Dec 25, 2012 - at Patan Multiple Campus for suggesting me to work on Climate ...... base resulting from geography coupled with high levels of poverty ...... Siddhartha(14) HD2092/HD1962//E4870/3/K65 ...... Bhatt, M.P., Masuzawa T., Yamamoto M., Sakai A. and Fujita K.: ..... âGlacier Lake Outburst Flood Hazard Mapping.
ECONOMIC IMPACT OF CLIMATE CHANGE ON FOOD GRAIN PRODUCTION IN NEPAL A CASE STUDY OF WHEAT PRODUCTION IN TERAI, NEPAL
A THESIS
SUBMITTED TO CENTRAL DEPARTMENT OF ECONOMICS TRIBHUVAN UNIVERSITY IN PARTIAL FULFILMENT OF THE REQUIRMENTS FOR THE DEGREE OF MASTER OF PHILOSOPHY IN ECONOMICS
SUBMITTED BY NIRANJAN DEVKOTA ROLL NO. 13/2009
DECEMBER 2012
M. Phil. Program
VIVA-VOCE SHEET
We have conducted the Viva-Voce examination of the thesis submitted by Niranjan Devkota Entitled ECONOMIC IMPACT OF CLIMATE CHANGE ON FOOD GRAIN PRODUCTION IN NEPAL A CASE STUDY OF WHEAT PRODUCTION IN TERAI, NEPAL
and found the thesis to be the original work of the student and written according to the prescribe format. We recommended the thesis to be accepted as the partial fulfillment of the requirements for Master of Philosophy in Economics
Evaluation committee Thesis Supervisor Prof. Dr. Sharad Sharma Central Department of Economics
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Internal Examiner Prof. Dr. Punya Prasad Regmi ………………………. Vice Chairman Ministry of Finance Youth & Small Entrepreneur Self Employment Fund Secretariat External Examiner Krishna Prasad Pant, Ph D Chief Market Research and Statistics Management Programme Department of Agriculture Asso. Prof. Dr. Ram Prasad Gyanwaly Acting Head, Central Department of Economics Date: 28th November, 2012
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ACKNOWLEDGMENTS
The present study “Economic Impact of Climate Change on Food Grain Production in Nepal: A Case Study of Wheat Production in Terai, Nepal” has been prepared as a partial fulfillment of the requirment for the M. Phil. Degree in Economics. I would like to express my profound gratitude to my respectable Supervisor Prof. Dr. Sharad Sharma, Professor at Central Department of Economics, Tribhuvan University for his constant encouragement, patient guaidence and painstaking supervision at every stage of my work without which the work would be in limbo. Special thanks to Internal Examiner Prof. Punya Prasad Regmi (Vice Chairman, Ministry of Finance, Youth & Small Entrepreneur Self Employment Fund Secretariat) and External Examiner Krishna Prasad Pant, PhD (Chief, Market Research and Statistics Management Programme, Department of Agriculture) who found the shortcoming and lapses in the thesis and encourage, guidance and painstaking supervised in due time. I am also highly graceful to Asst. Prof. Dr. Ram Prasad Gyanwali, Acting Head of Central Department of Economics, for his valuable suggestion during the study. Similarly, I would like to express my gratitude to all the senior Professors and the faculties of Central Department of Economics for their Co-operative and valuable support in preparing this study. To complete this work, there are several contributions from several people; I would like to express my profound gratitude all of them. Prof. Dr. Govind Nepal, Professor of Economics at Patan Multiple Campus for suggesting me to work on Climate Change and its possiible impact, Mr. Nayan Krishna Joshi, a Ph D candidate at the Tilburg University, Norway, for providing valuable suggestion with recardian approach wthich I have used as methodology in my paper and Asst. Prof. Dr. Mani Nepal, Associate Professor of Economics at Central Department of Economics, Tribhuvan University, for suggesting me to work under wheat production. Likewise, I have great timing with Dr. Baikuntha Aryal, Joint Secretary at Office of the Prime Minister and Council of Ministers. Handling software STATA and learned more about applied Econometrics was the great effort he put inside me our meet (during my research period) besides that his suggestions over my work was really constructive. It was my fortune that I am able to connect with Dr. Ram Prasad Phuyal, Ph D in Economics from Chonnam National University, South Korea and Satis Chandra Devkota, a Ph D candidate at the Department of Economics, Wayne State University, Detroit, USA, who support me by checking all the work and providing valuable suggestion during my whole research period although they are outside the country and having busy schedule with their own study. I have no words to gratitude Mr. Naveen Adhikari and Mr. Resham Thapa, Lecturer of Economics at Central Department of Economics, without whom the research would be in Limbo as well as impossible to move from one stage to another. Their continuous and stepwise suggestion, correction and guideline make possible the research meaningful and become in shape. I also have great remembrance of Mr. Baburam Karki, Mr. Niral Kumar Raut and Khagendra Katuwal faculties of Central Department of Economics for their constructive suggestion and support. I would like to thanks all the Professors, Faculties, Administrations Staff, Librarian and other staffs of Central Department of Economics for providing their great help during my research period.
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Solely, I have to gratitude all the staff of various Ministries, Departments and Bureau whose valuable suggestion, significant co-operations and helps was becomes the successive tools for me to collect the rear data. Bigyan Koirala my friends cum Officer of Government of Nepal manage the link to enter Singh Durbar for collection secondary and valuable data. Suresh Marasini, staff of Ministry of Agriculture & Cooperatives Singha Durbar, for providing valuable data set of agriculture production district wise. Tilak Raj Chaulagai my brother cum Agricultural Economist at Government of Nepal at Department of Agriculture Harihar Bhawan, Pulchok, for providing me data regarding Price of agricultural Inputs and constructive suggestion regarding this area. Besides this I like to Libraian of Central Bureau of Statistics (CBS) for his great support, the Staff of Nepal Agricultural Research Council (NARC) for detail information about wheat, soil and fertilizer and the staff of Nepal Wheat Research Centre (NWRC), Khumaltar, for detail about wheat and its genetics. The Statistics Division Head of Irrigation Department for providing me Irrigation data district wise. The staff of Department of Hydrology and Meterology, Babarmahal, for providing me the data regarding Temperature and Precipitation of different districts stations of Terai. The Market Research and Statistics Management Program, Department of Agriculture for providing data regarding Input of labor, Bullock, Tractor, Seeds, Fertilizer for per hectare land and Department of Survey, Ministry of Land Reform and Management, Min Bhawan for providing detail information about soil situation of Nepal. There are so many other information centers which become valuable during data collection like Fertilizer Promotion Center, Seeds Promotions Center and Nepal Rastra Bank. I am also graceful to all those seen and unseen authors who formed a pool of published information‟s. I like to thank all the members of Team SANDEE (The South Asian Network for Development and Environmental Economics) for providing me chance for participate the Program called „Environment and Development in South Asia‟ held on Godavari Village Resort, Kathmandu, Nepal, 6th-7th December 2010, at the beginning of my thesis work. This program not only encourages me more about learning Climate Change but also provides me golden opportunity to meet the living legends of environment Karl Goran Malder and Novel Laurate Elinor Ostrom and virtuosos of this area whose speech become the greatest energy for my research. I got chance to met Asst. Prof. Dr. K. S. Kavikumar, a famous climate change expert at South Asian Region, whose Idea normally I have reflected in the case of Nepal. I am very lucky to get scholarship fund from University Grand Commission (UGC) Nepal for my academic research work. That‟s what I would like to my sincere thanks to Mr. Subash Chandra Dhungel and the entire member of UGC for providing this support. Last but not least, I must express debt of gratitude to my family for their support and encouragement, colleague, classmate, and other whom I am unable to remember now for their valuable encouragement and suggestions during preparation of my research work. I must not leave the name of my fiancée Ranjana Koirala, who is always supporting me morally and encouraging me in every hard circumstance. Thank you all for your kind cooperation and support. I would like to share this happiest moment with you. This is your success and I am really proud of you all. Niranjan Devkota Date: 25th December 2012 (10 Poush 2069)
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TABLE OF CONTENTS
Page no: RECOMMENDATION
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ACKNOWLEDGMENTS
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TABLE OF CONTENTS
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LIST OF TABLES
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LIST OF FIGURES
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LIST OF ACRONYMS
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CHAPTER I – INTRODUCTION 1.1.Background 1.2.Statement of the Problems 1.3.Objectives of the Study 1.4.Significance of the Study 1.5.Limitation of the Study 1.6.Organization of the Study
1 4 6 6 6 7
CHAPTER II - REVIEW OF LITERATURE 2.1. Climate Change 2.1.1. Climate Change at the Global Level 2.1.2. Climate Change at the Regional Level i) Africa ii) East Asia & Pacific iii) Europe & Central Asia iv) Latin America & Caribbean v) Middle East & North Africa vi) South Asia 2.1.3. Climate Change at SAARC Countries 2.1.4. Climate Change in Nepal 2.2. Impact of Climate Change on Grain Food Production 2.2.1. Impact of Climate Change on Grain Food Production at Global Level 2.2.2. Impact of Climate Change on Grain Food Production at Regional Level i) Africa ii) Asia iii) Australia and New Zealand iv) Europe v) Latin America vi) North America iv
8 8 11 12 13 13 13 14 14 14 16 18 19 20 20 20 21 21 21 22
vii) viii)
Polar Regions (Arctic and Antarctic) Small Islands
2.2.3. Impact of Climate Change on Food Grain Production in Developed and Developing Countries 2.2.4. Impact of Climate Change on Food Grain Production in SAARC Countries i) Bangladesh ii) Bhutan iii) India iv) Nepal v) Pakistan vi) Sri Lanka 2.2.5. Impact of Climate Change on Food Grain Production in Nepal 2.3. Climate Change and Wheat Production 2.3.1. Global Level 2.3.2. Regional Level 2.3.3. SAARC Level 2.3.4. Climate Change and Wheat Production in Nepal
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22 24 25 25 25 26 27 27 28 29 30 33 37 39
CHAPTER III - RESEARCH METHODOLOGY 3.1. Theoretical Framework 3.1.1. Approaches to Measure the Effect of Climate Change on Agriculture 3.1.1.1. Agronomic-economic models 3.1.1.2 Agro-ecological zone analysis 3.1.1.3. The Ricardian cross-sectional approach 3.2. Research Design 3.2.1. Data Requirements and Sources 3.2.2. Defining variables 1. Dependent Variable 2. Explanatory Variables Climate variables Temperature Rainfall Economic variables Fertilizer Improved seeds Manure Other inputs Price of agricultural inputs 3.2.3. Expected Sign of Variables 3.2.4. Method of Analysis 3.3. Data Analysis Process
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42 43 43 43 44 47 47 48 48 48
52 52 57
CHAPTER – IV: CURRENT STATUS OF WORLD GRAIN FOOD PRODUCTION 4.1. Introduction 4.2. Cereal Production Trends 4.2.1. Regional Cereal Production Trends Sub-Saharan Africa The Middle East South Asia East and South East Europe North America/ Oceania Latin America 4.3. Climate Change Effect 4.4. Future Expectation 4.5. Poverty and Food and Nutrient Situation 4.6. How to Feed the World in 2050? 4.7. Conclusion
58 61 62 62 62 62 62 63 63 63 63 65 65 67 70
CHAPTER – V: CURRENT STATUS OF WHEAT PRODUCTION IN NEPAL 5.1. Introduction 5.2. History of Wheat Production 5.3. Disease and Pests of Wheat 5.4. Wheat Development 5.5. Current Wheat Production Situation 5.6. Conclusion
71 71 73 75 76 81
CHAPTER – VI: FUNCTIONAL RELATIONSHIPS AND THEIR IMPACTS 6.1.Effect of Climate Change in Wheat Production in Terai Nepal 82 Area and Production Temperature Precipitation Irrigation Population density Seeds Fertilizer Manure Soil Net Revenue 6.2.Econometric Analysis 86 6.2.1. Husman Fix Random Test: 87 6.2.2. Panel Regression Analysis 88 6.2.3. Testing for Cross-Sectional Dependence (contemporaneous correlation) 93 6.2.4. Serial Correlation Test 94 6.2.5. Heteroscedasticity Test 94 6.3.Forecast of Future Trends 95 6.4.Conclusion 96
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CHAPTER – VII: SUMMARY, CONCLUSIONS AND RECOMMENDATION 7.1. Summary 7.2. Conclusion 7.3. Recommendation 7.4. Research Gap
97 100 103 104
REFRENCES ANNEX
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LIST OF TABLES
Page no. Table 3.1: The Expected Sign for Wheat Production in Terai, Nepal 52 Table 4.1: World Grain Situation 2010 – 2060 58 Table 4.2: Expected Impacts of Climate Change on Global Cereal Production 64 Table 4.3: Undernourishment in the World and in the Selected Groups and Regions 66 Table 4.4: Expected Number of Undernourished (in millions), Incorporating the Effects of Climate Change 67 Table 5.1: Improved Breed Wheat Varieties Released for Terai Nepal (1960 – 2010) 72 Table 6.1: Wheat Grain Productions in the Terai Districts of Nepal (A) 84 Table 6.2: Wheat Grain Productions in the Terai Districts of Nepal (B) 85 Table 6.3: Determination of Net Revenue from Wheat 89 Table 6.4: Comparison of Wheat Production and Net Revenue from Wheat Production for Different Period with Growth Scenario 95
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LIST OF FIGURES Page no. Figure 3.1: Theoretical Framework
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Figure 4.1: World Grain Production
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Figure 4.2: World Grain Area Harvested
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Figure 4.3: World Grain Yield
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Figure 4.4: World Grain Production and Consumption
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Figure 4.5: Number of Undernourished People in the World, 1969 – 2010
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Figure 5.1: Annual Wheat Production in Nepal
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Figure 5.2: Annual Wheat Production in Terai Nepal
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Figure 5.3: Comparison National Wheat Production with Terai
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Figure 5.4: Comparison between National Wheat Cultivated Area with Terai
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Figure 5.5: Per Hectare Wheat Yield in Nepal
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Figure 5.6: Comparison of Wheat Growth Rate of Terai with Rest of the Regions 80
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LIST OF ACRONYMS
AIACC - Assessment of the Impacts of and Adaptation to Climate Change APP - Agriculture Perspective Plan CBS - Central Bureau of Statistics CERES - Comprehensive crop–soil System Simulation Models CGIAR - Consultative Group on International Agricultural Research CIMMYT - The International Maize and Wheat Improvement Center CO2 - carbon dioxide CRP - C-reactive protein DCs – Developed Countries DHM - Department of Hydrology and Meteorology ECLAC - Economic Commission for Latin America and the Caribbean FAO - Food and Agriculture Organization GCM - Gas chromatography-mass spectroscopy GCM – General Circulation Model GDP - Gross Domestic Product GHG - greenhouse gas GLOF – Glacial Lake Outburst Flood HDR - Human Development Report HLB - Helminthosporium leaf blight ICIMOD - International Centre for Integrated Mountain Development ICRIER - Indian Council for Research on International Economic Relations INGOs - International Non-governmental Organizations IPCC - Intergovernmental Panel on Climate Change IRRI - International Rice Research Institute LDCs – Least Developed Countries x
Mt - metric ton MDG - Millennium Development Goals MoAC - Ministry of Agriculture and Cooperatives NARC - Nepal Agricultural Research Council NASA - National Aeronautics and Space Administration NEC - National Environment Commission (Royal Government of Bhutan) NGOs - Non-Governmental Organization NPC - National Planning Commission NWRP - National Wheat Research Program OECD - Organization for Economic Co-operation and Development OLS – Ordinary Least Squares RWCS - Rheumatology Winter Clinical Symposium SAARC - South Asian Association for Regional Cooperation SWOPSIM - Static World Policy Simulation Model TERI - The Energy and Resources Institute UN – United Nations UNCED - United Nations Conference on Environment and Development UNEP - United Nations Environment Programme UNFCCC - United Nations Framework Convention on Climate Change USAID - United States Agency for International Development USD - United States Dollar WWF - World Wildlife Fund
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CHAPTER I INTRODUCTION 1.1 Background: Over the past two decades the debate on global climate change has moved from scientific circles to policy circles with nation-states more serious in order to explore a range of response strategies to deal with this complex phenomenon that is threatening to have significant and far reaching impacts on human society (Kavikumar, 2009). The Intergovernmental Panel on Climate Change (IPCC) in its fourth assessment reports observed that, the warming of the climate system is now unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice and rising global sea levels‟ (Solomon et.al, 2007). Available evidences suggest that human beings have exerted and continue to exert an influence on the world‟s climate. Major global surface temperature reconstructions show that Earth has warmed since 1880. Most of this warming has occurred since the 1970s, with the 20 warmest years having occurred since 1981 and with all 10 of the warmest years occurring in the past 12 years. Even though the 2000s witnessed a solar output decline resulting in an unusually deep solar minimum in 2007-2009, surface temperatures continue to increase (Allison et.al, 2009). The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost 150 to 250 cubic kilometers (36 to 60 cubic miles) of ice per year between 2002 and 2006, while Antarctica lost about 152 cubic kilometers (36 cubic miles) of ice between 2002 and 2005 (NASA, 2010). As human population has increased its level of production and consumption, greenhouse gas emissions have increased. Since 1750 at the time of the industrial revolution, CO2 has increased by 31 percent, methane by 151 percent and nitrous oxide by 17 percent (Dahal, 2006). Rising concentrations of anthropogenically produced Green House Gases (GHGs) are leading to changes in the climate. Greenhouse gases include gases like Carbon dioxide, Methane, Nitrous Oxide, Chlorofluorocarbons and Water vapor. These gases block infrared radiation escaping directly from the surface to the space resulting in warming of the atmosphere. Nepal is a landlocked country situated at the foothills of the Himalayas between 26° 22‟ N and 30° 27‟ N north latitude and 80° 04‟ E and 88° 12‟ E. It is almost rectangular in shape with an average length of about 885 Km from east to west and 1
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width of about 193 Km from south to north, encompassing an area of 147,181 Km . The altitudinal variation of Nepal extends from a mere 60 m to 8,848 m above sea level made up of Indo-Gangetic plain, hill slopes, river systems, valleys, doons and permanent snows. Mainly, the country is divided into three ecological regions namely Mountain, Hills, and Terai regions. The development regions are different in nature. Total population of Nepal in 2001 census was 23,151423 and growth rate per annum was 2.25 percent. Population density per sq. km. was 157.30 (CBS 2005). In 2011, total population of Nepal has reached 2, 66, 20, 809 and growth rate per annum was 1.4 percent. Population density per sq. km. is 180.87 (CBS, Primary Leaflets, 2011). Nepal lies near the northern limit of the tropics, due to complex topography, a wide range of climate, from the summer tropical heat and humidity of the terai to the colder dry continental and alpine winter climate through the middle and northern mountainous sections (WECS, 2011). Agriculture remains by far the most important sector in the Nepalese economy. Total area of Nepal is divided in Mountains (35 percent), Hills (42 percent) and Terai (23 percent). A total of 3,091,000 ha area is cultivated for agriculture (Malla, 2008). Agriculture in Nepal is considered most important sector because of following reasons: (i) it directly supports about 66 percent of the population in terms of employment and livelihood; (ii) it contributes about 33 percent of the country‟s gross domestic product (GDP); (iii) it generates about 70 percent of the total exports (MoAC, 2008/09). It is also the major source of food for the population and hence the prime contributing sector to food security. Due to the complex topography and climatic variation, agriculture systems are different at different locations. Different types of crops are grown in different ecological zones. The main agricultural crops are: rice, maize, wheat, millet, mustard, pulses, barley, buckwheat and potato. The cropping pattern adopted in the country is mostly traditional. Most of the crops are grown in summer and rainy season due to the required soil moisture which is received by monsoon rain. Climate change is a phenomenon due to emissions of greenhouse gases from fuel combustion, deforestation, urbanization and industrialization (Upreti, 1999) that is resulting variations in solar energy, temperature and precipitation. It is a real threat to the lives in the world that largely affects water resources, agriculture, coastal regions, 2
freshwater habitats, vegetation and forests, snow cover and melting and geological processes such as landslide, desertification and floods, and has long-term effects on food security as well as in human health. Wheat is the third important Agriculture Prospective Plan (APP) prioritized cereal crop after rice and maize in Nepal but in terms of human consumption it ranks second. In 1965/66, wheat area in the country was 100,000 ha and the production was 112,000 metric tons. As a result of the semi-dwarf wheat varieties that were introduced during the mid- 1960s and intensive research and development efforts, Nepal‟s wheat area has increased 7-fold, its production 14-fold, and its productivity 2-fold. Since 1985, annual wheat production has increased 148 percent from 0.63 million tons to 1.56 million tons, and productivity has increased 85 percent from 1.15 tons per hectare to 2.13 tons per hectare (NARC, 2007). In 2009/10, its area and production have increased to 731,000 ha and 1,556,000 metric tons respectively. The present national average wheat productivity is 2130.0 kg/ha (Economic survey, 2009/10). Wheat is cultivated in 20 percent of the total cultivated land area and contributes 18.8 percent to the total national cereal production. Per capita wheat consumption has increased from 17.4 kg in 1972 at the time of NWRP establishment to 60 kg in 2007 (NARC, 2007). In Terai, as irrigation facility is steadily increasing there is still ample opportunity to expand the wheat area where the lands remain fallow after rice harvesting.
