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Project on Influences of Climatic Pattern on the food chain supply globally
The abnormalities and changes occurring in the climate very often are the main challenges impressed on the environment that needs to be taken care of. These environmental changes will affect the human beings on earth. This Data Science Project makes an attempt to analyse the changes in the food production globally that occurs due to change in climatic conditions. The main purpose of this study is to evaluate the consequences of climatic changes on primary agricultural yields. This project will evaluate all the effects related to change in temperature and rainfall pattern. The amount of carbon dioxide that impacts plant development and the uncertainties in climate change will next be considered. As a result, data representations will be the primary focus of this project. It will also assess productivity across different locations and geographical regions.
This paper is a review of a number of previous studies, carried out by the authors, of the possible effects of climate change on global agricultural yield potential, on cereal production, food prices and the implications for changes in the number of hungry people. At present, almost 800 million people in the developing world are estimated to be experiencing some form of shortage in food supply. In general, the conclusion from recent research has been that, while one may be reasonably optimistic about the prospects of adapting the agricultural production system to the early stages of global warming, the distribution of the vulnerability among the regions and people are likely to be uneven. Where crops are near their maximum temperature tolerance and where dryland, non-irrigated agriculture predominates, yields are likely to decrease with even small amounts of climate change. The livelihoods of subsistence farmers and pastoral people, who are already weakly coupled to markets, could also be negatively affected. In regions where there is a likelihood of decreased rainfall, agriculture could be substantially affected regardless of latitude .
The story of how we have arrived at this conclusion is traced below. We outline the research method, its testing and the first evaluation of effects on food supply with those climate change scenarios available in the early 1990s. This is followed by an assessment of effects under some of the most recent scenarios developed for the Intergovernmental Panel on Climate Change. Finally, we consider the effects of various mitigation strategies and different development pathways.
The first model-based studies of effects on global food supply were published in the early 1990s. The general conclusions of that work still hold today: that climate change is likely to reduce global food potential and that risk of hunger will increase in the most marginalized economies . In the study, two main components were considered.
Scenarios of climate change were developed to estimate the effect on yields and food supply. The range of scenarios used aimed to capture the range of possible effects and set limits on the associated uncertainty. The scenarios for this study were created by changing the observed data on current climate (1951–80) according to doubled carbon dioxide (CO2) simulations of three general circulation models (GCMs). The GCMs used were those from the Goddard Institute for Space Studies (GISS; Hansen et al. 1983, 1988), Geophysical Fluid Dynamics Laboratory (GFDL; Manabe & Wetherald 1987) and the United Kingdom Meteorological Office (UKMO; Wilson & Mitchell 1987).
The IBSNAT crop models were used to estimate how climate change and increasing levels of carbon dioxide may alter yields of work crops at 112 sites in 18 countries representing both major production areas and vulnerable regions at low, mid and high latitudes (Rosenzweig & Iglesias 1994). The IBSNAT models employ simplified functions to predict the growth of crops as influenced by the major factors that affect yields, e.g. genetics, climate (daily solar radiation, maximum and minimum temperatures and precipitation), soils and management practices. Models used were for wheat (Ritchie & Otter 1985; Godwin et al. 1989), maize (Jones & Kiniry 1986; Ritchie et al. 1989), paddy and upland rice (Godwin et al. 1993) and soybean (Jones et al. 1989).
The IBSNAT models were selected for this study because they have been validated over a wide range of environments (Otter-Nacke et al. 1986) and are not specific to any particular location or soil type. They are thus suitable for use in large-area studies in which crop growing conditions differ greatly. The validation of the crops models over different environments also improves the ability to estimate effects of changes in climate. Furthermore, because management practices, such as the choice of varieties, planting date, fertilizer application and irrigation may be varied in the models, they permit experiments that simulate adjustments by farmers and agricultural systems to climate change.
Most plants growing in experimental environments with increased levels of atmospheric CO2 exhibit increased rates of net photosynthesis (i.e. total photosynthesis minus respiration) and reduced stomatal opening (Acock & Allen 1985; Cure 1985). By so doing, CO2 reduces transpiration per unit leaf area while enhancing photosynthesis. Thus, it often improves water-use efficiency (the ratio of crop biomass accumulation or yield to the amount of water used in evapotranspiration). The crop models used in this study account for the beneficial physiological effects of increased atmospheric CO2 concentrations on crop growth and water use (Kimball 1983; Rogers et al. 1983; Cure & Acock 1986; Allen et al. 1987; Peart et al. 1989).
The crop growth models embody a number of simplifications. For example, weeds, diseases and insect pests are assumed to be controlled, there are no problem soil conditions (e.g. high salinity or acidity) and there are no extreme weather events such as heavy storms. The crop models simulate the current range of agricultural technologies available around the world. They do not include induced improvements in such technology, but may be used to test the effects of some potential improvements, such as varieties with higher thermal requirements and the installation of irrigation systems.
Crop modelling simulation experiments were performed at 112 sites in 18 countries for the baseline climate (1951–80) and the GCM-doubled CO2 climate change scenarios, with and without the physiological effects of CO2. This involved the following tasks.
Crop model results for wheat, rice, maize and soybean from all sites and 18 countries were aggregated by weighting regional yield ranges (based on current production) to estimate change in national yields. The regional yield estimates represent the current mix of rainfed and irrigated production, the current crop varieties, nitrogen management and soils. Since the site results relate to regions that account for about 70% of the world's grain production (FAO 1996), the conclusions concerning world production total contained in this report are believed to be adequately substantiated.
