flooding in others, rising salinity, and changing pathogen and insect threats . Such improvements will require diverse approaches that will enhance the sustainability of our farms. These include more effective land and water use policies, integrated pest management approaches, reduction in harmful inputs, and the development of a new generation of agricultural crops tolerant of diverse stresses . These strategies must be evaluated in light of their environmental, economic, and social impacts—the three pillars of sustainable agriculture . This review discusses the current and future contribution of genetically engineered crops to sustainable agricultural systems.Genetic engineering differs from conventional methods of genetic modification in two major ways: genetic engineering introduces one or a few well-characterized genes into a plant species and genetic engineering can introduce genes from any species into a plant. In contrast, most conventional methods of genetic modification used to create new varieties introduce many uncharacterized genes into the same species. Conventional modification can in some cases transfer genes between species, such as wheat and rye or barley and rye.
In 2008, the most recent year for which statistics are available, 30 genetically engineered crops were grown on almost 300 million acres in 25 countries , 15 of which were developing countries . By 2015, .120 genetically engineered crops are expected to be cultivated worldwide . Half of the increase will be crops designed for domestic markets from national technology providers in Asia and Latin America.There is broad scientific consensus that genetically engineered crops currently on the market are safe to eat. After 14 years of cultivation and a cumulative total of 2 billion acres planted, no adverse health or environmental effects have resulted from commercialization of genetically engineered crops . Both the U.S. National Research Council and the Joint Research Centre have concluded that there is a comprehensive body of knowledge that adequately addresses the food safety issue of genetically engineered crops . These and other recent reports conclude that the processes of genetic engineering and conventional breeding are no different in terms of unintended consequences to human health and the environment . This is not to say that every new variety will be as benign as the crops currently on the market. This is because each new plant variety carries a risk of unintended consequences. Whereas each new genetically engineered crop variety is assessed on a case-by case basis by three governmental agencies, conventional crops are not regulated by these agencies. Still, to date, compounds with harmful effects on humans or animals have been documented only in foods developed through conventional breeding approaches. For example, conventional breeders selected a celery variety with relatively high amounts of psoralens to deter insect predators that damage the plant. Some farm workers who harvested such celery developed a severe skin rash—an unintended consequence of this breeding strategy .In the 1960s the biologist Rachel Carson brought the detrimental environmental and human health impacts resulting from overuse or misuse of some insecticides to the attention of the wider public. Even today, thousands of pesticide poisonings are reported each year . This is one reason some of the first genetically engineered crops were designed to reduce reliance on sprays of broad-spectrum insecticides for pest control. Corn and cotton have been genetically engineered to produce proteins from the soil bacteria Bacillusthuringiensis that kill some key caterpillar and beetle pests of these crops. Bt toxins cause little or no harm to most non target organisms including beneficial insects, wildlife, and people . Bt crops produce Bt toxins in most of their tissues. These Bt toxins kill susceptible insects when they eat Bt crops. This means that Bt crops are especially useful for controlling pests that feed inside plants and that cannot be killed readily by sprays, such as the European corn borer , which bores into stems, and the pink boll worm , which bores into bolls of cotton. First commercialized in 1996, Bt crops are the second most widely planted type of transgenic crop. In 2009, Bt crops covered .50 million hectares worldwide .Most of the Bt toxins used in transgenic crops are called Cry toxins because they occur as crytalline proteins in nature . More recently, some Bt crops also produce a second type of Bt toxin called a vegetative insecticidal protein . Bt toxins in sprayable formulations were used for insect control long before Bt crops were developed and are still used extensively by organic growers and others. The long-term history of the use of Bt sprays allowed the Environmental Protection Agency and the Food and Drug Administration to consider decades of human exposure in assessing human safety before approving Bt crops for commercial use. In addition, numerous toxicity and allergenicity tests were conducted on many different kinds of naturally occurring Bt toxins. These tests and the history of spraying Bt toxins on food crops led to the conclusion that Bt corn is as safe as its conventional counterpart and therefore would not adversely affect human and animal health or the environment . Planting of Bt crops has resulted in the application of fewer pounds of chemical insecticides and thereby has provided environmental and economic benefits that are key to sustainable agricultural production. Although the benefits vary depending on the crop and pest pressure, overall, the U.S. Department of Agriculture Economic Research Service found that insecticide use in the United States was 8% lower per planted acre for adopters of Bt corn than for non-adopters . Fewer insecticide treatments, lower costs, and less insect damage led to significant profit increases when pest pressures were high .