Wheat is also an important crop of Nepal where farmers have complained over the years about increasing difficulty to plant wheat due to delayed monsoon. Several factors including, market, population growth, human induced deforestation and desertification have already threatened food security in Nepal (Dahal, 2009). APP (1995) had revealed long ago that Nepal which was once a food exporting country has become a net importer due to decline in food production. Decline in food production would lead to more malnutrition and huge consequences particularly for children (HDR, 2007/08). Climate change, now, has added to the stress by undermining the ability of people to obtain food. If the delayed monsoon has already been affecting wheat planting and subsequently its yield, it is worth examining if this has any relation with the changing climate scenario. If the results show any relation of the changing climate scenario, it must be addressed before it is too late. 3
1.2 Statement of the Problems Wheat is grown in Terai, river basins, mid-hills, and high-hills of Nepal during winter season (October to July). Wheat is not sole crop in winter because winter legumes (Lentil, Chickpea, Lathyrus, Peas), oil crops, potato and number of winter vegetables are alternative to wheat (CIMMYT, 2001). Farmers may prefer not to grow wheat in large area if they harvest bumper rice crop. A poor rice crop prompts them to grow more wheat for their food security. The average yield of wheat in Nepal is 1.6 ton/ha, which is far below the average yield average of South Asian countries (2.5 ton/ha) (Kataki, 2001). Several factors are responsible for the low productivity of wheat in Nepal. Of these, availability of irrigation water, soil nutrient status and outbreaks of insect pests and diseases are major constraints to higher productivity (Kataki, 2001). Despite the increasing trend in the use of production inputs and the adoption of new technologies, productivity of wheat is not responding in terms of yield growth as it should be. In fact, the per unit productivity growth of rice and wheat is declining, often resulting in food deficits (Kataki, 2001 and Duxbury, 2002). Nepal occupies the 16th position among 31 countries that are suffering from a food deficit.
Most of the discussion about the impact of climate change on wheat production focuses on the temperature regime of the area, which lowers the ripening period. But upland farmers grow wheat crop in unirrigated land, production of which is guided by both temperature as well as the amount of water available. Water requirement of unirrigated wheat is met by the rainfall, which means any change in rainfall time or duration or intensity affects unirrigated wheat yield. Wheat takes about 3 to 4 months from planting to harvesting (Dahal, 2009).
Observing last few decades, within three decades it is recorded that there are twelve warmest years out of which 2006 is the warmest year (Sarju et.al, 2007). Late or premonsoon, unusual precipitation, decreased rainy and intense rainfall events caused the more runoff and low ground water recharge, Terai region observed condition of extreme fog and traditional rainfall of Jestha and Asar (mid July) have been shifted in Shrawan and Bhadra in Kathmandu. Also, in Kathmandu valley winter cold shifted to a month later than regular and snowfall occurs (Feb 2007, after 60 years).
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Climatic parameters like rain and temperature strongly affect the growth and productivity of wheat. An experiment conducted in Open Top Chamber at Khumaltar shows the increase of wheat yield by 8.63 and 9.74 % even at the increase of the temperature by 6.940C and the doubling of CO2. Greenhouse effect due to doubling of CO2 was observed only by 0.180C and produced 9.74% higher than ambient plots. Increase in the CO2 level rice and wheat helped to increase the production. Wheat production was increased by 41.5 % in the Terai plain, 24.4 % in the hill and 21.2 % in the mountain under the elevated CO2. The yield however decreased by 1.8% in the Terai but continued to increase by 5.3 % in the hill and 33.3 % in the mountain at 40C rise in temperature under irrigated condition. The study conducted in India showed that, in subtropical region there will be small decrease in potential yield by 1.5-5.8% but in tropical zone the decrease will be 17-18% (Agrawal and Kalra, 1994). It indicates that rainfed wheat productivity is likely to suffer more in Terai as compared to the mid-hill‟s environment in a climate change scenario. The additional rains had favorable impacts on the wheat yield at all levels of temperature rise (Sherchand et. al., 2007). The country is susceptible to disasters, including flash flood, Glacier Lake Outburst Flood (GLOF) and melting snow in the mountains and droughts and inundation in the Terai. The rising temperature and emission of CO2 to some extent is helpful in production of major crops. For example, increase in agricultural production by enhancing photosynthetic processes, water use efficiency, shortening physiological period and soil microbial activities. Decrease in grain filling period due to increase in respiration process, fertilizer use efficiencies, shift in agricultural zone, increase in insect pest population, desertification, increase in soil erosion, evapo-transpiration and cause malnutrition in a world overflowing with food due to reducing protein and decrease in mineral nutrients content in different crops are negative effects. The impacts on agriculture are the decrease of productive land in some region and increase in other region. So, it is a complex problem to the world (Pathak et. al., 2003). Rising CO2 promotes plant growth and if the CO2 gas doubles, yields of wheat will increase by 40%. Climate change therefore has significant impact in wheat production. In the countries like Nepal where most of the farming system depends on monsoon and corresponding climate, its effect is more severe. Therefore, this study attempts to measure the effect of climate change in wheat production in Nepal. In addition, it will 5
measure the effect of change in wheat production due to climate change on household welfare.
1.3 Objectives of the Study: The general objective of this study is to analyze the effect of long term climate change on wheat production in Terai Nepal. The specific objectives are: 1.
To measure the effect of climate change in wheat production in Terai Nepal.
2.
Forecast the wheat production and net revenue generated from wheat production for next 10 years.
1.4 Significance of the Study: Wheat is one of the main crops in terms of area and production and most traded food commodity together with rice and maize (Porter et.al. 2004). The present study integrates the effects of the ecological system, indirect effects of climate on crop growth as well as management in the analysis. That is, the climate is seen as one input factor in the overall production frame for agriculture. Climate therefore has a complex impact on agriculture, where the volume distribution as well as the temporal distribution of meteorological outcomes matter. The broad pattern of a local climate is shaped by the total volume of radiation, precipitation and temperature over a certain period, typically a year. There has been extensive research on the impacts of climate change, but little on the economic impacts on agriculture in Nepal. To fill this gap, this study carries out an analysis of the potential impacts of climate change on Nepal‟s agricultural sector focusing on wheat production in Terai Nepal. This study will be highly useful to the related institutions and individuals.
1.5 Limitation of the Study: The Impact of climate change is very vast and broad because it is very difficult to grasp the direct and indirect effect in one empirical analysis. So it has many limitations. The major limitations of this study are given below:
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1) Efforts have made to obtain reliable and accurate data and information from the various organizations. It‟s quite obvious that usual constraints are applicable in this study too. 2) All these indirect factors from climate to wheat production are not fully or jointly describable in as per the result from laboratory tests of soil and quality of water as being irrigated. Therefore, the study may have shortcomings. 3) It is only based on secondary data which is available from different sources and the available data are not verified with any another data sources. 4) The study covers only Terai districts and single production (i.e. wheat) for 18 years time period ranging from 1992/93 to 2009/10 and it may have influence by other unseen and beyond explanatory variables. 5) The simulation exercise is a research gap for the future researchers and expert to explore in this area.
1.6 Organization of the Study This study is presented into eight chapters. The first chapter introduces the subject matter of the study. It describes the problems, objectives, significance and limitations of the study. The second chapter presents the review of the literature on the wheat production especially of Terai belt of Nepal including brief presentation of theoretical foundation on climate change and its economic impact on wheat production and its impact on net revenue of the people depends on this profession. The third chapter is related to the research methodology. It provides conceptual framework and defines the methodology to attain the objectives, and the nature and types of variables including hypothesis tested in the study. The fourth chapter provides detail information regarding current situation of world food grain production. The fifth chapter is separated for current status of wheat production in Nepal. This chapter provides information of wheat along with its development and entomology. The sixth chapter provides different results that have been tested by using different statistical tools and its interpretations. Last but not least, chapter seven provides the major findings and its strengths and weaknesses in Nepalese agricultural field along with summary, recommendation and conclusion.
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CHAPTER II REVIEW OF LITERATURE
Since past decade the most discussed issue is the climate change. There are several research papers, surveys, articles and books on climate change and its impact on agriculture at international, regional and even local levels. Similarly, different conclusions of researchers on effect of climate change on agricultural productivity on different locations have been observed. This study has the focus on economic impact of climate change on food grain production. Therefore, the literature related to climate change is discussed in detail on the first section. The second section deals with the impact of climate change on food grains production and third section mostly deals with impact of climate change on wheat production. 2.1. Climate Change Climate refers to the average weather and represents the state of the climate system over a given time period. Climate changes over time may be due to natural variability or as a result of human induced increases of greenhouse gases in the atmospheres and is reflected in the variation of the mean state of weather variables including temperature, precipitation and wind (Orindi and Eriksen, 2005). Climate change is a long-term change in the statistical distribution of weather patterns over periods of time that range from decades to millions of years. It may be a change in the average weather conditions or a change in the distribution of weather events with respect to an average, for example, greater or fewer extreme weather events. Climate change may be limited to a specific region, or may occur across the whole Earth. 2.1.1. Climate Change at the Global Level In the fourth assessment report of Inter Governmental Panel on Climate Change (IPCC, 2007), it is intended to access scientific, technical and socio-economical information concerning climate change as well as its potential effects and options for adaptation and mitigation. This assessment report is released with four distinct sections entitled (i) working group I report: the physical science basis (ii) working group II report: impact adaptation and vulnerability (iii) working group III report: 8
mitigation and climate change and (iv) the synthesis report: summary for policymakers. The major findings of IPCC can be listed under four categories: Working Group I Report: the physical science basis assesses the current scientific knowledge of the natural and human drivers of climate change, observed changes in climate, the ability of science to attribute changes to different causes, and projections for future climate change.
The primary source of the increase in methane is very likely to be a combination of human agricultural activities and fossil fuel use. How much each contributes is not well determined.
Nitrous oxide concentrations have risen from a pre-industrial value of 270 ppb to a 2005 value of 319 ppb. More than a third of this rise is due to human activity, primarily agriculture.
Warming in the last 100 years has caused about a 0.74 °C increase in global average temperature. This is up from the 0.6 °C increase in the 100 years prior to the Third Assessment Report.
Sea level rose at an average rate of about 1.8 mm/year during the years 19612003. The rise in sea level during 1993-2003 was at an average rate of 3.1 mm/year. It is not clear whether this is a long-term trend or just variability.
Working Group II Report: impact adaptation and vulnerability states that evidence from all continents and most oceans shows that many natural systems are being affected by regional climate changes, particularly temperature increases.
With a high confidence (about an 8 in 10 chance to be correct) WGII asserts that climate change has resulted in more and larger glacial lakes, increasing ground instability in permafrost regions, increasing rock avalanches in mountain regions, changes in some Arctic and Antarctic ecosystems, Increased run-off and earlier spring peak discharge in many glacier and snow-fed rivers and so on.
Dry regions are projected to get drier, and wet regions are projected to get wetter.
9
It is projected with medium confidence (about 5 in 10 chances to be correct) that globally; potential food production will increase for temperature rises of 13 °C, but decrease for higher temperature ranges.
Many millions more people are projected to be flooded every year due to sealevel rise by the 2080s.
Working Group III Report: mitigation of climate change analyses mitigation options for the main sectors in the near-term, addressing also cross-sectorial matters such as synergies, co-benefits, and trade-offs. It also provides information on long-term mitigation strategies for various stabilization levels, paying special attention to implications of different short-term strategies for achieving long-term goals. Stabilization of greenhouse gas concentrations is possible at a reasonable cost, with stabilization between 445ppm and 535ppm costing less than 3% of global GDP. The Synthesis Report: summary for policymakers states that anthropogenic warming could lead to some impacts that are abrupt or irreversible, depending upon the rate and magnitude of the climate change.
There is medium confidence that approximately 20-30% of species assessed so far are likely to be at increased risk of extinction if increases in global average warming exceed 1.5-2.5°C (relative to 1980-1999). As global average temperature increase exceeds about 3.5°C, model projections suggest significant extinctions (40-70% of species assessed) around the globe.
Partial loss of ice sheets on polar land could imply metres of sea level rise, major changes in coastlines and inundation of low-lying areas, with greatest effects in river deltas and low-lying islands. Such changes are projected to occur over millennial time scales, but more rapid sea level rise on century time scales cannot be excluded.
Stern (2006) in his review report “The Economics of Climate Change” attempt to access the effect of global warming on the world economy. He emphasizes climate change is serious issue because human activity is causing global warming with the main source of greenhouse gases. He added climate change involves as externality i.e. the emission of greenhouse gases damages other at no cost to the agent responsible for 10
the emissions. He considers that economics of climate change has been focused on modeling, the implications of growth for emissions, examine the modeling, the economics of technological options, calculating social cost of carbon and exploring tax, market and other structure. His book can be divided into two parts: first half examine the evidence on the economic impact of climate change itself, and explores the economics of establishing greenhouse gases in the atmosphere. The second half of the review considers the complex policy challenge involved in managing the transition to a low carbon economy and in ensuring the societies can adapt to the consequences of climate change that can no longer be avoided. His interesting findings are that there do remain uncertainties about the nature of scale and long-term impacts and some of the risks are more serious than had first appealed. Climate change threatens the basic elements of life for people around the world and the poorest countries and people will suffer earliest and most. And if and when the damages appear it will be too late to reverse the process. He highlights that climate change initially have small positive effects for a few developed countries, but it is likely to be very damaging for the much higher temperature increases expected by mid-to-late century. In his word “Adaptation policy is crucial for dealing with the unavoidable impacts of climate change, but it has been under-emphasized in many countries”. Stern in his report told that there is still time to avoid the worst impacts of climate change, if we take strong action now. If we ignore this now, Climate change could have very serious impacts on growth and development for future generation. He recommended that the costs of stabilizing the climate are significant but manageable; delay would be dangerous and much more costly. Action on climate change is required across all countries, and it need not cap the aspirations for growth of rich or poor countries and a range of options exists to cut emissions; strong deliberate policy action is required to motivate their take-up. So in conclusion he added climate change demands an international response, based on a shared understanding of long-term goals and agreement on frameworks for action. 2.1.2. Climate Change at the Regional Level ECLAC (2009) on its research document incorporate the socio-economic institutional and geographical features of Latin America and the Caribbean make climate change a 11
pressuring issue. The document presented an aggregate economic analysis of climate change in Latin and the Caribbean based on the national studies carried out on the topics during a relatively short timeframe. The major findings of ECLAC state that the average temperature will continue to rise gradually but persistently and at different rate across the region and there will be change in the volume, intensity and frequency pattern of precipitation. The average temperature in South America is projected to rise steadily by between 10C and 40C under the lower GHG concentrations and by between 20C and 60C under the lower GHG concentrations, with rainfall rising in some case by 5% - 10% and falling others by 20% - 40%. Furthermore, part of the glaciers in the Andean countries are expected to melt and extreme weather events in the Caribbean, Central America and the tropical and sub-tropical parts of South America will probably increase. In the conclusion part ECLAC suggest that it is essential to design a regional strategy aimed at reducing the severest impacts and preventing those that are unacceptable, such as irreversible loss of biodiversity, human lives and livelihoods. The region has significant options for mitigations. Some of them are already deployed, but at the aggregate level the cost of mitigation are significant. WDR (2010) states in its official report “Development and Climate Change” that all developing regions are vulnerable to the impacts of climate change for different reasons. The problems common to developing countries today are limited human and financial resources; weak institutions which are consider critical drivers of their vulnerability. But other factors, attributable to their geography and history, are also significant. WDR highlights the regional impact due to climate change along with the possible outcomes. vii)
Africa
Sub- Saharan Africa suffers from natural fragility (two- thirds of its surface area is desert or dry land) and high exposure to droughts and foods, which are forecast to increase with further climate change. The region's economies are highly dependent on natural resources. Biomass provides 80 percent of the domestic primary energy supply. Rainfed agriculture contributes some 30 percent of GDP and employs about 70 percent of the population. Rainfed agriculture contributes some 30 percent of GDP 12
and employs about 70 percent of the population. Inadequate infrastructure could hamper adaptation efforts, with limited water storage despite abundant resources. Malaria, already the biggest killer in the region, is spreading to higher, previously safe, altitudes (WRD, 2010). viii)
East Asia & Pacific
In East Asia and the Pacific one major driver of vulnerability is the large number of people living along the coast and on low-lying islands of what over 130 million people in China, and roughly 40 million, or more than half the entire population, in Vietnam. A second driver is the continued reliance, particularly among the poorer countries, on agriculture. As pressures on land, water, and forest resources increase as a result of population growth, urbanization, and environmental degradation caused by rapid industrialization greater variability and extremes will complicate their management. In the Mekong River basin, for example, the rainy season will see more intense precipitation, while the dry season lengthens by two months. A third driver is that the region‟s economies are highly dependent on marine resources and the value of well-managed coral reefs is $13 billion in Southeast Asia alone which are already stressed by industrial pollution, coastal development, overfishing, and runoff of agricultural pesticides and nutrients (WRD, 2010). ix)
Europe & Central Asia
Vulnerability to climate change in Eastern Europe and Central Asia is driven by a lingering Soviet legacy of environmental mismanagement and the poor state of much of the region‟s infrastructure. An example: rising temperatures and reduced precipitation in Central Asia will exacerbate the environmental catastrophe of the disappearing Southern Aral Sea (caused by the diversion of water to grow cotton in a desert climate) while sand and salt from the dried-up seabed are blowing onto Central Asia‟s glaciers, accelerating the melting caused by higher temperature. Poorly constructed, badly maintained, and aging infrastructure and housing which is a legacy of both the Soviet era and the transition years are ill suited to withstand storms, heat waves, or foods (WRD, 2010).
