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Fertilizer requirements in 2015 and 2030 revisited
Fertilizer requirements in 2015 and 2030 revisited
Land and Plant Nutrition Management Service Land and Water Development Division
FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS Rome, 2004
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[email protected] [email protected]
© FAO
2004
Contents
EXECUTIVE SUMMARY
v
ACKNOWLEDGEMENTS
viii
GLOSSARY 1. INTRODUCTION
ix 1
2. CATEGORIZING COUNTRIES BY ADOPTION LEVEL Sub-Saharan Africa North Africa and the Middle East West Europe, Central Europe and FSU North America, Latin America and the Caribbean Asia Oceania Conclusion
3 7 7 8 9 9 10 10
3. PROPOSED FERTILIZER DEMAND FORECASTING METHODS Fertilizer Demand Studies Three different approaches
11 13 14
4. CONCLUSIONS AND NEXT STEPS
23
REFERENCES AND BACKGROUND READING
27
APPENDICES
39
A. OVERVIEW OF ANALYSIS OF DATA WITH SPATIAL STRUCTURE
39
B. LIST OF FERTILIZER CONSUMING COUNTRIES BY NEW CATEGORIES
41
iv
List of figures
1. 2.
Projected fertilizer use ef ficiency in selected countries for wheat yields < 3 t/ha
4
Projected fertilizer use ef ficiency for selected countries with a wheat yield > 3 t/ha
5
List of tables
1. 2.
Average fertilizer application rate and paired t-test statistics for the regional categories Summary of past fertilizer demand studies
6 12
v
Executive Summary
Fertilizer has been a key element in the growth of agricultural productivity in the last century and it will continue to be important in meeting the demand for food, feed, fibre and other crop products. The general objective of this study was to propose improved methodologies for FAO forecasting of fertilizer demand that are consistent with FAO projections of agricultural production in 2015 and 2030. These forecasts are needed for public and private planning. The specific objectives were to: i) review the literature on the adoption of fertilizer technologies worldwide; ii) categorize countries according to their position on the adoption curve; and iii) suggest up to three different methodologies. methodologies.
RECATEGORIZATION The observed pattern in fertilizer consumption suggests re-categorization of countries. For instance the changing structure of the EU, the economic growth of Mexico and its proximity to the United States, and South Africa s atypical consumption in Africa, are a few examples that testify to this. The following categories have been suggested: ʼ
1. SSA (excluding S South outh Africa and Sudan) 2. Oceania (including South Africa) 3. East Asia (al (alll East Asian countries) 4. Rest of Asia (RoA) (RoA),, (excluding East Asian coun countries). tries). 5. North America (including Mexico) 6. Latin America aand nd the Caribbean (excluding Mexico) 7. EUR (W (West est Europe, Bulgaria, Czech Republic, Hungary, Poland, and Romania) 8. Rest of Europe (RoE) Central Europe and FSU (excluding B Bulgaria, ulgaria, Czech Republic, Hungary, Hungary, Poland, and Romania) 9. Near East – all N North orth African and Middle East countries. Appendix B provides a list of all countries in each category.
vi
THREE METHODOLOGIES FOR FERTILIZER DEMAND FORECASTING Long term forecasting is at best an inexact science, which must make the best of both formal estimation methods and the informal observations of those in the fertilizer and related industries. The three formal methodologies proposed are: a) simple structural econometric models (SEM) based on modification of past fertilizer demand methodologies; b) time series modelling with Vector Autoregression (VAR); (VAR); and c) causal production economics approach models (PEA) based on economic duality theory. The current FAO FAO fertilizer demand model is a starting point for the develo development pment of the simple structural econometric model. Fertilizer use in the current period is explained by cropland, crop production, the change in crop production, the change in fertilizer use over the previous period (essentially lagged fertilizer use) and a trend variable. The yield change variable captures the effect of technical improvements in fertilizer use on fertilizer demand. The trend variable combines combin es both technology and environmental quality effects. The coef ficients of this model would be estimated on cross section time series data for each macronutrient using econometric techniques to deal with the temporal and spatial correlation. The model directly reflects the effects of cropland change, technology and environmental concern. It indirectly reflects the build up or depletion of soil fertility through the crop production variable. The suggested VAR VAR approach uses past observations of the variable in question and crop production. Past observations of other variables could be included if the historical data are available for estimation and the projections are available for the period up to 2030. The estimate does not depend on economic theory and as such, it is easy to Cropland, and environmental effects are embodied inmodel. the lagged valuestechnology of the fertilizer demand and trend crop production variables. Depletion or build up of soil fertility can be analysed by comparing the estimated coef ficient of crop production to crop removal parameters. Researchers have found that VAR models produce more accurate forecasts than other econometric estimates. The VAR can be estimated with a spatial error structure if diagnostic tests show that this is needed. The forecast is generated by repeatedly estimating one period ahead out to 2015 and 2030. The PEA mode modell is based on duality theory. It is suggested to estimate a system of the cost function and macronutrient demand equation as a function of input prices and other factors. A cost minimizing approach is used to provide a direct mechanism for incorporating the estimated esti mated F FAO AO 2015 and 2030 crop production
vii
into the model; the target production level is a parameter in the cost function. Because of the theoretical base, it requires strict assumptions, but its results tend to provide more insight into the mechanism of the fertilizer market than the other two approaches. The cropland can be included as an independent variable. The trend variable captures technology change and growth in environmental concern. Depletion or build up of soil fertility can be analysed by comparing the estimated coef ficient of crop production to crop removal parameters, as in the VAR model. The forecasts are generated by inserting projected fertilizer prices and crop production into the macronutrient demand equations.
viii
Acknowledgements
This study is based on the work of F. Tenkorang, Department of Agricultural Economics, Purdue University, United States of America. The study benefited from the contribution of J. Lowenberg-DeBoer (Purdue University), J. Poulisse and T. van den Bergen (FAO).