The estimates of climate-induced changes in yields were used as inputs to a dynamic model of the world food system (the BLS) in order to assess the possible impacts on the future levels of food production, food prices and the number of people at risk from hunger (Rosenzweig et al. 1993). Impacts were assessed for the year 2060, with population growth, technology trends and economic growth projected to that year. Assessments were first made assuming no climate change and subsequently with the climate change scenarios described above. The difference between the two assessments is the climate-induced effect. A further set of assessments examined the efficacy of a number of adaptations at the farm level in mitigating the impact and the effect on future production of liberalizing the world food trade system, and of different rates of growth of economy and population.
The BLS consists of linked national models. The BLS was designed at the IIASA for food policy studies, but it also can be used to evaluate the effect of climate-induced changes in yield on world food supply and agricultural prices. It consists of 20 national and/or regional models that cover around 80% of the world food trade system. The remaining 20% is covered by 14 regional models for the countries that have broadly similar attributes (e.g. African oil exporting countries, Latin American high income exporting countries, Asian low income countries). The grouping is based on country characteristics such as geographical location, income per capita and the country's position with regard to net food trade.
The BLS is a general equilibrium model system, with representation of all economic sectors, empirically estimated parameters and no unaccounted supply sources or demand sinks. In the BLS, countries are linked through trade, world market prices and financial flows. It is a recursively dynamic system: a first round of exports from all countries is calculated for an assumed set of world prices, and international market clearance is checked for each commodity. World prices are then revised using an optimizing algorithm and again transmitted to the national model. Next, these generate new domestic equilibria and adjust net exports. This process is repeated until the world markets are cleared of all commodities. At each stage of the reiteration domestic markets are in equilibrium. This process yields international prices as influenced by governmental and intergovernmental agreements.
The system is solved in annual increments, simultaneously for all countries. Summary indicators of the sensitivity of the world system used in this report include world cereal production, world cereal prices and prevalence of world population at risk from hunger (defined as the population with an income insufficient to produce or procure their food requirements).
The BLS does not incorporate any climate relationships per se. Effects of changes in climate were introduced to the model as changes in average national or regional yield per commodity as estimated above. Ten commodities are included in the model: wheat, rice, coarse grains (e.g. maize, millet, sorghum and barley), bovine and ovine meat, dairy products, other animal products, protein feeds, other food, non-food agriculture and non-agriculture. In this context, however, consideration is limited to the major grain food crops.
The results described in this paper consider the following scenario.
This involved projection of the agricultural system to the year 2060 with no effects of climate change on yields and with no major changes in political or economic context of the world food trade. It assumed:
These are projections of the world food trade system including the effects on agricultural yields under different climate scenarios (the ‘2×CO2 scenarios’ for the GISS, GFDL and UKMO GCMs). The food trade simulations for these three scenarios were started in 1990 and assumed a linear change in yields until the double CO2 concentration was reached in 2060. Simulations were made both with and without the physiological effects of 555 ppmv CO2 on crop growth and yield for the equilibrium yield estimates. In these scenarios, internal adjustments in the model occur, such as increased agricultural investment, reallocation of agricultural resources according to economic returns and reclamation of additional arable land as an adjustment to higher cereal prices, based on shifts in comparative advantage among countries and regions.
The food trade model was first run with yield changes assuming no external adaptation to climate change and was then re-run with different climate-induced changes in yield assuming a range of farm-level adaptations. These included such measures as altering planting dates and crop varieties and the use of different amounts of irrigation and fertilizer. Two adaptation levels to cope with potential effects on yield and agriculture were considered. Adaptation level 1 included those adaptations at the farm level that would not involve any major changes in agricultural practices. It thus took account of changes in planting date, amounts of irrigation and the choice of crop varieties that are currently available. Adaptation level 2 encompassed, in addition to the former, major changes in agricultural practices, such as large shifts of planting date, the availability of new cultivars, extensive expansion of irrigation and increased fertilizer application. This level of adaptation would be likely to involve policy changes both at the national and international level and significant costs. However, policy, cost and water were not studied explicitly.
A final set of scenarios assumed changes to the world tariff structure and different rates of growth of economy and population. As with previous experiments, these were conducted both with and without climate change impacts. These scenarios included:
The results show that climate change scenarios excluding the direct physiological effects of CO2 predict decreases in simulated yields in many cases, while the direct effects of increasing atmospheric CO2 mitigate the negative effects primarily in mid and high latitudes. The differences between countries in yield responses to climate change are related to differences in current growing conditions. At low latitudes crops are grown nearer the limits of temperature tolerance and global warming may subject them to higher stress. In many mid and high latitude areas, increasing temperatures may benefit crops, otherwise limited by cold temperatures and short growing seasons in the present climate.
The primary causes of decreases in yield are:
The future without climate change. Assuming no effects of climate change on crop yields but that population growth and economic growth are as stated above, world cereal production is estimated at 3286 million tonnes (mt) in 2060 compared with 1795 mt in 1990. Cereal prices are estimated at an index of 121 (1970=100). The number of people at risk of hunger is estimated at about 640 million (cf. 530 million estimated in 1990).