13
x)
Latin America & Caribbean
Latin America and the Caribbean's most critical ecosystems are under threat. First, the tropical glaciers of the Andes are expected to disappear, changing the timing and intensity of water available to several countries, resulting in water stress for at least 77 million people as early as 2020 and threatening hydropower, the source of more than half the electricity in many South American countries. Second, warming and acidifying oceans will result in more frequent bleaching and possible diebacks of coral reefs in the Caribbean, which host nurseries for an estimated 65 percent of all fish species in the basin, provide a natural protection against storm surge, and are a critical tourism asset. Third, damage to the Gulf of Mexico‟s wetlands will make the coast more vulnerable to more intense and more frequent hurricanes. Fourth, the most disastrous impact could be a dramatic dieback of the Amazon rain forest and a conversion of large areas to savannah, with severe consequences for the region‟s climate and possibly the world's (WRD, 2010). xi)
Middle East & North Africa
Water is the major vulnerability in the Middle East and North Africa, the world's driest region, where per capita water availability is predicted to halve by 2050 even without the effects of climate change. The region has few attractive options for increasing water storage, since close to 90 percent of its freshwater resources are already stored in reservoirs. The increased water scarcity combined with greater variability will threaten agriculture, which accounts for some 85 percent of the region's water use. Vulnerability is compounded by a heavy concentration of population and economic activity in food- prone coastal zones and by social and political tensions that resource scarcity could heighten (WRD, 2010). xii)
South Asia
South Asia suffers from an already stressed and largely degraded natural resource base resulting from geography coupled with high levels of poverty and population density. Water resources are likely to be affected by climate change, through its effect on the monsoon, which provides 70 percent of annual precipitation in a four- month period, and on the melting of Himalayan glaciers. Rising sea levels are a dire concern in the region, which has long and densely populated coastlines, agricultural plains threatened by saltwater intrusion, and many low-lying islands. In more severe 14
climate- change scenarios, rising seas would submerge much of the Maldives and inundate 18 percent of Bangladesh's land (WRD, 2010). 2.1.3. Climate Change at SAARC Countries South-Asia region is broadly defined by the IPCC as consisting of Pakistan, India, Nepal, Sri Lanka, Bhutan, Bangladesh including non SAARC member nation Myanmar, and the Tibetan Plateau. However, the whole region has large climate variability, making climate change harder to identify and meaning that the current level of understanding of how the climate is influenced by human activity is low. Despite this, climate anomalies and changes in extreme events have been observed throughout the region, with intense rains, floods and droughts reported (Practical Action, 2010). Of particular note is severe and recurrent flooding in Nepal. More gradual year-onyear changes in temperature have also been observed, with a 0.09ºC per year increase in recorded in the Himalayas and 0.04ºC per year increase in the Terai (with higher increases in winter). These regional climate extremes and climate changes have produced multiple impacts across the South Asian region and in Nepal in particular. The predicted seasonal changes for the South Asian region are summarized as:
Many parts of the region have suffered a reduction in food production due to reduced water availability, increases in temperature and a reduction in rain fall days.
Water shortages and poor water quality have been attributed to the effects of rapid urbanization and industrialization, aggravated by climate change, in India, Pakistan, Nepal and Bangladesh.
The incidence of diarrheal diseases and other infectious diseases such as cholera, hepatitis, malaria and dengue fever is expected to increase due to severe floods, rainfall and droughts in combination with poverty, poor access to safe water and poor sanitation. High temperatures and poor hygiene contribute to bacterial proliferation.
The changes have been calculated relative to the average temperature and precipitation in the period 1961- 1990 from which they notify that the results of climate projections for high and low future global greenhouse gas emissions are 15
presented – demonstrating the enormous difference in the impacts that result from alternative future levels of greenhouse gas emissions, particularly by the end of the century. The impact that the highest emitting countries considered the most developed countries in the West. But South Asian nation would not be apart from its negative impact. The „high emissions‟ assume rapid, fossil fuel-intensive economic growth over the coming century: very much a business as usual in the global economy. The „low emissions‟ on the other hand, assume reductions in the use of natural resources and the introduction of clean and resource efficient technologies during the course of this century. The implication is clear – large cuts in carbon emissions and radical changes in global patterns of consumption, particularly in the West, will be required to prevent climate change from bringing catastrophic changes across South Asia. 2.1.4. Climate Change in Nepal Some of the environmental challenges facing Nepal in the context of global warming are changes in hydrological cycles and the depletion of water resources. It is estimated that a temperature rise of 40C can result in the loss of 70% of snow and glacier area due to melting of snow and ice. This melt water will contribute to the faster development of glacier lakes, and this will lead to increased potential for Glacier Lake outburst flood hazards (Kafley, 2004). Practical Action (2010), in its report “Promoting Adaptation to Climate Change in Nepal”
points out that climate change is likely to bring particularly rapid
temperature increases in Nepal – faster than the average global rate of warming. By using regional climate change model to predict vast majority of climate change relevant to Nepal conclude that warming across Asia will accelerate. There is now higher confidence in climate projections, including regional-scale warming, wind patterns, precipitation and some aspects of extreme events. Country-scale trends and projections, however, remain difficult to discern. In their field study based on Jugedi watershed region in Chitwan District, Nepal they have revealed the range of problems that the changing climate has brought in Nepal. The weather has been observed to have become be hotter in the summer months, yet colder in the winter, whilst the number and quality of water resources have fallen. Monsoon rainfall has increased whilst winter rainfall has become scarcer and periods of drought have become longer. Higher levels of sediment have altered the course of 16
rivers, liver disease has been observed in cattle and cereal crop production has fallen. The effect on livelihoods has been seen through an increase in alcohol production to offset the failure of agriculture, whilst paddies have been converted to maize, millet and gram fields as the agricultural conditions change. The major finding of their report was winter temperatures will increase more than summer temperatures. The level of winter rainfall is expected to decrease, whilst summer rainfall will increase. Extreme weather events such as heat waves and very high rainfall are likely to become more frequent. Overall, Nepal is likely to become wetter, the east of Nepal experiencing more rain than the west. They stress, some level of uncertainty is inevitable in measuring and anticipating climate change. Attributing individual current events to climate change is impossible due to inherent climate variability, whilst a lack of observations over a sufficiently long time frame or narrow geographical area can hamper the analysis of climate trends. However, they found the degree of certainty over all aspects of climate change has increased in recent years and in particular between the publication of the IPCC‟s reports in 2001 and 2007.
As recommendation they highlight, policy makers from all sectors urgently need to focus attention on the implications of climate change. Many aspects of climate change and variability are already having a profound effect on the livelihoods of poor rural communities, and enough is known about the future impacts of climate change for action to be taken now. The vulnerability of the poorest to climate change is a central challenge and sustainable agricultural systems, must be a priority. All government departments must acknowledge the importance of climate change and analyse the impacts for their sector. Disaster planning and risk reduction strategies must account for the new challenges of climate induced disasters. Sarju et al. (2007), in their report “Climate Profile and Observed Climate Change and Climate Variability in Nepal” observed that being one of the least contributors of GHG in the atmosphere, the potential impacts of the climate change in Nepal is large. Whose rate of increase in temperature (0.04°C/year) is higher that the mean global rate. They use De Martonne method to identify aridity period over the year in Nepal. The aridity index (A) of a place is defined by the following formula: 17
[ (
)
(
]
)
in which P is annual precipitation, T is the mean annual
temperature, p is the precipitation of the driest month and t is the temperature of the desired month. By using De Martonne method they found most of the eastern, central and western parts of the country are humid to veryhumid. Mid and far-western regions have mainly subhumid climate. The leeward side of the Annapurna range in the north of western and mid-western regions is largely semiarid to dry. The western extremity of the country bordering India have humid climate due to the influence of winter rain in these regions. The southern plains (Terai) are also mainly subhumid. These regions therefore have water excess throughout the year. The trend of monsoon rainfall is slightly positive, while the numbers of rainy days are in slight decreasing trend. The heavy rainfall events (≥ 100 mm/day) are observed to be in increasing trend. They conclude it by saying climate change is the most burning issue because warming is evident in all the seasons, and the winter is warming faster than rest of the seasons. Similarly, they observed that the days and nights both are becoming warmer and the frequency of cool days and nights are becoming less which implies that floods and landslides will be more common in future. Communities of different parts of Nepal have already begun experiencing unusual changes in weather patterns. Some of them are happy with these changes; for example, farmers of Mustang and Manang districts have noticed improved apple sizes in recent years. But others face hardship; for example, water leakage into traditional houses has increased, which people feel is due to new precipitation patterns (Dahal, 2006). Although, Nepal along with over 150 other countries, signed the United Nations Framework Convention on Climate Change (UNFCCC) at the United Nations Conference on Environment and Development (UNCED) in Rio de Janeiro in June 1992. Nepal ratified the convention on 2nd May in 1994, and this convention came into force on 31st July in 1994. However, there is difficult to say progress on Climate Change adaptation and mitigation process and program which is big challenge.
2.2. Impact of Climate Change on Grain Food Production Climate change and agriculture are interrelated processes, both of which take place on a global scale (IPCC, 2007). Agriculture currently accounts for 24% of world output, 18
and uses 40% of land area (FAO, 2003). As we already mention in our previous chapter global warming is projected to have significant impacts on conditions affecting agriculture, including temperature, carbon dioxide, glacial run-off, precipitation and the interaction of these elements which determine the carrying capacity of the biosphere to produce enough food for the human population and domesticated animals. Therefore, the overall effect of climate change on agriculture will depend on the balance of these effects (Fraser, 2008). Climate change may increase the amount of arable land in high-latitude region by reduction of the amount of frozen lands. Sea levels are expected to get up to one meter higher by 2100, though this projection is disputed. A rise in the sea level would result in an agricultural land loss, in particular in areas such as South East Asia. Erosion, submergence of shorelines, salinity of the water table due to the increased sea levels, could mainly affect agriculture through inundation of low-lying lands. Low lying areas such as Bangladesh, India and Vietnam will experience major loss of rice crop if sea levels are expected to rise by the end of the century (IRRI, 2007). In its report FAO shows Cereal crops – rice, wheat and maize make up 85% of world cereal exports, and are thought to be particularly sensitive to climate change (FAO, 2003). Therefore, in their report WDR outlines agriculture faces new challenges from natural resource degradation and climate change. It warns that though there is great investment in agriculture in developing countries, Millennium Development Goal (MDG) of halving extreme poverty and hunger by 2015 will not be realized unless the sector is placed at the central of the development agenda (WDR, 2008).
Schulze et al. (1993), in a study of South Africa, Lesotho and Swaziland find climate change to be associated with potential increases in maize production, though they argue that it is likely to have little effect in marginal areas where yields are already low. Sivakumar (1992), in a study for Niger argues that climate has significant implications for agriculture because farmers tend to change their farming patterns with climate change and this is likely to have adverse environmental consequences. Onyeji and Fischer (1994), in a study for Egypt find that adverse climate change will lead to a decline in agricultural production and in GDP. However, they argue that large instruments in adaptation are required to make significant gains in avoiding the adverse impacts of climate change on the economy. Yates and Strzepek (1998) argue that global warming is likely to have adverse consequences for the Egyptian 19
economy. Benson and Clay (1998) in a study involving a number of African countries argue that developing countries in Africa may be less prone to climate change shocks than industrial countries.
2.2.1. Impact of Climate Change on Grain Food Production at Global Level Kurukulasurya et al. (2003), find out from their analysis that there are four ways in which climate affect agriculture. They are: (1) Changes in temperature and precipitation directly affect crop production and can even alter the distribution of agro-ecological zones (2) Increased CO2 is expected to have a positive effect on agricultural production due to greater water use efficiency and higher rates of plant photosynthesis (3) Runoff or water availability is critical in determining the impact of climate change on crop production, especially in Africa and (4) Agricultural losses can result from climate variability and the increased frequency of changes in temperatures and precipitation (including droughts and floods). Between 1996 and 2003, grain production has stabilized slightly over 1800 millions of tons. In 2000, 2001, 2002 and 2003, grain stocks have been dropping, resulting in a global grain harvest that was short of consumption by 93 millions of tons in 2003. Global cereal production could continue to increase up to 3.7bn - 4.8bn tones by 2080 without climate change. When it is factored in, global cereal production could be within 2% of reference scenarios, but with potentially large regional variations (Fischer et al, 2005). But Schneider et. al. (2007), accessed the literature on key vulnerabilities to climate change. They conclude that for about a 1 to 30C global mean temperature increased by 2100 (relative to the 1990 – 2000) there would be productivity decreases for some cereals in low latitude and productivity increases in high latitudes. For example, average crops yields is expected to drop down to 50% in Pakistan whereas crop Production in Europe is expected to grow up to 25% in optimum hydrologic conditions (UK Meteorological Office). 2.2.2. Impact of Climate Change on Grain Food Production at Regional Level Agriculture consumes 85 percent of the world‟s utilized water and the sector contributes to deforestation, land degradation, and pollution. The report says in agriculture-based countries home to 417 million rural people, 170 million of whom live on less than $1 a day the agricultural sector is essential to overall growth, poverty 20
reduction, and food security. Most of these countries are in Sub-Saharan Africa, where the sector employs 65 percent of the labor force and generates 32 percent of GDP growth (WDR, 2008). The following regions highlight the situation of agriculture production on their respective area. ix)
Africa
Africa's geography makes it particularly vulnerable to climate change, and seventy per cent of the populations rely on rain-fed agriculture for their livelihoods. With high confidence, IPCC concluded that climate variability and change would severely compromise agricultural production and access to food (WDR, 2008). x)
Asia
With medium confidence, IPCC projected that by the mid-21st century, in East and Southeast Asia, crop yields could increase up to 20%, while in Central and South Asia, yields could decrease by up to 30%. Taken together, the risk of hunger was projected to remain very high in several developing countries. More detailed analysis of rice yields by the International Rice Research Institute forecast 20% reduction in yields over the region per degree centigrade of temperature rise. Rice becomes sterile if exposed to temperatures above 35 degrees for more than one hour during flowering and consequently produces no grain (WDR, 2008). xi)
Australia and New Zealand
Hennessy et al. (2007) assessed the literature for this region. They concluded that without further adaptation to climate change, projected impacts would likely be substantial: By 2030, production from agriculture and forestry was projected to decline over much of southern and eastern Australia, and over parts of eastern New Zealand; In New Zealand, initial benefits were projected close to major rivers and in western and southern areas. Hennessy et al. placed high confidence in these projections (WDR, 2008). xii)
Europe
With high confidence, IPCC (2007) projected that in Southern Europe, climate change would reduce crop productivity. In Central and Eastern Europe, forest productivity
21
was expected to decline. In Northern Europe, the initial effect of climate change was projected to increase crop yields (WDR, 2008). xiii)
Latin America
With high confidence, IPCC (2007) projected that in drier areas of Latin America, productivity of some important crops would decrease and livestock productivity decline, with adverse consequences for food security. In temperate zones, soybean yields were projected to increase (WDR, 2008). xiv)
North America
According to a paper by Deschenes and Greenstone (2007), predicted increases in temperature and precipitation will have virtually no effect on the most important crops in the US. With high confidence, IPCC (2007) projected that over the first few decades of this century, moderate climate change would increase aggregate yields of rain-fed agriculture by 5–20%, but with important variability among regions. Major challenges were projected for crops that are near the warm end of their suitable range or which depend on highly utilized water resources (WDR, 2008). xv)
Polar Regions (Arctic and Antarctic)
For the Guardian newspaper, Brown (2005) reported on how climate change had affected agriculture in Iceland. Rising temperatures had made the widespread sowing of barley possible, which had been untenable twenty years ago. Some of the warming was due to a local (possibly temporary) effect via ocean currents from the Caribbean, which had also affected fish stocks. Anisimov et al. (2007) assessed the literature for this region. With medium confidence, they concluded that the benefits of a less severe climate were dependent on local conditions. One of these benefits was judged to be increased agricultural and forestry opportunities (WDR, 2008). xvi)
Small Islands
In a literature assessment, Mimura et al. (2007) concluded, with high confidence, that subsistence and commercial agriculture would very likely be adversely affected by climate change (WDR, 2008). 22
2.2.3. Impact of Climate Change on Food Grain Production in Developed and Developing Countries IPCC (2001), in its report said poorest countries would be hardest hit, with reductions in crop yields in most tropical and sub-tropical regions due to decreased water availability, and new or changed insect pest incidence. In Africa and Latin America many rain fed crops are near their maximum temperature tolerance, so that yields are likely to fall sharply for even small climate changes; falls in agricultural productivity of up to 30% over the 21st century are projected. Marine life and the fishing industry will also be severely affected in some places. Likewise, in long term climate change also could affect agriculture in several ways:
Productivity, in terms of quantity and quality of crops.
Agricultural practices, through changes of water use (irrigation) and agricultural inputs such as herbicides, insecticides and fertilizers.
Environmental effects, in particular in relation of frequency and intensity of soil drainage (leading to nitrogen leaching), soil erosion, reduction of crop diversity.
Rural space, through the loss and gain of cultivated lands, land speculation, land renunciation, and hydraulic amenities.
Adaptation, organisms may become more or less competitive, as well as humans may develop urgency to develop more competitive organisms, such as flood resistant or salt resistant varieties of rice.
Crosson (1997), in his report “Impact of Climate Change on Agriculture” says that the IPCC report estimates climate change impacts on grain production at the global level and then zeros in on the estimated effect on the developed countries (DCs) of North America and Europe as well as on the less developed countries (LDCs) of Asia, Africa, and Latin America. (Grain is often used as a proxy for all food because it accounts for over half of all food calories consumed in the world). He found two sharp differences (i.e. physical and eco-structural) of climate change on grain production in developed and developing countries. In physical notation, he argues the GCMs estimate that the high latitudes will warm more than the tropics. Most of the DCs are in the northern latitudes, and their agriculture would benefit from 23
the longer growing seasons that a warmer climate would bring. Most LDCs, on the other hand, include much terrain in the tropics where the negative effects of a warmer climate would not be offset by other favorable trends. In eco-structure factor he add that compared with the LDCs, the DCs have much greater economic resources that can be devoted to helping farmers adjust to climate change. In addition, the institutional structures of the DCs appear to be more efficient than those in the LDCs in mobilizing the resources needed to pursue specific social objectives, whether they are adjustments to climate change or anything else.
If the GCMs are right in predicting generally beneficial climate change in the northern latitudes, then the physical factor accounting for the difference in impacts on the DCs and the LDCs would seem to be pretty much fixed. But the effect of the eco-structural factor may be more malleable. In east and south-east Asia, and to a lesser extent in south Asia, agricultural performance over the last 10 to 15 years has been impressive. Farmers have adopted new, more productive technologies as they have become available and production, both per person and per hectare, has increased. This strong agricultural performance has been part of a generally impressive economic performance in the countries of those regions.
It is not clear why some Asian countries have been so much more successful than countries in Latin America, and especially in Africa. Their success does suggest, however, that the eco-structural weaknesses so common now among the LDCs are not fixed for all time. The Asian experience offers some promise that, given time and incentive to improve their material standard, farmers in other LDCs can and will seize the opportunities presented. This prospect provides some reason to hope that by the time that climate change begins to impinge negatively on LDCs, they will have developed a capacity to adjust to it well beyond what they could accomplish under present conditions. If so, the differences between the DCs and LDCs in terms of the effects of climate change on grain production could be much less than the 1996 IPCC report suggests. 2.2.4. Impact of Climate Change on Food Grain Production in SAARC Countries The SAARC countries which account for over 22 percent of total population of the world with India alone contributing about 1.17 billion people (over 17 %) has high 24
population growth in relation to output growth and resulted in marginal improvements on per capita basis. The expansion in food production and general economic growth of SAARC region are somewhat satisfactory. Agricultural production in South Asia is prone to high risks resulting from high variations in weather. The future projections of climate change indicate that is South Asia is very likely to be affected by warming during this century. The availability of freshwater is projected to decrease and coastal areas will be at greatest risk due to increased flooding from the sea and rivers. It is predicted that a rise in temperature may reduce yields of rice, wheat, other cereals, and certain cash significantly (ICRIER, 2009). South Asian agriculture is still highly dependent on weather and vulnerable to natural disasters. The total volume of food production fluctuates widely from year to year. The problem is aggravated by factors such as rapidly growing population, skewed distribution of assets and income, degradation of the natural resource base and unsustainable management of land and water resources, which include increased and imbalance in the use of plant nutrient, loss of soil fertility and growing use of pesticides (SAARC/FAO, 2006). Mittal et.al, (2009) on their working paper “Food Security in South Asia: Issues and Opportunities” highlights the following impact of climate change on food grain production:
vii)
Bangladesh
On an average during the period 1962-1988, Bangladesh lost about 0.5 million tons of rice annually as a result of floods. This amounts to nearly 30% of the country's average annual grain food imports (Paul and Rashid 1993). Karim et al (1996) project a net negative effect of climate change on the rice yields. The estimated impacts on rice yield vary between -6% to +14% depending on different climate change scenarios.
viii)
Bhutan
In Bhutan, upland crop production, practiced close to the margins of viable production, can be highly sensitive to variations in climate. Climate change will cause the cultivating zone to shift upwards to unsuitably steep slopes if temperatures increase by 2 ºC. It is also expected to increase the severity and frequency of monsoonal storms and flooding in the Himalayas, which could aggravate the occurrence of landslides. In addition to the danger to life and property, some of the 25
generated sediments may be deposited in agricultural lands or in irrigation canals and streams and will result in a deterioration in the quality of agricultural lands and hence productivity (NEC 2000).
ix)
India
Wheat yields in central India may drop by 2% in a pessimistic climate change scenario (GoI 2004). Kumar and Parikh (2001) show that even after accounting for farm level adaptation, a 2 °C rise in mean temperature and a 7 % increase in mean precipitation will reduce net farm revenues by 8.4% in India. Districts in western Rajasthan, southern Gujarat, Madhya Pradesh, Maharashtra, northern Karnataka, northern Andhra Pradesh, and southern Bihar are highly vulnerable to climate change Numerous physical (e.g. cropping patterns, crop diversification, and shifts to drought/salt-resistant varieties) and socioeconomic (e.g. ownership of assets, access to services, and infrastructural support) factors come into play in enhancing or constraining the current capacity of farmers to cope with adverse changes (TERI 2003). The major food-grain producing regions of Haryana, Punjab and western Uttar Pradesh experience the most negative effects, along with the coastal districts of Tamil Nadu. Punjab and Haryana are significant from the perspective of food security in India. These regions are also facing severe depletion of groundwater resources due to intensive cultivation techniques. Temperature rise of 1.5 degree centigrade and 2 mm increase in precipitation could result in a decline in rice yields by 3 to 15 %. Sorghum yields would be affected and yields are predicted to vary from +18 to -22 % depending on a rise of 2 to 4 degree centigrade in temperatures and increase by 20 to 40 % of precipitation (IPCC 2001).
x)
Nepal
Soil loss is a major cause of decline in agricultural production in Nepal and the negative effects of climate change may further aggravate this situation. The impact of a rise in temperatures on wheat and maize is expected to be negative (Mittal et.al, 2009). A survey done by ICIMOD and UNEP, highlights that 26 lakes in Nepal are categorized as dangerous due to threat to glacier lake outburst floods (GLOFs) (WWF 2005). As highlighted by IPCC (2001), glacial melt is expected to increase under changed climate conditions, which would lead to increased summer flows in some river systems for a few decades, followed by a reduction in flow as the glaciers 26
disappear. DHM (2004) found that almost 20% of the present glaciated area above 5000 m altitude is likely to be snow and glacier free with an increase of air temperature by 1ºC. Similarly, a 3-4ºC temperature rise would result in the loss of 58 to 70 % of snow and glaciated areas with threat of GLOFs. Shrestha et al (2003) revealed increasing number of flood days and consecutive days of flood events in Nepal, and 26 lakes have been identified as dangerous with respect to glacier lake outburst floods (WWF 2005). Haritashya et al (2006) used remote sensing techniques to observe surging and variation in the frequency and size of supra-glacial lakes in the Hindukush and Karakoram Himalayas. In Putalibazar municipality of Syangja district, Nepal, disaster losses show an increasing trend over the last 20 years not only due to a recorded increase in rainfall but because of increased settlements in the floodplains and improper road construction (Shreshtha 2006).
xi)
Pakistan
In the hot climate of Pakistan, cereal crops are already at the margin of stress. An increase of 2.50C in average temperature would translate into much higher ambient temperatures in the wheat planting and growing stages. Higher temperatures are likely to result in decline in yields, mainly due to the shortening of the crop life cycle, especially the grain filling period. A report by ministry of environment highlighted that crops like wheat, cotton, mango, and sugarcane would be more sensitive to increase in temperatures compared to rice. The flow of Indus river is also likely to affect cotton production in Pakistan, which might be detrimental to the economy as it is the country‟s main cash crop. Wheat yields are predicted to decline by 6-9 % in sub-humid, semiarid, and arid areas with 1°C increase in temperature (Sultana and Ali 2006), while even a 0.3°C decadal rise could have a severe impact on important cash crops like cotton, mango, and sugarcane (MoE 2003).
xii)
Sri Lanka
Most crops, e.g., coarse grain, legumes, vegetables, and potato are likely to be adversely affected due to climate change. The highest negative impact is estimated for coarse grains and coconut production. A rise in temperature by half a degree is expected to increase the frequency of droughts and extreme rainfall events. This, in turn, is expected to reduce rice output by 6 %. Increased dryness will also adversely affect yields of key products like tea, rubber, and coconut (MENR 2000). With the tea 27
industry in Sri Lanka being a major source of foreign exchange and a significant source of income for labourers, the effects are likely to be grave. An ongoing AIACC project confirmed that changes in the monsoon rainfall pattern and an increase in maximum air temperature are likely to be the two key factors that will affect coconut production in the principal growing regions. The projected coconut production after 2040 in all climate scenarios, when other external factors are non-limiting, will not be sufficient to cater to local consumption because of population increase. Among the different stakeholders in coconut industry, the coconut oil (CNO) industry would be most affected.
2.2.5. Impact of Climate Change on Food Grain Production in Nepal The three crops rice, maize and wheat cover over 75% the total food production of Nepal. All the three crops rice, maize and wheat showed increased yield with doubling CO2 level but decline tendency at the elevated temperature. Among the three crops maize was the most affected by the rise in temperature although increased CO2 level could increase the crop yield. The Terai plain and hills of Nepal were more affected whereas the mountain showed favorable tendency (Pariyar and Sherchand, 2005). As 80 percent of the Nepalese population depends on agriculture for a livelihood and follow traditional cultivation practices, relying on rainwater and the seasons, any changes in climatic conditions affecting rainfall patterns will have an adverse impact on the livelihoods of most of the Nepalese people, which means that there is always the high risk of food insecurity. The impact of Climate Change on agriculture will eventually affect the economic well being of the population because it will have either a decreasing effect or an increasing effect on the production pattern of the agriculture sector affecting in turn the economy of the country in the similarly way (Regmi et.al., 2007). Many farmers in Nepal are feeling the impact of global warming in terms of reduced agricultural production. Traditional crops like rice, maize, wheat and millet have had lower yields in recent year due to extreme weather (drought and floods). Bajura is considered most food-insecure district was badly affected by drought in 2009. 87 percent of Bajura‟s population (nearly 125,000) is food-insecure. Same like in Humla 28
nearly 85 percent of its population (almost 50,000) is suffering from food insecurity. The other areas which already suffer from very low agricultural production are Mugu, Kalikot, Jumla, Dailekh, Accham, Doti, Bajhang, Darchula and Baitadi. Therefore, some of them are switching and some of them are planning for other favorable production (IRIN, 2010). The effect of climate change and drought on agriculture and food security will have serious implications for sustainable development. Food security in developing countries is already threatened by trade, population growth, human induced deforestation and desertification. Climate change is another factor threatening the ability of people to obtain food. Climate change impacts in agriculture, forestry/biodiversity, and health would have serious consequences for Nepal and people‟s livelihoods. In the context of Nepal different studies identify the agriculture sector is vulnerable due to climate change. Agriculture sector is highly dependent on the weather, and given the low productivity increases of the last few years compared to population growth, climate change is likely to have serious consequences for Nepal‟s agriculture. Most of the population is directly dependent on a few crops, such as rice, maize, and wheat. Decreased precipitation from November to April would impact the winter and spring crops. Rice yields would fall in the Western and Far Western Regions where a greater population of the poor live, threatening food security. Due to diversity in topography, culture and climatic conditions, different cropping patterns are in use in Nepal. Extreme events such as rainfalls causing flooding and landslides, droughts, heat stress, hot winds, cold waves, hailstones and snowfalls are undesirable; in resent years, their frequency seems to have increased noticeably in the country, and long dry spells and cold waves have negatively affected the crop production. These can lead to crop failure and eventually to a famine in the country. Similarly, high humidity creates a favorable environment for the growth of fungal and bacterial diseases. In addition, some insects and pests become active and damage the crops (HDR, 2007). IPCC (2007) in its global report expressed serious concern about food insecurity in Nepal due to changes in rainfall patterns. It said livelihoods would be affected. Nepal‟s agriculture will face many challenges over the coming decades as the soils are degrading and water resources will place enormous strains on achieving food security for growing populations. These conditions may be worsened by climate change. Warming of more than 2.50C could reduce global food supplies and 29
contribute to higher food prices. Decline in food production would lead to more malnutrition and huge consequences particularly for children. In 2001, the total population of the country was marginally self sufficient from domestic production. Food sufficiency in the beginning will marginally go down but at later stage would show slight improvement due to the expected technological innovation and lower population growth. There will be marginal surplus under the normal scenario but will be barely meeting the requirement under the climate change scenario when the CO2 level expected to double.
2.3. Climate Change and Wheat Production luigi et al., (2008), in his review paper in the latest Agriculture, Ecosystems and Environment looks at what climate change will do to wheat, and what can be done about it. There are 12 different types of places where wheat is grown around the world which is so-called “mega-environments.” They range from “high rainfall, hot” (e.g. in Bangladesh) to “low rainfall, severe cold” (around Ankara in Turkey). Some are better than others. One of the best is northwest Mexico, the Indo-Gangetic Plains and the Nile Valley that amounts to 32 million hectares and accounts for 15% of global wheat production, it is in trouble. When you look at the likely 2050 climate, half of the area of the Indo-Gangetic Plains which is now in mega-environment might well need to be re-classified from pretty ideal low rainfall, irrigated, temperate to heatstressed, short season. That is, conditions will look more like the Gezira in Sudan or Kano in Nigeria. That will reduce yields, affecting 200 million people. So wheat breeders will have to develop varieties that can maintain yields under higher temperatures, unless you want farmers to switch to another crop entirely. Which might be the easiest thing in some places, actually, but that‟s another story. 2.3.1. Global Level Singh et al. (2007) in their study found, “Wheat is one of the most important food staples for mankind. It is cultivated on 15.4% of the arable land in the world and in almost all countries, except the humid and high-temperature areas in the tropics and high-latitude environments, where fewer than 90 frost-free days are available for crop. Wheat is the primary source of calories for millions of people worldwide, accounting for around 30% of global grain production and 44% of cereals used as food, of which 30
18% is traded internationally. Globally, approximately 220 million ha of land is sown to wheat each year, producing about 600 metric tons, with nearly half of this area and production attributed to developing countries. In addition, developing countries consume most of the wheat sold on the export market, reflecting a huge number of consumers in these countries. In some countries, such as those in North Africa, per capita consumption of wheat is as high as 240 kg per annum. Wheat provides nearly 55% of the carbohydrates and 20% of the food calories consumed globally”.
Nassiri et al. (2006) with their study on impact of climate change on wheat production highlights that, “A number of climate change impact studies on wheat production around the world have reported varying results. Yield increased under climate change scenarios in Australia, in most cases. Yield gain moderated with increased CO2 concentration at a higher temperature, and in some cases decreased under a reduced rainfall scenario (Luo et al. 2003). Asseng et al. (2004) found in the Mediterranean environment of Western Australia that the impact of elevated CO2 and temperature on wheat yield varied with seasonal rainfall amount and distribution. Kosmas and Danalatos (1994) projected that in Greece by 2050 rainfed wheat biomass could decrease by 90, 72, and 53% with rainfall reduction of 65, 50, and 30%, respectively. Attri and Rathore (2003) observed in India an increase of 1.080C and a doubling of the atmospheric CO2 concentration could increase wheat yields by 29 – 37%. However, Attri and Rathore (2003) found further increases in temperature beyond 3.80C would negate the beneficial impacts of enhanced CO2, and wheat yield would decrease by 20%”. CIMMYT (2010) in its proposal “WHEAT ‐ Global Alliance for Improving Food Security and the Livelihoods of the Resource‐poor in the Developing World” states that wheat is second only to rice as a source of calories in the diets of developing country consumers, and it is first as a source of protein. Wheat is an especially critical stuff of life for the approximately 2.5 billion poor, men, women and children, who live on less than USD 2 in countries where wheat is among the top three food crops. Wheat provides 21% of the food calories and 20% of the protein to more than 4.5 billion people in 94 developing countries. As per their report, wheat production has increased to 3.6% per annum during 1996 on developing countries which are credit for creation and use of highly yielding, semi31
dwarf varieties and improved cropping system along with favorable policies and institutional support. With which the poverty and hunger dramatically reduced. But productivity growth has slowed steadily in wheat, slipping to 2.8% during 1984-94 and 1.1% during 1995-2005, an outcome mainly of flagging investments in wheat research and development. Demand for wheat in the developing world is projected to increase 60% by 2050. At the same time, climate change induced temperatures increases are likely to reduce wheat production in developing countries by 29%. As a result, prices will more than double, eroding the purchasing power of poor consumers and providing the basis for wide‐spread social unrest. This scenario is worsened by stagnating yields, soil degradation, increasing irrigation and fertilizer costs, and virulent new disease and pest strains. They argued by 2050, agriculture‟s share of water use now at 70‐80% will decline to 60‐70%, through competition with urban areas for diminishing water supplies. At the same time, farmers can expect sharp increases in the price of fertilizers, driven by rising costs for fossil fuels and depleting reserves of phosphorus and potassium. Adequately irrigating and fertilizing wheat crops will become ever more difficult. Even more disconcerting are the implications of climate change for wheat throughout the developing world. No other major staple crop is expected to suffer the production losses projected for wheat from rising temperatures in low‐latitude countries. In the absence of significant efforts to improve wheat's heat tolerance, average yield losses in low and middle income countries of 16% are expected by 2025 and 29% by 2050, representing foregone grain worth an estimated USD 20 and 46 billion, respectively, each year. South Asia‟s Indo‐Gangetic Plains, the breadbasket created by the Green Revolution, is especially at risk. The region is now considered optimal for wheat farming, but rising temperatures and changing rainfall patterns will reduce wheat harvests by 17‐35% on more than half its croplands by 2020, jeopardizing food supplies for as many as 900 million people, which is about one‐seventh of the entire human population. This could result in the politically risky prospect of South Asia having to import as much as a third of its wheat consumption by 2050. Together, West Asia and North Africa have the highest per capita wheat consumption and imports as well as yield losses, and year-to-year production swings will increase in that region, due to rising temperatures, more severe weather extremes, and decreasing water availability. Climate change may also give rise to new or more virulent races of 32
major wheat diseases and pests; this brings threat to world wheat production. More than 90% of the varieties currently in production are suffering from wheat stem rust (i.e. Ug99 race of stem rust) and production shortfall. Of the three major cereal crops, wheat is the most sensitive to high temperatures but also the most water‐use efficient. Since heat and drought often go together, this constitutes a strong argument for enhancing heat tolerance and improving the crop to use water more effectively. According to them global problems could affect the success of the food production. The three most significant wheat risk facing wheat as a whole are: 1) Financial risk – funding for the CRP, a global crisis could lead to reduce funding along with political pressure to cut aid financing. 2) Implementation risk – it should not focus on particular countries; if so, such implementation risk could include inept or seriously inefficient CRP management combined with inept or seriously inefficient oversight functions. And 3) Domino effect risk - a particular failure of the CRP in a particular area, while not CRP‐threatening in and of itself, could conceivably be blown out of proportion to affect the CRP as a whole. And they suggest mitigation approach for this risk factor as follows: 1) Financial risk - develop both public and private sources of funding, and both Consortium and non‐ Consortium sources; broaden sources of finance. 2) Implementation risk - strong monitoring and evaluation, both within the Consortium as well as independently of the Consortium; broad‐based advice and feed‐back opportunities; effective approaches for decision making and conflict resolution. 3) "Domino effect" risk - strong safety and control standards for product releases, coupled with a steady and reliable communications function.
Tobey et al (1992) used SWOPSIM statistical world policy simulation based on General Circulation Model (GCM). The model used by them is static in nature in the sense that it presents only on spot effect of doubling of CO2 on global agriculture. The model used 20 agriculture commodities. According to their result the negative impact of climate change on some region would not sabotage the world agriculture market rather this negative impact would be counterbalanced by agriculture yield of some other region which would experience positive impact of the global warming of climate change.
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2.3.2. Regional Level Eva Erdelyi (2006), in her PhD thesis under the title “Climate Change and Winter Wheat: Possible Impacts and Responses” analyzed the climate change impacts on winter wheat which is one of the most cultivated plant in Hungary. The aim of her research was to learn the possible changes and their effects to the plant phenology and yield as much as possible based on comparative statistical analyses and crop modeling results. She studied the production risk between 1951 and 1990. For the simulation she used the 4M model which is based on CERES model, adapted to Hungarian circumstances by applying the results of Hungarian scientists. The major finding of this research can be categorized into two different sections: The first step of her research was trying to examine the production data of winter wheat by analyzing how the production risk has changed with time. For this she studied the winter wheat production data for four selected countries. The observed time interval was 1951-2005, which was split into five twenty-year intervals for the later analysis (i.e. 1951-1970, 1961-1980, 1971-1990, 1981-2000 and 1986-2005). She found, comparing the first three times intervals to last two times intervals, the risk of wheat production has increased in all the four countries. The next step was to study the climatic needs of the plant through the most important periods of its development. By her analysis, she found out the quantity of winter wheat might be better in near future because climate needs of winter wheat will be fulfilled, but there is uncertainty on increasing on production. By the simulation exercise she found about 18% yield increase for 2031-2040 compared to 1961-1990 and close to 6% for the two weeks earlier but with increasing variation. From the possibilities mentioned in her thesis, the increase of the production risk proves the fact that the climate has already been changing (which we can already experience the effects). The predicted meteorological parameters do not change much in the near future – as it does up to the end of the century – but they can have serious impacts on the agriculture. The more frequent the extreme weather events are, the more we can be convinced of uncertainty. There are lots of questions about the change itself and also on the possible impacts. For planning the response strategies we need wide range multidisciplinary approaches and collaborative research work on both national and international level. Living under changing climate conditions we have to answer many questions. Our duty in the future is to find out the possible ways 34
how the positive effects of climate change can be utilized and, at the same time, how the negative effects can be prevented, avoided or reduced. Risk caused by climate change should also be managed with coordinated adaptive strategies. Researches on impacts and adaptation possibilities have to support the decision makers in policy as well as in agriculture with information and plans. There is a wide scientific consensus that if these changes continue, significant damage of global ecosystems, food production and economies will ensue, so further interdisciplinary, collaborative research projects are very much needed all over the world. M. Sh. Abd El-Maaboud et.al. (2002) in their paper under title “Climate Change and Productivity of Some Wheat Cultivars Under Rainfed and Supplementary Irrigation Conditions” tries to show the climate change effect over wheat production under rainfed and irrigation conditions. For this, they measured data taken from a field experiment carried out at Maryout (Northwest Coast of Egypt) during 1991-92, 199293 and 1993-94 growing seasons to evaluate the productivity of some bread wheat varieties under rainfed and supplementary irrigation conditions at different growth stages. Their experiment included twenty-four treatments, which were the combination of four supplementary irrigation schedules: (i) only rainfed 170 mm of rainfall; (ii) one irrigation at heading stage 480 m3/ha; (iii) one irrigation at milk ripe stage 480 m3/ha; and (iv) two irrigations at the above stages (960 m3/ha) and six wheat varieties (i.e. Sakha 8, Sakha 69, Giza 155, Cham 4, Cham 6 and Gomam). They conducted theoretical study at the Central Laboratory for Agricultural Climate (CLAC) to estimate the efficiency of Decision Support System for Agrotechnology Transfer (DSSAT) in predicting the wheat yield and climate change effect on the yield prediction. The crop simulation models used in their study are included in DSSAT 3.1. DSSAT estimates the coefficients for a genotype by iteratively running the crop model with an approximate value of the coefficients concerned, comparing the simulated and measured data, then automatically altering the cultivar coefficient until the simulated and measured values match or are within predefined error limits. As a result, they found there were significant and favorable differences between the six wheat cultivars where Cham 4 and Giza 155 cultivars had the higher grain yield under rainfed treatment. Whereas, under one supplementary irrigation treatment Sakha 69 cultivar recorded the higher grain yield followed by the Cham 6 cultivar. Moreover, Cham 6 and Sakha 69 cultivars produced the higher grain yield under the 35
two supplementary irrigation treatments. On the other hand, there was a significant increase in the grain yield with increasing supplementary irrigation times. The potential impacts of climatic change on wheat production were evaluated by simulation of wheat production under climatic change conditions by the year 2040 compared to the predicted production under current conditions. In this respect their results indicated that the grain yield increased differently according to the wheat cultivar. This may be due to the positive effect of duplication in CO2 on wheat as C3 plants. They suggest that crop model programs are currently needed to study the impact of climate change on agricultural production. This could help the decision makers to implement future agricultural strategies together with different scenarios related to agricultural practices. One of these programs is DSSAT (Decision Support System for Agrotechnology Transfer), which is used to evaluate and predict wheat yield under all environmental conditions such as soil, weather, irrigation and fertilizers and simulate the yield of different wheat cultivars to select the cultivar(s) for different environments.
Gbetibouo and Hassan (2004) employed Ricardian model on wheat, sorghum, maize, sugarcane, ground nut, sunflower and soybean for the South African region. They found that temperature increase would be having positive impact on the agriculture production of maize, sorghum, sunflower, soybean whereas it would be having negative impact on sugarcane and wheat productivity. They said that this region is already having high temperature and any further increase in temperature in future due to climate change would havoc the wheat productivity. They suggested to replace wheat by maize and sorghum or other heat adapted crops in order to avoid possible loss of yield due to increased temperature.
Wolf et al. (1996) compared five wheat models designed for Europe at different levels of agronomic conditions. They concluded that almost all the models predicted the same results. Their results showed that temperature increase would result in yield reduction whereas increased level of precipitation and CO2 fertilization would have positive impact on the production of wheat for Europe.
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Anwar et al (2007) used the Australian Commonwealth Scientific and Industrial Research Organization (CSIRO‟s) global atmospheric model under three climate change scenarios which were Low, Mid and High for the time period of (2000-2070) for South-East Australian location. Their results showed that for all the three scenarios the medium wheat yield declined by about 29%, however in the presence of elevated CO2 affect reduced this decline in production from 29% to 25%. CO2 fertilization affect offset a very small level of low fall rain and higher temperature. They suggested that higher yield could be made through better agronomic strategies and variety of wheat.
Cerri et al. (2007) used simulation model for Central South region of Brazil up to 2050. They revealed that 30C to 50C increase in temperature and 11% increase in precipitation would cause to decrease the productivity of wheat to the level equal to one million ton by weight. They said that in Brazil wheat had already been cultivating at the threshold level of temperature and any further addition to this level of temperature would cause to decline agricultural production specially wheat. They further added that most of the developing countries lying on the tropical belt and relying on agriculture would, face losses in agricultural yield.
Lobell et al. (2005) used CERES-Wheat simulation model for the climate trend effect on wheat production in the Mexico region. They studied the climate trend and wheat yield for the last two decades from 1988-2002. They found that the climate had favored during the two decades and resulted in 25% increase in wheat production. It means climate was having positive effect on the wheat yield for this region. However 25% increase is less as compared to the previous studies which predicted higher increase in wheat productivity for this region.
2.3.3. SAARC Level South Asia which is among the most populous region of the world is regarding as a low income region with a vast number of small and marginal farmers. Wheat is a major staple food crop of this region along with the rice (Joshi et al, 2007). Wheat is the second major staple crop in India and Pakistan and is also gaining similar importance in Nepal and Bangladesh. Wheat production in South Asia has increased 37
from 15 million ton in 1960s to 95.5 mt during 2004–2005. It still needs to grow at the rate of 2–2.5% annually until the middle of 21st century. However, for India, recent estimations have shown a growth requirement of about 1.1% (Chatran et al., 2007). The wheat acreage in south Asia is more than 36 million ha which is around 16% of the global wheat area and produce 15% of world wheat (FAO, 2007). Chatran et al. (2007) in their study “Challenge to Wheat Production in South Asia” state that wheat is a major staple crop after rice in all South Asian countries. Having total population 1.5 billion it is one of low income region of a world which could suffer from food crisis sooner. The current wheat production of this region is around 95 million tons and demand for 2020 is estimated to be around 137 million tons. Though the demand increases, there is less chance to increase supply because of urbanization diversification, dwindling water resource and soil health deterioration. They argue the need to produce more wheat has to be met with fewer resources in a sustainable and cost effective way. This targets appears to be achievable since there is a big yield gap around 2 tons/ha between research station farmers field. In their report they point outs the several constraints associated with wheat cropping system. They states Rice-wheat cropping system is one of the most important cropping pattern for food and nutritional security in the South Asia and prevalent in the fertile, alluvial Indo-Gangetic Plains (IGP) covering around 14 million hectares area of Bangladesh, India, Nepal and Pakistan. The major constraints for wheat coping system are due to delay showing of wheat, which exposes the crop to unfavorable temperature along with soil fertility and scarcity of natural resources, salt affected soil, availability of improved seeds, water quality constraints and socioeconomic constrains. Then, they suggest the different strategies to cope with such kinds of turbulence. It is important to incorporate late heat tolerance in the wheat germplasm which is expected to be a bigger problem in future due to increasing global warming. In addition, it is imperative to develop genotypes that are early in maturity in order to escape the terminal heat stress, and thus nick well in the RWCS. Timely availability of crucial inputs like improved seeds, fertilizer and water; better access to irrigation water with reliable water in canal system; strong extension service and good efforts to reach out to farmers; needed infrastructure development in energy, transport and related sectors; 38
proper land consolidation and resource sharing; organized grain marketing and trade; providing incentive to produce more; lessening social taboo against change; quality and vocational education levels of both men and women with reliable credit facilities. Among these, timely availability of irrigation, quality seed and fertilizer play the most crucial role in determining wheat production. Perveg et al. (2009), in their report “Impact of Climate Change on Wheat Production: A Case Study of Pakistan” shows that Climate change is an emerging issue of agricultural production and geographical location of Pakistan. The objective of their study was to look at the impact of climate change on wheat production which is the main food crop of Pakistan. They used Vector Auto Regression (VAR) model to evaluate the impact of global climate change on the production of wheat in Pakistan. Their study considered annual data from 1960 to 2009. On the basis of this historical data the study captures trends for the impact of climate change on wheat production for the period 2010-2060. They found Climate change is basically due to the increased in the concentration of greenhouse gases (GHGs) like carbon dioxide, methane and nitrous oxide through anthropogenic activities. These gases trap the sunlight and increase the earth‟s overall temperature. This higher temperature may negatively affect the growth process of wheat and hence decreases the productivity of wheat. The results from their estimation revealed that global climate change may influence the wheat production in Pakistan. Therefore, appropriate adaptative and mitigative techniques as well as measures like timely cultivation, better irrigation system, new technology and utilization of drought resistant seeds had been recommended to cope with or at least to reduce this newly emerging hazard of global climate change on wheat production in Pakistan. Hussain and Mudasser (2007) used Ordinary Least Square (OLS) method to assess the impact of climate change on two regions of Pakistan, Swat and Chitral 960m and 1500m above the sea level, respectively. They investigated whether increase in temperature up to 30C would decrease the growing season length (GSL) of the wheat yield of this county. Their result showed that increase in temperature would create positive impact on Chitral district as its location on high altitude and negative impact on Swat because of its low altitude position. An increase in temperature up to 1.50C 39
would create positive impact on Chitral and would enhance the yield by 14% and negative effect on Swat by decreasing its yield by 7%. A further increase in temperature up to 30C would decrease the wheat yield in Swat by 24% and increase in Chitral district by 23%. They suggested adaptation strategies of cultivating high yielding varieties for warmer areas of northern region of Pakistan because of expected increasing temperature in the future.
2.3.4. Climate Change and Wheat Production in Nepal Wheat is the third largest cereal crop in Nepal after rice and maize. Before the introduction of Mexican semi-dwarf wheat varieties, wheat cultivation in Nepal was limited to mid and far-western hills only and it was considered as a minor cereal in the country. After the introduction of semi-dwarf varieties from Mexico, the area and production of wheat in Nepal has been increased dramatically and now it has significant contribution to the national food supply (NARC). The area under wheat cultivation has increased more than three folds (i.e. 228400 ha during 1970/71 to 706481 ha during 2007/08) and wheat production has increased more than eight folds (i.e. 193760 mt in 1970/71 to 1.57 million mt. in 2007/08). In mountain region area increase almost double and yield (production of wheat in this region increased from 28,900 mt to 83,739 mt from 1970/ 71 to 2007/08) increase to the above 50% which is 7.6% of the total wheat area and contributes 5.3% of the total wheat production in the country. The hills region showed nearly three folds increase of area and the yield showed 100% increase during the same period from 1970/71 to 2007/08. The production of wheat in this region increased from 82,800 mt to 447,791 mt during the study period. This region includes only 34.57% of the total wheat area and contributes 28.5% of the total wheat production in the country. Similarly, there are 57.8% the area of wheat which lies in the Terai and similar environment contributes about 66.2% of the country‟s total wheat production. During the last 38 years period from 1970/71 to 2007/08, the production of wheat in the Terai region increased from 81,600 mt to 1,040,535 mt. Due to the topographical differences within short north-south span of the country, Nepal has wide variety of climatic condition. About 70 to 90% of the rainfall occurs during the summer monsoon months (June to September) in Nepal and the rest of the months are almost dry. Wheat is cultivated during the dry winter period and therefore, the supplementary irrigation plays a vital role in its cultivation. 40
Varieties of wheat have been developed to suit the local climatic conditions. Due to the availability of improved seeds, modern cultivation practice and a supplementary irrigation; the wheat cultivation has increased substantially throughout Nepal.
The present national average wheat productivity is 2156 kg/ha. Wheat is cultivated in 20 percent of the total cultivated land area and contributes 18.8 percent to the total national cereal production. Per capita wheat consumption has increased from 17.4 kg in 1972 at the time of NWRP establishment to 60 kg in 2007. In Terai, as irrigation facility is steadily increasing there is still ample opportunity to expand the wheat area where the lands remain fallow after rice harvesting (SOHAM, 2009).
Nepal occupies 16th position among 31 countries that are suffering from a food deficit. According to the statistics of the Ministry of Agriculture and Cooperatives, wheat production was down 17 percent this fiscal year 2009/10 due to drought. Similarly, drought brought down paddy production by 11 percent and corn by 4 percent.
Nayava (2009), on his research under “Impact of Climate Change and Modern Technology on Wheat Production in Nepal: A Case Study of Bhairahawa” attempts to show possible impact of climate change on wheat. They cover all three ecological belts and a case study at one national wheat research centre at Bhairahawa, Terai, where the irrigation facilities are available. They studied relation between climate and wheat production in Nepal for the period 1970/71-2007/08. They found the national area and production of wheat has remarkably increased from 228,000 ha to 706,481 ha and 193360 mt to 1,572,065 mt during 1970/71 to 2007/2008, respectively. The present rate of annual increase of temperature was 0.060C in Nepal. Trends of temperature rise were not uniform in Nepal. An increase of annual temperature at Bhairahawa during 1970-2008 was only 0.0180C. However, the wheat growing seasons at Bhairahawa, the trend of annual maximum temperature during November to April was -0.00680C during their study period. As a result, the decrease of wheat cultivation was noted up to 1998 and latter the area of wheat has been increasing trend as shown. Another puzzling factor, the yield showed sharp up and down in different years. Thus up to 60% variations of productivity of wheat depended on weather fluctuation. Though modern facilities such as irrigation, improved seeds and fertilizers 41
are available to some extent, weather and climate still plays an important role in the increase of area and production of wheat in Nepal. They conclude saying that study in future at different temperature regimes in Nepal and extensive field works are necessary. For this purpose collection of the information on fertilizer application, irrigation, time of weeding and their cultural practices is urgent issue. Future planning to increase the wheat production in Nepal should give due consideration to the effect of global warming also. Therefore, in the present scenarios of climate change, the overall impact of agriculture has to be explored.
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CHAPTER III RESEARCH METHODOLOGY The methodology of this research work has incorporated the research design, nature of data, collection procedures, models and tools of analysis and presentation of findings. 3.1. Theoretical Framework Statistical and econometric techniques have been employed to establish a logical association between climate variation and change. Multiple regression methods has used to estimate the impact of meteorological variables on wheat yield. The econometric approach is based on a statistical relationship between output (e.g. income) and input (weather). By regressing agricultural sector performance on a set of climate variables (rainfall and temperature), traditional inputs (land and labor) and support systems (infrastructure such as irrigation), it becomes possible to measure the contribution of each factor to the outcome and project the effects of long term climate change on the agricultural sector. The figure below shows the theoretical framework adopted from Kavikumar (2009) and modified as per the main focus of the study.
The research steps show the pattern that should follow during the research period by any researcher. Therefore, this thesis also has been designed with two broad categories based on research steps as theoretical steps and empirical steps. In theoretical steps, we mention what is climate change and what it can bring as a result along with agricultural production and in this research especially with the case of wheat. Finally, food deficit or surplus after the changes in climate change and its impact on welfare level which is the prime concern of this thesis. For showing this, the empirical analysis, that helps to go along with total work what the thesis designed for. This theoretical framework works as a roadmap for the entire research to come up with policy impact. Starting with Nepalese climatic scenario and its impact on food grain production focusing wheat in this research by following Recardian model to see the welfare change at target group and region is crystal clear once the methodology set as empirical steps. Hence, this research follows the aforementioned methodology strictly.
43
Ricardian Approach
CS Evidence on Farmer Response
Mendelsohn et al., 1994; Kumar and Parikh, 2001; Kumar, 2003; Mendelsohn et al, 2004; Sanghi and Mendelsohn, 2008 Climate Response Function
Economic and Welfare Implications
Data on Geo - Bio – Climate and CC
Agronomic Models
Physical Response of Crops
Agronomic-Economic Approach
Source: Kavikumar, 2009
Economic Models
Adams et al. 1990, 1999; Rosenzweig and Paryy 1994; Kumar and Parikh,2001; IIASA, 2002
Figure 3.1: Theoretical Framework 3.1.1. Approaches to Measure the Effect of Climate Change on Agriculture Three basic approaches have been used to assess the likely economic effects of climate change on agriculture: agronomic-economic models, agro-ecological zone models and Ricardian cross-sectional models (Mendelsohn & Dinar, 1999).
3.1.1.1. Agronomic-economic models This analytical technique make use of well-calibrated crop models from carefully controlled experiments in which crops are grown in field or laboratory settings that simulate different climates and levels of carbon dioxide (Adams et al., 1989, 1990, 1993, 1999; Easterling et al., 1993; Kaiser et al., 1993; Rosenzweig & Parry, 1994; Parry et al., 1999, 2004; Kumar & Parikh, 2001). To ensure that all different 44
outcomes across experimental conditions can be assigned to the variables that are being investigated (temperature, precipitation or carbon dioxide), no variability is allowed in farming methods. In addition, farmer adaptation to changing climate is not included in the estimates from these models. Economic models are then used to predict aggregate crop outputs, prices and net revenue using the yields from the agronomic models (Mendelsohn & Dinar, 1999). 3.1.1.2 Agro-ecological zone analysis This approach assigns crops to agro-ecological zones and yields are then predicted (FAO 1996). The agro-ecological models examine changes in agro-ecological zones and crops as the climate changes. The agro-ecological models, examines changes in agro-ecological zones and crops as climate changes and predict the effect of alternative climate scenarios on crop yields. Economic models then use the yields changes to predict the overall supply and market effects (Darwin et. al., 1995). According to Mendelsohn and Dinar (1999), the climate scenarios can be relatively simple stories of uniform changes across a country, or they can involve complex geographic distributions of changes. As a result most impact studies examine multiple climate scenarios.
3.1.1.3. The Ricardian cross-sectional approach Cross-sectional models measure farm performances across climatic zones (Mendelsohn et al., 1994, 1996; Mendelsohn, 2000, Mendelsohn & Dinar, 1999, 2003; Sanghi, 1998; Sanghi et al. 1998). The Ricardian approach is the common cross-sectional method that has been used to measure the impact of climate change on agriculture. The method was named after David Ricardo (1772–1823) because of his original observation that land rents would reflect the net productivity of farmland (Mendelsohn & Dinar, 2003). The Ricardian approach has been applied in the United States (Mendelsohn et al., 1994, 1996) and in some developing countries – Brazil (Sanghi, 1998), India (Sanghi et al., 1998; Kumar & Parikh, 1998, Kavikumar, 2009) and South Africa (Gbetibouo & Hassan, 2005) – to examine the sensitivity of agriculture to changes in climate.
The Ricardian approach regresses farm performance (land value or net income) on a set of environmental factors, traditional inputs (land and labor) and support systems (infrastructure) to measure the contribution of each factor to the outcome and detect 45
the effects of long-term climate change on farm values (Mendelsohn et al., 1994, 1996; Mendelsohn & Dinar, 1999). In a well-functioning market system, the value of a parcel of land should reflect its potential profitability, implying that spatial variations in climate derive spatial variations in land uses and in turn land values (Polsky, 2004). With this background, it should be possible to estimate a meaningful climate–land value relationship by specifying a multivariate regression model. The estimated coefficients for the climate variables would reflect the economic value of climate to agriculture, holding other factors constant.
The Ricardian cross-sectional approach automatically incorporates farmer adaptation by including adaptations farmers would make to tailor their operations to a changing climate. An important example of farmer adaptation strategies is crop choice where, depending on the effects of warmer climate, a particular crop will be the optimal choice. Optimal crop switching is therefore an important factor to consider when measuring the impact of climate change on agriculture (Mendelsohn et al., 1994, 1996; Mendelsohn & Dinar, 1999). The Ricardian approach provides a framework for making a comparative assessment of „with‟ and „without‟ adaptation scenarios that can show how adaptation measures may help reduce this impact. Farmer adaptations that are implicit in the Ricardian model results are projected to largely offset the economic costs associated with climate change (Polsky, 2004). Farmers will use available information to their maximum economic benefit in adapting to climatic shocks in any economy at equilibrium. For instance, a standard Ricardian model would imply that if growing citrus crops is more profitable than growing wheat, and if the climate becomes more suitable for citrus than wheat, then those farmers will adapt to the changed climate by drawing on the experiences of citrus farmers elsewhere and switching from wheat to citrus (Polsky, 2004). In Nepalese case, since wheat is a winter crop, winter vegetables may be use as substituting crops.
A criticism of the Ricardian approach is that it fails to fully control for the impact of variables that could also explain the variation in farm incomes. For example, incomplete
specification
can
result
in
underestimating
the
damages
and
overestimating the benefits of climate change (Mendelsohn, 2000; Kurukulasuriya & Rosenthal, 2003). Variability in farms is a result of many factors besides the effects of climate change. Efforts in the Ricardian studies to control for this problem through 46
including other variables, such as soil quality, market access and solar radiation, are hampered by the difficulty of obtaining perfect measures of these variables (Mendelsohn, 2000). The result is that many of these factors may not be taken into account when assessing the impacts on farm revenues. According to Mendelsohn (2000), this is a common problem in developing countries where data is often incomplete. Household labor and animal power are two important variables in many developing country farms that are difficult to control for. The agronomic approach, on the other hand, does not face this problem of extraneous variables as it relies on carefully controlled experiments.
Assuming that prices will remain constant is another limitation of the Ricardian approach (Cline, 1996). Mendelsohn et al. (1994), as cited in Kurukulasuriya and Rosenthal (2003), agree that including price effects is problematic and that the Ricardian approach is weaker in that respect. Existing cross-sectional studies rely on a cross section within a country where there is little price variation across farms, with the result that the studies have not been able to estimate the effects of prices. The assumption in the Ricardian studies that prices are constant leads to bias in the welfare calculations (Cline, 1996). The cross-sectional approach only measures the loss as producer surplus from the climate change and ignores the price change that would occur if supply changed, and as a result omits consumer surplus from the analysis. The result is that damages are underestimated (omit lost consumer surplus) and benefits are overestimated (overstate value of increased supply) (Mendelsohn, 2000). The argument however, is that this also applies to all agro-economic models that are confronted with the same difficulty of predicting domestic price changes when changes in agricultural prices due to climate change are determined at the global level (Kurukulasuriya & Rosenthal, 2003). Despite the failure to address this problem, Mendelsohn et al. (1994) contend that the bias is less than 7% (Kurukulasuriya & Rosenthal, 2003).
Another limitation of the Ricardian approach is that measuring impact in a static spatial model would only be valid if technology, policy or any other temporally varying factor that would affect land use and farmers‟ production management decisions does not change, or if the value of alternative uses of the land does not change (Antle, 1995). For instance, technological changes would alter the relationship 47
between environmental characteristics and land values and thus the approach would give inaccurate effects of climate change on land values (Antle, 1995).
Failure to take account of water supply is another important criticism that has been raised concerning existing cross-sectional models (Darwin, 1999; Kurukulasuriya & Mendelsohn, 2006). In examining the effect of country climate on country production the existing models do not take into account water that might come from distant countries through rivers and other water supplies. According to Mendelsohn (2000), there has not been data available predicting the magnitude of these water supplies and how they in turn would be affected by climate change. In addition, the cross-sectional models have not considered the effects from flooding. Integrating water systems into agricultural analysis will be important to all approaches and Mendelsohn and Dinar, (2003) have made a significant contribution by testing the sensitivity of net farm revenues to other sources of water.
The above review of the literature shows that in spite of the weaknesses of the production function approach, the findings of studies based on it concur in some ways with those of studies using the Ricardian approach. Nevertheless, most of the production function approach studies seem to argue more strongly that climate change may be expected to have positive impacts on agriculture, while most Ricardian studies predict a negative impact. In addition, the production function studies show that farmers can overcome the adverse impact of global warming by implementing adaptation measures as the climate changes. 3.2. Research Design 3.2.1. Data Requirements and Sources The data for the analysis based on panal data from 20 Terai districts of Nepal, namely Jhapa, Morang, Sunsari, Saptari, Siraha, Dhanusha, Mahottari, Sarlahi, Rautahad, Bara, Parsa, Chitawan, Nawalparashi, Kapilbasthu, Lumbini, Dang, Banke, Bardiya, Kailali and Kanchhanpur. The comprehensive district level dataset for the period 1992/93 to 2009/10 for the purpose of the analysis were used. Agricultural data were assembled at district level in the dataset comprising demographic, meteorological and other data. Districts were selected on the basis of agro-climatic and hydrological zones, representation and latitude. Data regarding total estimated area under wheat 48
production, total estimated production of wheat and Population density were collected from Ministry of Agriculture and Co-operatives and Central Bureau of Statistics (CBS). Data concerning Hydrology, temperature and precipitation came from Ministry of Environment, Department of Hydrology and Meteorology, Babarmahal, Kathmandu, Nepal. Likewise, data related with Fertilizer, Improved seeds, use of manure and Price of agricultural inputs came from Department of Agriculture, Harihar Bhawan Pulchowk, Lalitpur. Besides that, we also used Economic Surveys, publications from Central Bureau of Statistics (CBS), Publications of National Planning Commission (NPC), World Development Report (WDR 2010) and different official and unofficial documents of different INGOs and NGOs along with newspapers as well as published and unpublished documents of various research institutions.
3.2.2. Defining Variables i.
Dependent variable
The wheat yield per hectare was used as the dependent variable to estimate the climate response function for Nepal.
ii.
Explanatory variables
Climate variables The suitable season of wheat production in Terai region of Nepal is from November to February for cultivated to harvest. Therefore, in this study only the mean temperature and precipitation of wheat production season (i.e. from November to February) has been taken. This dataset, created by the Department of Hydrology and Meteorology, which is the sole authority in Nepal for keeping data (i.e. hourly, daily, weekly, and monthly), is based on ground station measurements of precipitation and minimum and maximum temperature. The monthly means were estimated from approximately 18 years of data (1992/93–2009/10) to reflect long-term climate change. Temperature: Wheat is crop of cool environment. However, it requires different temperatures at different stages of plant growth and development. Temperature requirement may slightly differ from one variety to another at the time of germination, 49
however, general minimum temperature is required from 3.5-5.50C and optimum 20250C and maximum temperature is 350C. On temperature below or above to optimum, germination of seed decreases slowly. If temperature is more than 300C at the time of maturity it leads to force maturity and yield loss. Winter wheat bears cold waves and frost in a better way in comparison to spring wheat. Production of wheat has remarkably increased in Nepal. The present rate of annual increase of temperature was 0.060C in Nepal (NARC, 2001). Trends of temperature rise were not uniform in Nepal. However, the wheat growing trend with annual maximum temperature during November to Feb was 18.300C during the study period. As a result, production of wheat has been increasing trend as shown.
Another
puzzling factor, the yield showed sharp up and down in different years. Thus up to 60% variations of productivity of wheat depended on weather fluctuation (Navaya et. al., 2009). Precipitation (Rainfall): Wheat is cultivated in the region where annual precipitation occurs from 25 to 175 cm, though 75% wheat area falls where annual rainfall precipitation occurs between 37.5mm to 87.5 mm (NARC, 2001). Region with 62.54 to 87 mm rainfall are most suitable for wheat cultivation and out of this 10-15 mm rainfall is required when crop is in the field. Rainfall and snowfall at the time of maturity cause severe loss to wheat crop affecting yield and seed quality adversely. Economic variables Economic variables included in the estimation are use of Fertilizer, use of improved seeds, use of manure, total cropped area, total production and price of agricultural inputs. It is hard to collect actual data regarding socio-economic variable as we know the record of socio-economic variable are normally ignored by the peasant. Besides that, we also do not have strong research organization in nation. But as per requirement of our research, we collect the data from different sources (as mentioned earlier) in different forms and make them available for our purpose. The data has been extrapolated based on available information provided by Ministry and concern departments. To make extrapolated data genuine and authentic I discussed severally with the research head and the research division. Some idea and encouragement has been collected from the research officer of the concerned ministry and department for extrapolation of data. The following data has been extrapolated in the following way: 50
Fertilizer: Fertilizer is one of the major components for better production, but the district wise allocation of the data regarding fertilizer is not properly recorded. Eventhough, many study consider fertilizer as a regular input, they are not properly classified what percentage of share of fertilizer is normally used for wheat and what is the allocation of fertilizer districtwise? Therefore, we use fertilizer data available from Department of Agriculture, Market Research and Statistical Management Program, Harihar Bhawan, Lalitpur. The book calculates average cost of production and marketing margin per hectare. From the source we got per hectare fertilizer use in district and we calculate total fertilizer used for wheat. Modern wheat verities are very much responsive to fertilizer application and require 100:50:50 kg/ha NPK under irrigated conditions whereas in rain-fed wheat the additional of 60:40:25 kg/ha NPK is beneficial. Researches are in progress on the use of nitrogen fertilizer. For the purpose I had conducted several discussions with the concern people, peasant, and government authority along with some market information. They agreed in the regarding question positively and researchers are unable to quantify the exact number of quantity that peasant used, enter inside the country due to open boarder and easily availability during the peak hour of cultivation. During the phase of calculating total use of fertilizer and its quality several discussions regarding distribution of fertilizer for Terai region raised. Some most raised questions were (1) Do farmers use quality fertilizer provided by the government depot? (2) Is there any effect of open border for it? (3) Do fertilizer available freely during needy time? Due to mentioned problems regarding fertilizer and the research only needs quantity of fertilizer use for the production, we only calculated total fertilizer use in different districts in different year from the data collected as per hectare given by Department of Agriculture, Market Research and Statistical Management Program, Harihar Bhawan, Lalitpur Improved seeds: Seeds is another important factor of production of our analysis, therefore I calculated total improve seeds use. As fertilizer, seed data also available from Department of Agriculture, Market Research and Statistical Management 51
Program, Harihar Bhawan, Lalitpur. The book calculates average cost of production and marketing margin per hectare. From the source we got per hectare seed use in district and we calculate total seed used for wheat. Regarding the improved seeds as like fertilizer, it is very difficult to calculate the portion of inputs purchased by government sector or self stored or bring from other nation taking advantage of free border. While discussing regarding this matter with the government researcher, they replied saying that total seeds has been calculated based on total input during cultivation. Interestingly, they told that in case of wheat seeds inputs are generally improved seeds. Optimum planting date for Terai is found to be the middle of November. However, wheat showing in Terai can be delayed up to the second week of December without significant reduction in yield and wheat seed shown beyond these dates result yield reduction of 30 to 50 kg/day/ha. In previous years there was general recommendation for the seed rate of 100 kg/ha. Recent experiments have shown that additional seeds of 25 to 50 kg/ha is required under late shown and under farmers‟ broadcast system.
Manure: Manure is organic matter used as organic fertilizer in agriculture. Manures contribute to the fertility of the soil by adding organic matter and nutrients, such as
nitrogen, that are trapped by bacteria in the soil. Higher organisms then feed on the fungi and bacteria in a chain of life that comprises the soil food web (Wikipedia, 2010). Therefore, our analysis also includes manure as a prerequisite of production. We calculate total input of manure in different district from the same way I had calculated fertilizer and seeds given by Department of Agriculture, Market Research and Statistical Management Program, Harihar Bhawan, Lalitpur. Wheat requires a moderate quantity of a natural manure to fertilize the soil and ensure proper growth of the wheat. However, application of chemical depends in an epidemic year and its use as a seeds dressing compound deserving its significance. Wheat crop, in general, is less attacked by insects and pest in the field. Other Inputs: we also calculate total human labor and total bullock labor for the making our finding as good as possible. Therefore, I used data available from Department of Agriculture, Market Research and Statistical Management Program, Harihar Bhawan, Lalitpur. The book calculates average cost of production and 52
marketing margin per hectare for human labor and bullock labor. From this basis I calculated total use of human labor and total bullock labor for production of wheat from cultivation to harvest.
Price of Agricultural Inputs: we also calculate price of agricultural inputs to see the change in per unit production cost between the year 1992/93 and 2009/10 which helps us to analyze the tendency and perception of peasant for wheat cultivation. In regard of price too we use data available from Department of Agriculture, Market Research and Statistical Management Program, Harihar Bhawan, Lalitpur.
3.3. Expected Sign of Variables This research was set some expected sign which plays the major role for research finding. The table below shows the expected sigh for wheat production in, Terai, Nepal of our analysis.
Table 3.1: The Expected Sign for Wheat Production in Terai, Nepal VARIABLES
Expected Sign
TEMPERATURE
±
TEMPERATURESQ
-
PRECIPITATION
±
PRECIPITATIONSQ
-
TEMPERATURExPRECIPITATION
-
SEEDSCOST
+
HUMANLABORCOST
+
BULLOCKCOST
+
FERTILIZERCOST
+
MANURECOST
+
3.2.3. Method of Analysis Most studies on impact of climate change on agriculture employ the Ricardian analysis (Mendelsohn et al., 1994) while traditional studies have used the production function approach (for example Rosenzweig & Iglesias, 1994). The Ricardian approach is based on the observation by David Ricardo (1772–1823) that land rents 53
reflect the net productivity of farmland and it examines the impact of climate and other variables on land values and farm revenues. This approach has been found attractive because it corrects the bias in the production function approach by using economic data on the value of land. By directly measuring farm prices or revenues, the Ricardian approach accounts for the direct effects of climate on yield of different crops as well as the indirect substitution of different inputs, the introduction of different activities and other potential adaptations to different climates (Mendelsohn et al., 1994). It is also attractive because it includes not only the direct effect of climate on productivity but also the adaptation response by farmers to local climate. To measure the impact of climate change on wheat production in Nepal, we use the Mendelsohn & Dinar approach (Mendelsohn & Dinar, 2003).
The Ricardian model is based on a set of well-behaved production functions of the form: (
)
( )
Where, Qi is quantity produced of good i, Kij is a vector of production inputs j used to produce Qi and E defines a vector of exogenous environmental factors such as temperature, precipitation, and soil, characterizing production sites.
Given a set of factor prices wj , E and Q, cost minimization gives the cost function: (
)
( )
Where Ci is the cost of production of good i and W ( w1,w2…wn) is the vector of factor prices and E is the cost of environmental inputs. Using the cost function Ci at given market prices, profit maximization by farmers on a given site can be specified as: (
)
( )
Where PL is annual cost or rent of land at that site, such that under perfect competition all profits in excess of normal returns to all factors (rents) are driven to zero
54
(
)
( )
If the production of good i is the best use of the land given E, the observed market rent on the land will be equal to the annual net profits from the production of the good. Solving for PL from the above equation gives land rent per hectare to be equal to net revenue per hectare: (
)
( )
The present value of the stream of current and future revenues gives the land value VL:
∫
∫
(
(
))
( )
The analyzed issue is the impact of exogenous changes in environmental variables on net economic welfare (ΔW). The net economic welfare is the change in welfare induced or caused by changing environment from a given state to another. Consider an environmental change from the environmental state A to B, which causes environmental inputs to change from EA to EB. The change in annual welfare from this environmental change is given by: (
)
(
)
∫
(
(
))
∫
(
(
))
If market prices do not change as a result of the change in E, then the above equation reduces to:
55
(
)
(
) ∑
∑
)
(
)
)
(
Substituting for (
(
(
( )
) from equation (5)
)
∑(
)
( )
Where PLA and LA are at EA and PLB and LB are at EB The present value of the welfare change is thus: ∫
∑(
)
( )
The Ricardian model takes either (8) or (9) depending on whether data are available on annual net revenues or capitalized net revenues (land values V L). The model in (8) was employed for this research, as data on land prices for the selected samples were not available. This is the same approach followed by Sanghi et al., 1998 and Kumar & Parikh, 1998 in the India context.
The empirical strategy of the study is to estimate a functional relationship between land value, or net revenue, and climate variables using panel data while controlling for variables that could cause variability in the dependent variable. We could then use the estimated functional relationship to access the climate change impact.
Scholars have used a variant of the Recardian approach due to the non-existence of well functioning land markets in the developing countries (Dinar et al., 1998). In 56
place of land values, in the earlier Indian study scholar have used farm level net revenue welfare indication while they have accessed the value of change in the environment/climate through a change in farm level net revenue. They control for variability in the dependent variable caused by factors other than climate through: soil characters, the level of technology penetration and the extent of development. Differences across cross sectional units in physical characteristics such as the extent of the day length could also contribute to variability in the firm level net revenue (Kavikumar, 2009).
It is possible that some of these control variables are endogenous in nature and hence do not qualify as exogenous variables. However, we have included them as exogenous variables in line with the existing literature on climate change impacts (Kumar and Parikh, 2001b; Sanghi and Mendelsohn, 2008) in order to facilitate comparability of results.
We thus specify the Recardian model as follows: ( (
)
)
Where, Production represents wheat production per hectare in Terai and T and P represents temperature and precipitation respectively. The control variables include the area under high-yielding variety seeds, extent of mechanization (capture through number of labor and bullocks). The most important part of this thesis is, we include the cost – output as well as input – in the model. But we are only able to costing the variables such as seeds, human labor, bullock, fertilizer and manure. So far, no evidence exists from previous studies about the influence of input costs in case of Nepalese context. Therefore, we do see their cross-sectional variation at our research.
We measure the dependent variable (namely, wheat yield per hectare) in equation 10 and some of the explanatory variables (such as seeds, bullocks, manure and so on) for every single year of the entire time period. The annual data for each district are available for a continuous period of time; we have used the rolling average of 18 years weather data as „climate‟ for each year. This would ensure that the farmer in 57
each year responds to the climate that is experiances. We work under the assumption (followed by Kavikumar (2009) and Sanghi and Mendelshon (2008) which states climate has remained stable over their study period) that the climate has not change significantly over the study period and that the average weather of the 18 year period is highly correlated.
Given the scope for the presence of unobserved variables that could confound with climate variables, it is possible to employ the district fixed effect specification for efficient estimation. Such a specification would knock out the climate coefficients which are invariant over time. Deschences and Greenstone (2007) in a recent study on US agriculture have used country fixed effects specification and have access the value of weather shocks to the farmer as against the climate change impacts. Similarly, K. S. Kavikumar (2009) in his study on Indian Agriculture has use State fixed effect for same purpose. Therefore, The present study with its focus on climate change attempts to address this issue by including district fixed- effects. Because, we capture the year fixed effects after the Hausman test rejected the null hypothesis, implying that the random effect model produces baised estimates see annex E. Further, since the use of analysis (i.e. Districts) differ significantly in size and agricultural activities, the measurement errors might also substantially differ across districts. Hence, we weigh the data for each unit of analysis by the total area under the wheat in order to adjust for heteroscedasticity.
3.3. Data Analysis Process For data analysis, estimation of OLS; STATA 10 and MS-EXCEL are used. The calculation of different statistical tests such as autocorrelation, cross-sectional dependence, heteroscedasticity and others as per its requirements was done through STATA.
58
CHAPTER IV CURRENT STATUS OF WORLD GRAIN FOOD PRODUCTION 4.1 Introduction In the year 1950-1951, the global population was nearly 2.5 billion and then there has been a gradual increase in it. At the end of the 20th century the global population had touched a figure of 6.0 billion marks, meaning a phenomenal rise of about 3.5 billion in just half a century (Iqbal, 2010). By 2010, the global population reached 6.8 billion. On the other side of it, total global output production of food grains has witnessed an overall rise of more than 3 times. In 1950/51, it was just 631 million tons and by the year 2007/08, it has touched a figure of 2,075 million tones. This means between 1950/51 and 2007/08 the global population have recorded a rise of more than 2.2 times, whereas global output of food grains has registered an increase of more than 3.3 times. During this period, the per capita consumption of food grains has witnessed a rise of just 1.2 times (Iqbal, 2010). By 2009/10, the global population has recorded a rise of more than 2.7 times, whereas global output of food grains has registered an increase of more than 3.5 times. During this period, the per capita consumption of food grains has witnessed a rise of just 1.3 times. For the detail please look at the Annex A. The table below shows the total world average cereal production with average area and average yield. Table 4.1: World Wheat Grain Situation 2060 - 2010 Decade
Average Average Average Average Average Area Production Annual Yield Consumption (MT) (MT) Increase (T/ha) (MT) (%)
1960 – 1970 1970 – 1980 1980 – 1990 1990 – 2000 2000 – 2010
653 693 709 689 676
926 1260 1568 1794 2026
Average Surplus or Deficit (MT) 2.4 1.42 918 8 2.0 1.82 1245 15 2.5 2.21 1551 17 0.9 2.60 1772 22 1.5 2.99 2031 -5 Source: US Department of Agriculture, 2011
The table above shows the trend of wheat grain situation during the year 2060 – 2010. The average area for food grain production in 2000s have seen less than the 1980s but average production is higher in 2000s in all cases, it may be the reason of modern technology, high yield seeds, irrigation and proper use of fertilizer. However, the average annual increase in production of grain has decrease continuously from 1980s.
59
In case of production and consumption, there were always sufficient food grain production in comparison to its consumption accept 2000s. The main reason of it was high growth of population. Various figures have been used to describe the total and current situation of world food grain production. The figure given below shows the trend of world grain production, 1960 – 2010. Figure 4.1: World Grain Production 1960 – 2010 2,500
2,000
Million Tons
1,500
1,000
500
0 1960
1970
1980
1990
2000
2010
2020
Source: UN population division and FAO statistics, 2011 The figure shows that despite some dificiency, the food grain production has in increasing trend. But the figure can be explained with two parts early and late 1980s. In early 1980s the food grain production seems smooth and not much fluctuated, but later part the ratio of fluctuation is high. Figure 4.2: World Grain Area Harvested 1960 – 2010 750 700
Million Hectares
650 600 550 500 450 400 1960
1970
1980
1990
2000
2010
2020
Source: UN population division and FAO statistics, 2011 60
In case of grain harvested area too, we can analyse with pre and post 1980s. Up to 1975 the havested area increases in an increasing way, reach at peak in 1980, thenafter, grain harvested area decreases. Figure 4.3: World Grain Yield 1960 – 2010 3.5 3.0
Tons per Hectare
2.5 2.0 1.5 1.0 0.5 0.0 1960
1970
1980
1990
2000
2010
2020
Source: UN population division and FAO statistics, 2011
However, the production area has decreased after 1980s in comparision to previous decades, there has been no change in total grain yield. This figure shows there are significantly positive changes in food grain yields eventhough the area of grain production has decreased in a significant way. Figure 4.4: World Grain Production and Consumption 1960 – 2010 2,500 2,000
Million Tons
1,500 1,000 500 Production
0 1960
1970
1980
1990
2000
Consumption
2010
2020
Source: UN population division and FAO statistics, 2011
61
The ratio between food grain production and consumptions also shows that before 1980s the production dominated consumption but later on there is some fluctuation in consumption and production. Sometime consumption is higher than production. 4.2 Cereal Production Trends In 1951 when the entire human population numbered only 2.5 billion, the annual figures for per-capita output are surprisingly variable and reflect volatility in the world‟s harvest. Now, it is clear that world output has increased along with population growth (Iqbal, 2010). There have been two periods of falling per-capita cereal production. The first happened around 1960 and mainly reflects the agricultural losses associated with Mao‟s disastrous “Great Leap Forward” in China. The second period has been since the early 1980s (Dyson, 1999). The fact that world population growth has been outpacing cereal production since 1984 readily attracts attention, but interpreted without any qualification, it is seriously misleading for two reasons. First, it hides the fact that much of the recent decline in world cereal production has occurred in relatively well-fed regions. Second, it does not account for the fact that the regional composition of humanity is changing. In particular, most demographic growth is happening in parts of the world with low levels of per-capita cereal consumption and, other things being equal, this fact tends to weight downward the average level of world per-capita cereal consumption and hence production (Dyson, 1999). Global wheat supplies for 2010/11 are projected 2 percent higher with larger year-toyear beginning stocks more than offsetting lower expected production. Global 2010/11 wheat production is projected at 672.2 million tons, down 1 percent from 2009/10 and the third largest production on record if realized. Larger projected production in EU-27, South America, and the Middle East is more than offset by expected declines in FSU-12, North Africa, South Asia, China, Canada, and Australia. Global coarse grain production for 2010/11 is projected at a record 1,129.8 million tons, up 2 percent from 2009/10. Most of the 27.4-million-ton increase in coarse grains production results from higher projected foreign corn production, up 19.9 million tons from 2009/10. Higher expected foreign corn area and rising yields combine with higher U.S. area to boost global corn production to a record 835.0
62
million tons, up 26.5 million from 2009/10. Corn production is projected higher yearto-year for China, Mexico, India, Russia, EU-27, Ukraine, and Canada (FAO, 2011). 4.2.1
Regional Cereal Production Trends
Sub-Saharan Africa has poorly performed in food production. Widespread political instability, neglect of agriculture by governments, many health problems (including the AIDS epidemic), and rapid population growth are main causes for it. Around 1995 this region‟s average per-capita cereal output was only about 146 kg, which is a low figure, even allowing for the fact that cereals are not grown in much of middle and West Africa, and average levels of per-capita cereal consumption were only slightly higher because of cereal imports and aid (Dyson, 1999). The Middle East here combines North Africa and West Asia. This region has experienced a significant long-run decline in its per-capita output, which is not helped by its rapid demographic growth. The annual volatility of the harvest in this waterscarce area is also striking. Average levels of per-capita cereal production during the 1990s have fluctuated between 250 and 270 kg, with no particular trend apparent since the early 1980s. However, the Middle East imports large quantities of cereals, mostly from North America. Around 1990 these imports accounted for almost a third of the region‟s entire cereal consumption, and they raised the average level of percapita consumption to about 386 kg. These imports (which often are used to feed livestock) can be seen as an oblique way of importing water (Dyson, 1999). South Asia mainly comprises the populous countries of the Indian subcontinent (India, Pakistan, and Bangladesh). The trend is dominated by India, which contains around 70% of the region‟s people. The 5-year curve shows the effects of significant famines in the mid-1960s and early 1970s, which cost lives. But during the last two decades there has been no major food crisis, and average levels of per-capita cereal output have risen to around 225 kg in the mid-1990s. It has notice that despite the plateau of the 1990s, levels of per-capita production are still significantly higher than those of the early 1980s (Dyson, 1999). East and Southeast Asia is dominated by China, although it includes other major populations, especially Indonesia and Japan. This region‟s trend clearly reflects the agricultural output losses of China‟s calamitous “leap” around 1959–1964, when 63
perhaps 20 million-30 million died in famine. However, the subsequent trend in percapita cereal production generally has been upward. Notice the sharp acceleration after the agricultural policy reforms that were introduced in China around 1978. This acceleration also reflected the introduction of hybrid rice and, still more, large increases in the use of chemical fertilizers by Chinese farmers. This region has continued to experience a rise in average per-capita cereal output since the early 1980s, albeit at a slower rate. By the mid-1990s regional production averaged about 316 kg per head (Dyson, 1999). It is obvious that the last two regions hold the key to the decline in world per-capita output since 1984. The first is Europe, here including the countries of the former Soviet Union (FSU). The second is North America/Oceania, a hybrid region, essentially comprising the traditional major cereal exporters of Canada, Australia, and, above all, the United States. Both of these regions produce cereals in comparative abundance. In the mid-1990s the average per-capita output in Europe/FSU was about 530 kg, and the figure for North America/Oceania exceeded 1.2 tons per person. Although the U.S., Canada, and Australia together contain less than 6% of the world‟s population, they currently produce about 20% of the global cereal harvest (Dyson, 1999). Levels of per-capita cereal output in Latin America are relatively low, around 260 kg in the mid-1990s (although cereals are probably a poorer proxy for food in general here than is the case for any other developing region). The trend for Latin America is actually very similar to the North America/Oceania. In particular, per-capita cereal output declined from a peak in the early 1980s to a trough around 1990, and then there was a period of limited recovery in the 1990s. The explanation for this similarity of trend is the common influence of international market conditions, notably as they affected Argentina, which is the region‟s second biggest cereal producer (after Brazil) and the largest exporter by far. Confronted by a steadily deteriorating world price, Argentina‟s farmers had little choice but to shift large areas of land out of wheat in the 1980s (Dyson, 1999). 4.3 Climatic Change Effect Climate-change risks will have adverse impacts on food production, compounding the challenge of meeting global food demand. Consequently, food import dependency is 64
projected to raise in many regions of the developing world (IPCC 2007).With the increased risk of droughts and floods due to rising temperatures, crop-yield losses are imminent. In more than 40 developing countries - mainly in Sub-Saharan Africa cereal yields are expected to decline, with mean losses of about 15 percent by 2080 (Fischer et al. 2005). Other estimates suggest that although the aggregate impact on cereal production between 1990 and 2080 might be small - a decrease in production of less than 1 percent - large reductions of up to 22 percent are likely in South Asia. In contrast, developed countries and Latin America are expected to experience absolute gains. Impacts on the production of cereals also differ by crop type. Projections show that land suitable for wheat production may almost disappear in Africa. Nonetheless, global land use due to climate change is estimated to increase minimally by less than 1 percent. In many parts of the developing world, especially in Africa, an expansion of arid lands of up to 8 percent may be anticipated by 2080 (Fischer et al. 2005).
World agricultural GDP is projected to decrease by 16 percent by 2020 due to global warming. Again, the impact on developing countries will be much more severe than on developed countries. Output in developing countries is projected to decline by 20 percent, while output in industrial countries is projected to decline by 6 percent (Cline 2007). Carbon fertilization could limit the severity of climate-change effects to only 3 percent. However, technological change is not expected to be able to alleviate output losses and increase yields to a rate that would keep up with growing food demand (Cline 2007). Agricultural prices will thus also be affected by climate variability and change.Temperature increases of more than 3ºC may cause prices to increase by up to 40 percent (Easterling et al. 2007). The table shows the Expected Impact of climate change on global cereal production by 2080 based on 1990. Table 4.2: Expected Impacts of Climate Change on Global Cereal Production Region
1990–2080 (% change)
World
–0.6 to –0.9
Developed countries
2.7 to 9.0
Developing countries
–3.3 to –7.2
Southeast Asia
–2.5 to –7.8 –18.2 to –22.1
South Asia
–3.9 to –7.5
Sub-Saharan Africa 65
Latin America
5.2 to 12.5
Source: International Food Policy Research Institute, 2007
4.4. Future Expectation There is a true saying that in years to come, the output of food grains would not be able to sustain the ever rising population. This is a fact and really does require a serious thinking and the need of exploring the options to deal with it. The question does arise: how much can the earth yield? There is much truth to this fact/question. There is a limit to cultivate land and this presently accounts for 11% of the total land space of the earth, that is, 13.2 billion hectares as the remaining of the land is shared by forests, settlements, grasslands. Similarly, there is also a limit to water, a sine-quonon for agriculture development world over (Iqbal, 2010).
Now, what is most important is to estimate the need of the population in respect of food grains. This is because there has been alarming disparities in terms of consumption of cereals worldwide. An American consumes around 1,046 kg which is the highest in the world, whereas global per capita availability of food grains during 2009-2010 is just 333 kg (Iqbal, 2010). This indicates that global per capita availability is more than 3 times below the per capita availability of food grains in the US. If this is a requirement then the total need of the global output of food grains would be around 7 billion tons, which means three and half times of existing output. The result would be that the planet would become a wasteland by the end of 2050 (Varma, 2008).
4.5. Poverty and the Food and Nutrition Situation Climate change will create new food insecurities in coming decades. Low-income countries with limited adaptive capacities to climate variability and change are faced with significant threats to food security. In many African countries, for example, agricultural production as well as access to food will be negatively affected, thereby increasing food insecurity and malnutrition (Easterling et al., 2007).When taking into account the effects of climate change, the number of undernourished people in SubSaharan Africa may triple between 1990 and 2080 under these assumptions (Joachim,
66
2007). The following figure illustrates the number of Undernourished people in the world from 1969 to 2010.
Figure 4.5: Number of Undernourished People in the World, 1969 – 2010 1,050
2009
1,000 950
2008 900
Millions
850
2010
2000-2002 1969-1971
2005-2007
1990-
1979-1981
800
1995-1997 750 700 650
Source: UN population division and FAO statistics, 2011
The figure shows there was good progress in nourishment of the people in the world till 1997, thenafter, the situation perverted and reached to 1020 millions unnourished people by year 2009 and dropped down to around 2008 level by 925 millions in 2010. This figure indicates that as per the population increase the food shortage has also increased. The table below explains undernourishment in the world and in the selected groups and regions.
Table 4.3: Undernourishment in the World and in the Selected Groups and Regions Group or Region
Undernourished People in Share of Population 2010 (Millions) (Percent) Developed Countries 19 2 Developing Countries 907 16 Asia and the Pacific 578 16 Latin America and the Caribbean 53 9 Near East and North Africa 37 8 Sub-Saharan Africa 239 30 World 925 13 Source: International Food Policy Research Institute, 2007 67
From the table we can see, Sub-Saharan Africa (30%) is suffering a lot from malnutrition and follwed by Asia (16%). The both cases are above the average of world share of population. Developed countries have better off situation and even Near East and North Africa has better of position comparing Asia. Out of worlds total 925 Millions undernourished people 907 millions people belongs to Developing countries and within developing country too, 817 millions people are Asian and SubSaharan African. So, it is not worse to say people form Sub-Saharan Africa and Asia has suffered a lot with malnutrition and hunger.
International Food Policy Research Institute, (2007) has explain with its report that accept Africa there are significant possible to decrease in number of undernourished people by 2080. The table below shows the situation of the undernourished people in 2080 in comparision to 1990.
Table 4.4: Expected Number of Undernourished (in millions), incorporating the effects of climate change Region 1990 2020 2050 2080 2080/1990 Ratio Developing countries 885 772 579 554 0.6 Asia, Developing 659 390 123 73 0.1 Sub-Saharan Africa 138 273 359 410 3.0 Latin America 54 53 40 23 0.4 Middle East & North Africa 33 55 56 48 1.5 Source: International Food Policy Research Institute, 2007
The table shows that eventhough Asia and other developing countries progress a lot in nourishment African and Middle East countries cannot progress themselves. In SubSaharan Africa the condition is more velnerable. Table explains that in Sub-Saharan Africa the undernourishment ratio was 3 times higher than the 1990s stage and for Middle East and North Africa it is 1.5 times. It is expedite to think about Africa.
4.6. How to Feed the World in 2050? According to FAO in its document “How to Feed the World in 2050”, By 2050 the world‟s population will reach 9.1 billion, 34 percent higher than today. This means that there will be an addition of 3 billion of people to this earth and it would be very difficult for the governments all over the world to feed everyone on the landscape of 68
the earth (Iqbal, 2010). Nearly all of this population increase will occur in developing countries. Urbanization will continue at an accelerated pace, and about 70 percent of the world‟s population will be urban (compared to 49 percent today). Income levels will be many multiples of what they are now. In order to feed this larger, more urban and richer population, food production (net of food used for biofuels) must increase by 70 percent. Annual cereal production will need to rise to about 3 billion tonnes from 2.1 billion today and annual meat production will need to rise by over 200 million tonnes to reach 470 million tonnes.
In developing countries, 80 percent of the necessary production increases would come from increases in yields and cropping intensity and only 20 percent from expansion of arable land. But the fact is that globally the rate of growth in yields of the major cereal crops has been steadily declining, it dropped from 3.2 percent per year in 1960 to 1.5 percent in 2000. The challenge for technology is to reverse this decline, since a continuous linear increase in yields at a global level following the pattern established over the past five decades will not be sufficient to meet food needs. Although investment in agricultural research and development continues to be one of the most productive investments, with rates of return between 30 and 75 percent, it has been neglected in most low income countries. Currently, agricultural research and development in developing countries is dominated by the public sector, so that initially additional investment will have to come from government budgets. Increasing private sector investment will require addressing issues of intellectual property rights while ensuring that a balance is struck so that access of smallholder farmers to new technologies is not reduced.
Hunger can persist in the midst of adequate aggregate supplies because of lacking income opportunities for the poor and the absence of effective social safety nets. Experience of countries that have succeeded in reducing hunger and malnutrition shows that economic growth does not automatically ensure success, the source of growth matters too. Growth originating in agriculture, in particular the small holder sector, it is at least twice as effective in benefiting the poorest as growth from nonagriculture sectors. This is not surprising since 75 percent of the poor in developing countries live in rural areas and their incomes are directly or indirectly linked to agriculture. The fight against hunger also requires targeted and deliberate action in the 69
form of comprehensive social services, including food assistance, health and sanitation, as well as education and training; with a special focus on the most vulnerable.
Many countries will continue depending on international trade to ensure their food security. It is estimated that by 2050 developing countries‟ net imports of cereals will more than double from 135 million metric tonnes in 2008/09 to 300 million in 2050. That is why there is a need to move towards a global trading system that is fair and competitive; and that contributes to a dependable market for food. Reform of farm support policies in OECD countries is a welcome step which has led to a decline in the aggregate trade distortion coefficient from 0.96 in 1986 to 0.74 in 2007. However, there is clearly still room for improvement. There is also a need to provide support and greater market access to developing country farmers so that they can compete on a more equal footing. Countries also need to consider joint measures to be better prepared for future shocks to the global system, through coordinated action in case of food crises, reform of trade rules, and joint finance to assist people affected by a new price spike or localized disasters.
Climate change and increased biofuel production represent major risks for long-term food security. Although countries in the Southern hemisphere are not the main originators of climate change, they may suffer the greatest share of damage in the form of declining yields and greater frequency of extreme weather events. Studies estimate that the aggregate negative impact of climate change on African agricultural output up to 2080 – 2100 could be between 15 and 30 percent. Agriculture will have to adapt to climate change, but it can also help mitigate the effects of climate change, and useful synergies exist between adaptation and mitigation. Biofuel production based on agricultural commodities increased more than threefold from 2000 to 2008,. In 2007-08 total usage of coarse grains for the production of ethanol reached 110 million tonnes, about 10 percent of global production. Increased use of food crops for biofuel production could have serious implications for food security. A recent study estimates that continued rapid expansion of biofuel production up to 2050 would lead to the number of undernourished pre-school children in Africa and South Asia being 3 and 1.7 million higher than would have been otherwise the case. Therefore, policies
70
promoting the use of food-based biofuels need to be reconsidered with the aim of reducing the competition between food and fuel for scarce resources.
The world has the resources and technology to eradicate hunger and ensure long-term food security for all, in spite of many challenges and risks. It needs to mobilize political will and build the necessary institutions to ensure that key decisions on investment and policies to eradicate hunger are taken and implemented effectively. The time to act is now.
4.7. Conclusion On one side, it is highly unlikely that there will be any wonder breakthrough that will solve the problem of raising world food production because the process of raising yields and agricultural advance is extremely complex. There are significant problems of soil structure when land is cultivated year after year. On other side, there are some possibilities such as there will be new crops and improved seeds, technological knowhow, literacy regarding farming and many others extra facilities and provisions for food crops. But most of the required increase in the world‟s harvest will come from the application of procedures and knowledge that we already have to the current world harvested area. That is why, reducing food unavailability and limited access to income-generating opportunities - require expanded social - protection measures with productive social safety nets that should be tailored to country circumstances and should focus on early childhood nutrition is still questionable. Likewise, placing agricultural and food issues onto the national and international climate-change policy agendas is critical for ensuring an efficient and pro-poor response to the emerging risks. The world food situation has been improving and this trend probably will continue during the next few decades and world food output will continue to rise, although there will be a growing degree of mismatch between the expansion of food demand and the capacity to supply that demand.
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CHAPTER V CURRENT STATUS OF WHEAT PRODUCTION IN NEPAL
5.1.Introduction Wheat is one of the major cereal crops after rice and maize in Nepal. It is grown in Terai, river basins, mid-hills and high hills of Nepal in winter. It has occupied an area of 7, 31,131 ha with the total production of 15, 56,539 metric tons and the average productivity of 2.13 t/ha during 2009/10 in year round wheat season (MOAC, 2010). But unlike rice in summer, wheat is not a sole crop in winter. Winter legumes (Lentil, Chickpea, Lathyrus, and Peas), oil crops, potato and a number of winter vegetables are alternative of wheat. Farmers may prefer not to grow wheat in large areas if they harvest bumper rice crop. A poor rice crop system prompts them to grow more wheat for their food security. Besides that, wheat production and productivity are increasing these days because of appropriate wheat technology developed by NARC scientists. In a span of 35 years (1965 – 2000), Nepal has made a speclacture progress in wheat production. The area under wheat has been increased by more than 6 folds, the production by 10 folds, and the productivity by two folds in this period (NARC, CIMMYT, 2001). This research concludes that there are four main reasons for this: 1. Nepal is suitable place for wheat cultivation as it is above the tropic of cancer. The soil is neither too acidic nor too alkaline. The climate on the whole is favourable. 2. High yielding varieties of wheat having wide adaptations started replacing the local land races. 3. Improved in overall wheat crop management. 4. CIMMYT‟s support in providing germplasm, training and higher studies.
Comparing 2000 with 2010, the area under wheat has been increased by 1.10 fold, the production by 1.32 fold, and the productivity by 1.19 fold (MOAC, 2010). It signifies that the wheat production has favourable environment. 5.2.History of Wheat Production Wheat has been growing since time immemorial particularly in Far and Mid Western hills of Nepal. Wheat is the third most important crop after rice and maize in Nepal. 72
During mid 1960s the yield potential of dwarf high yielding varieties initiated a scope for raising wheat production in the country. Several exotic varieties were obtained through CIMMYT and USAID (NARC, 1997). To organize the research and development works on wheat as a commodity crop, the National Wheat Development Program (NWDP) was established in 1972 and with the slight modification on its nomenculture; National Wheat Research Program (NWRP) existed in 1990 under the Nepal Agricultural Research Council (NARC). Since establishment of NARC, there has been great achievement brought out by the consolidated efforts of wheat researchers, extension workers and farmers. So far there are 35 improved wheat cultivars and 90% of the wheat area is covered by modern wheat cultivars in Nepal (Bhatta et al., 2000).
During mid 1996s the yield potential of dwarf high yielding varieties initiated a scope for raising wheat production in the country. Several exotic varieties were obtained through CIMMYT and USAID and tested under Nepalese environments. Varieties such as Lerma Rojo 64, Pitic 62, Kalyansona and RR21 were identified as fertilizer responsive and high yielding and at the same time were highly preferred by the farmers (Joshi, 1997). By the end of 2007, it has been 35 years of the establishment of NWRP. During these years the NWRP has developed 28 high yielding wheat varieties with desirable traits and package of practices for different production environments. As a consequence, there is a remarkable increase in wheat production in the country (NARC, 2007). The table below shows the released and register crop varieties for Terai Nepal from 1960 – 2010. Table 5.1: Improved bread wheat varieties released since 1960 – 2010 SN.
Variety
Pedigree
Origin
1 2 3 4 5 6 7 8 9 10 11 12 13
NP835 NP 852 NP 884 Kalyansona(5) RR 21 (6) S 331 (7) NL 30 (8) HD 1982(9) UP 262 (10) Lumbini (11) Triveni (12) Vinayak (13) Siddhartha(14)
NP760/RN KF/2*NP761 KC6042/GUL//PLT/3/K58/N/4/NP755 PJ"S"/GABO55 II54-388/AN/3/YT54/N10B//LR64 LR64"S"/HUMANTALA(R) HD832-5-5-0Y/BB E5557/HD845 S308/BAJIO66 E4871/PJ62 HD1963/HD1931 LC55 HD2092/HD1962//E4870/3/K65
India India India Mexico Mexico Mexico India India India India India India India
73
Year of release 1962 1962 NA 1968 1971 1971 1975 1975 1978 1981 1982 1983 1983
Area of adaptation Plains Plains Plains Plains Hills & Plains Hills & Plains Western Plains Western Plains Plains Plains Plains Plains Plains
14 15 16 17 18 19 20 21 22 23 24 25 26
Vaskar (15) Nepal 297 (16) Nepal 251 (17) BL 1022 (20) Bhrikuti (22) BL 1135 (23) Achyut (25) Rohini (26) BL 1473 (29) Gautam (30) Aditya (32) NL 971 (33) BL 3063 (34)
TZPP/PL//7C Mexico 1983 Midwestern Plain HD2137/HD2186//HD2160 India 1985 Plains WH147/HD2160//WH147 India 1988 Plains PVN/BUC Nepal 1991 Western Plain CMT/COC75/3/PLO//FURY/ANA75 Mexico 1994 Plains QTZ/TAN Nepal 1994 Plains CPAN168/HD2204 India 1997 Plains PRL/TONI//CHIL Nepal 1997 Plains NL352/NL297 Nepal 1999 Plains SIDDHARTHA/NING8319/NL297 Nepal 2004 Plains GS348/NL746//NL748 Nepal 2009 Plains MRNG/BUC//BLO/PVN/3/PJ81 Mexico 2009 Plains NL748/NL837 Nepal 2010 Plains Source: Nepal Agricultural Research Council (NARC, 2011)
5.3.Diseases and Pests of Wheat Wheat is a widely adapted crop. It is grown from temperate, irrigated to dry and highrain-fall areas and from warm, humid to dry, cold environments. Undoubtedly, this wide adaptation has been possible due to the complex nature of the plant‟s genome, which provides great plasticity to the crop. Wheat is a C3 plant and as such it thrives in cool environments.
In Nepal, the yellow rust disease was first recorded in 1964 and it was also minimized when RR21, a rust resistent cultivar was popularly grown in both Terai and Hills (Karki et al. 2004). Suddenly, in mid-1980, the verulance of the rust pathology began to change and epidemic of rust was encountered due to prevalence of 7E150 race and RR21 became susceptible to existing race (Sharma et al. 1995). Then continuous considerable research were carried out with an objective to accomplished the management of newly emerged yellow rust race and some high yielding and resistence genotypes were developed (Baidya, 2011).
Again in mid 1990, the Yr9 virulent race that evolved in the Eastern African highland during 1980s and migrated to South Asia from North Africa to Middle East, Central and West Asia causing epedimics disease of various cultivars of wheat possessing resistance gene Yr9 (Singh and Duveiller, 2004). The genotype with Yr9 gene again showed suspectible by yellow rust disease due to appearance of new epidemic race 46S119 in various districts during 1996/97 (Sharma, 2001).
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Currently several resistant cultivars released for wheat growing area having Yr27 gene located on chromosome (Sharma and Duveiller, 2004). Recently, apprearance of new race 71E32, resulted Yr27 containing genotypes and CIMMYT germplasms became ineffective for this race (NARC, 2008 & 2009). However, the Yr9 gene showed resistant to 71E32 on which Yr27 is susceptible. Researcher argued that the year round epidemic of new race in the wheat growing area might be due to spread of fungal spores in the common epidemiological zone (Baidya, 2011).
As like yellow rust, Leaf rust is also an economically important disease which occures on major wheat area of river basin ( F = 0.0000 -----------------------------------------------------------------------------productionha | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------temper | 370.5718 123.6861 3.00 0.003 127.2591 613.8846 tempsq | -8.516073 3.01285 -2.83 0.005 -14.44289 -2.589259 precepi | -.2827503 16.49574 -0.02 0.986 -32.73283 32.16733 Precisq | -.0066871 .0743101 -0.09 0.928 -.1528684 .1394943 TempexPrecip | -.175844 .8627036 -0.20 0.839 -1.872936 1.521248 c_humanlabor | .1553278 .0174684 8.89 0.000 .1209643 .1896913 c_bullock | -.1592503 .0295722 -5.39 0.000 -.217424 -.1010765 c_seeds | .0389322 .0683991 0.57 0.570 -.095621 .1734855 c_fertilizer | -.0786828 .0576667 -1.36 0.173 -.1921235 .034758 c_manure | .440012 .1212297 3.63 0.000 .2015315 .6784925 _cons | -2939.774 1266.413 -2.32 0.021 -5431.034 -448.5146 -------------+---------------------------------------------------------------sigma_u | 190.43707 sigma_e | 259.3164 rho | .35036067 (fraction of variance due to u_i) -----------------------------------------------------------------------------F test that all u_i=0: F(19, 330) = 6.68 Prob > F = 0.0000
Testing for Cross-Sectional Dependency: Testing for cross-sectional dependence/contemporaneous correlation: Using Pasaran CD test As mentioned in the previous slide, cross-sectional dependence is more of an issue in macro panels with long time series (over 20-30 years) than in micro panels. Pasaran CD (cross-sectional dependence) test is used to test whether the residuals are correlated across entities*. Cross-sectional dependence can lead to bias in tests results (also called contemporaneous correlation). The null hypothesis is that residuals are not correlated. Detection Command: xtcsd, pesaran abs Pesaran's test of cross sectional independence = 14.417, Pr = 0.0000 Average absolute value of the off-diagonal elements = 0.322 (Cross Sectional Dependence)
Correction Command: xtscc productionha temper tempsq precepi Precisq TempexPrecip c_humanlabor c_bullock c_seeds c_fertilizer c_manure, fe Regression with Driscoll-Kraay standard errors
Number of obs
=
360
Method: Fixed-effects regression Group variable (i): district maximum lag: 2
Number of groups = 20 F( 10, 17) = 273.78 Prob > F = 0.0000 within R-squared = 0.6354 -----------------------------------------------------------------------------| Drisc/Kraay productionha | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------temper | 370.5718 131.082 2.83 0.012 94.0131 647.1306 tempsq | -8.516073 3.144897 -2.71 0.015 -15.15123 -1.88092 precepi | -.2827503 13.43059 -0.02 0.983 -28.61882 28.05332 Precisq | -.0066871 .109989 -0.06 0.952 -.2387436 .2253694 TempexPrecip | -.175844 .7792932 -0.23 0.824 -1.820009 1.468321 c_humanlabor | .1553278 .0433073 3.59 0.002 .0639574 .2466981 c_bullock | -.1592503 .0556366 -2.86 0.011 -.2766332 -.0418673 c_seeds | .0389322 .1208821 0.32 0.751 -.2161068 .2939712 c_fertilizer | -.0786828 .0690114 -1.14 0.270 -.2242842 .0669187 c_manure | .440012 .165464 2.66 0.017 .0909135 .7891105 _cons | -2939.774 1383.751 -2.12 0.049 -5859.234 -20.31535
Testing for Serial Correlation: Serial correlation tests apply to macro panels with long time series (over 20-30 years). Not a problem in micro panels (with very few years). Serial correlation causes the standard errors of the coefficients to be smaller than they actually are and higher R-square. A Lagram-Multiplier test for serial correlation is available using the command xtserial. Detection Command: xtserial productionha temper tempsq precepi Precisq TempexPrecip c_humanlabor c_bullock c_seeds c_fertilizer c_manure Wooldridge test for autocorrelation in panel data H0: no first order autocorrelation F( 1, 19) = 26.552 Prob > F = 0.0001 (Presence of Serial Correlation)
Correction Command: prais productionha temper tempsq precepi Precisq TempexPrecip c_humanlabor c_bullock c_seeds c_fertilizer c_manure Number of gaps in sample: 19 (gap count includes panel changes) (note: computations for rho restarted at each gap) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration
0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12:
rho = 0.0000 rho = 0.6110 rho = 0.7349 rho = 0.7739 rho = 0.7875 rho = 0.7923 rho = 0.7939 rho = 0.7945 rho = 0.7947 rho = 0.7948 rho = 0.7948 rho = 0.7948 rho = 0.7948
Iteration 13: Iteration 14:
rho = 0.7948 rho = 0.7948
Prais-Winsten AR(1) regression -- iterated estimates Source | SS df MS -------------+-----------------------------Model | 1015217.54 10 101521.754 Residual | 17200523.1 349 49285.1665 -------------+-----------------------------Total | 18215740.7 359 50740.2247
Number of obs F( 10, 349) Prob > F R-squared Adj R-squared Root MSE
= = = = = =
360 2.06 0.0270 0.0557 0.0287 222
-----------------------------------------------------------------------------productionha | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------temper | 83.80982 95.54966 0.88 0.381 -104.1158 271.7354 tempsq | -1.948339 2.347601 -0.83 0.407 -6.565564 2.668885 precepi | -5.946478 10.91139 -0.54 0.586 -27.40683 15.51387 Precisq | -.0740919 .0491461 -1.51 0.133 -.1707518 .0225679 TempexPrecip | .4216564 .5694148 0.74 0.459 -.6982598 1.541573 c_humanlabor | .0926803 .0197447 4.69 0.000 .0538467 .1315139 c_bullock | -.0821238 .0261116 -3.15 0.002 -.1334798 -.0307679 c_seeds | -.1144253 .0721646 -1.59 0.114 -.2563575 .0275068 c_fertilizer | -.0394338 .0396908 -0.99 0.321 -.1174971 .0386295 c_manure | .363604 .0886931 4.10 0.000 .1891637 .5380442 _cons | 647.1684 994.9382 0.65 0.516 -1309.661 2603.998 -------------+---------------------------------------------------------------rho | .7948309 -----------------------------------------------------------------------------Durbin-Watson statistic (original) 0.778271 Durbin-Watson statistic (transformed) 1.963736
Testing for Heteroskedasticity: A test for heteroskedasticiy is avalable for the fixed- effects model using the command xttest3. The null is homoskedasticity (or constant variance). Detection Command: xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (20) = Prob>chi2 =
158.67 0.0000 (Presence of heteroskedasticity)
Correction NOTE: Use the option ‘robust’ to control for heteroskedasticiy (in both fixed and random effects). Command: xtreg productionha temper tempsq precepi Precisq TempexPrecip c_humanlabor c_bullock c_seeds c_fertilizer c_manure, fe r Fixed-effects (within) regression Group variable: district
Number of obs Number of groups
= =
360 20
R-sq:
within = 0.6354 between = 0.0262 overall = 0.5046
corr(u_i, Xb)
= -0.1419
Obs per group: min = avg = max = F(10,330) Prob > F
= =
18 18.0 18 73.84 0.0000
(Std. Err. adjusted for clustering on district) -----------------------------------------------------------------------------| Robust productionha | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------temper | 370.5718 127.9205 2.90 0.004 118.9294 622.2143 tempsq | -8.516073 3.024516 -2.82 0.005 -14.46584 -2.566309 precepi | -.2827503 13.41127 -0.02 0.983 -26.66512 26.09962 Precisq | -.0066871 .0535758 -0.12 0.901 -.1120802 .0987061 TempexPrecip | -.175844 .7062793 -0.25 0.804 -1.565222 1.213534 c_humanlabor | .1553278 .0196725 7.90 0.000 .1166284 .1940272 c_bullock | -.1592503 .0263991 -6.03 0.000 -.2111821 -.1073184 c_seeds | .0389322 .0633354 0.61 0.539 -.0856598 .1635242 c_fertilizer | -.0786828 .0520301 -1.51 0.131 -.1810353 .0236698 c_manure | .440012 .1063181 4.14 0.000 .2308653 .6491587 _cons | -2939.774 1322.308 -2.22 0.027 -5540.991 -338.5579 -------------+---------------------------------------------------------------sigma_u | 190.43707 sigma_e | 259.3164 rho | .35036067 (fraction of variance due to u_i) ------------------------------------------------------------------------------