ix
Glossary
AR
Autoregressive
BNF
Biological Nitrogen Fixation
CABA
Common Agricultural Policy of Agenda
CBAT CBA T
Codes of Best Agricult Agricultural ural Practice
CE
Central Europe
CT
Conventio Conventional nal Tillage
EFMA
European Fertilizer Marketing Association
EPA EP A
Environm Environmental ental Protecti Protection on Agency
EU FAO
European Union Food and Agriculture Organization of the United Nations
FIAP
Farm Income and Adaptation Policy
FSU
Former Soviet Union
GPS
Global Positioning System
IFA IFA
Interna International tional Fertili Fertilizer zer Industry Associat Association ion
IFDC
International Fertilizer Development Centre
INES
Increased Nutrient use Ef ficiency Scenario
IRRI
International Rice Research Institute
LM NAFTA NAFT A
Lagrange Multiplier North American Free Trade Agreement
NT
No-Tillage
OECD
Organisation for Economic Co-operation and Development
PA
Precision Agricult Agriculture ure
PEA
Production Economics Approach
PP
Permanent Pasture
PPI
Potash & Phosphate Institute
PPIC
Potash & Phosphate Institute of Canada
SEM
Structural Econometric Models
x
SSA
sub-Saha sub-Saharan ran Africa
SUR
Seemingly Unrelated Regression
TFI
The Fertilizer Institute
VAR
Vector Autoregre Autoregression ssion
VRA
Variable Rate Applicat Application ion
1
Chapter 1 Introduction
Long term projections of international agricultural production and/or resource requirements are fraught with assumptions, data limitations, and ill-understood economic and physical relationships. Despite these well-known deficiencies, there continues to be considerable interest in future agricultural agricul tural production from a number of quarters. Public agencies charged with developing and implementing food, agriculture, environmental and trade policies; organizations concerned with food security issues, and agri-businesses focused on production, processing and marketing of agricultural commodities and inputs are constantly assessing the future state of the global agricultural sector. Investment planning and public policy initiatives are often better served when a systematic approach is employed to quantify and explicitly examine the relevant factors affecting the future state of agricultural production and resource requirements. There appears to be some consensus in the research community about the likely future path of global agricultural production and resource use (IFPRI, 1995; NAS, 1998). Aspects of this consensus can be succinctly summarized as follows: growing world population and per caput incomes will likely require more intensive agricultural crop production. Higher yields will, in turn, increase the demand for agricultural inputs. Future agricultural cropping patterns will reflect shifts in diets (e.g. greater meat consumption). Greater opportunities for agricultural trade may also lead to regional shifts in world crop production. At the same time, there will likely be economic and environmental incentives to improve the ef ficiency of fertilizer use over current levels in all countries, but especially in the developed countries. The overall goal of this paper is to examine improved methodologies for FAO forecasting of fertilizer demand that are consistent with FAO FAO projections of agricultural production in 2015 and 2030. This paper is a follow-up to the FAO publicatio publication n “Fertilizer requirements in 2015 and 2030”. Chapter 2 categorizes countries by fertilizer adoption level. Chapter 3 proposes three methodologies for fertilizer demand forecasting. Chapter 4 provides an overview and suggestions for next steps.
3
Chapter 2
Categorizing countries by adoption level
The review of literature identified key differences in the growth or decline of the fertilizer demand by region. Forecasts will be improved if regions with relatively similar fertilizer demand characteristics are identified. This chapter uses qualitative differences between regions and some simple statistical tests to identify separate regions. In West West Euro Europe, pe, five countries account for 80 percent of the region s fertilizer consumption. The region consumes about 11.5 million tonnes of fertilizers; fertilizer consumption is expected to decline. Over 40 percent of the fertilizers are applied to cereals (FAO/IFA/IFDC, (FAO/IFA/IFDC, 2002). Fertilizer Fert ilizer consumpti consumption on in Central ʼ
Europe (CE) and the Former Soviet Union (FSU) fell in the 1990s. There are 4 major consumers in CE and 3 in the FSU. Since the early 1990s, fertilizer consumption remained stable at about 20 percent of its former level. North America s fertilizer consumption has been rather stable around 20 million tonnes; the United States of America (USA) account for 90 percent of this amount. The consumption of Latin America and the Caribbean shows an upward trend; it reached about 13 million tonnes in 2001/02. The largest consumer is Brazil followed by Mexico and Argentina. Cereals receive the major part of the fertilizers. In North Africa and the Middle East, four countries consume 70 percent of the total consumption in the region (6.8 million tonnes). The consumption has been increasing since 1970 and this trend will continue. Subʼ
Saharan Africa Africa (SSA) is the region with the lowest fertilizer consumption. For the past 20 years, it has been around 2 million tonnes (IFA statistics). Adoption Adoption of fertilizer use has been slow and this may change gradually. South Africa is the major consumer (38 percent). Fertilizer consumption in Asia has increased considerably.. The region consumes almost 50 percent of the world total. considerably FAO currently estimates that the world fertilizer consumption must increase to about 180 million tonnes (±10 percent) in the next 30 years to attain projected crop production. This implies an annual growth rate of about one percent, which is less than the 3.3 percent experienced in the last 30 years (FAO, 2000). The consumption in countries in the developing world will presumably increase while consumption in the developed world will decrease. At present, geographical location is the basis for the FAO fertilizer regions (IFA, 2002).
4
Fertilizer requirements in 2015 and 2030 revisited
The differences in average fertilizer application rates between regions are tested for with a student t-test at a five percent level of significance. This is to determine whether some neighbouring regions can be grouped together. A scatter plot of fertilizer application rates versus wheat yield shows the extent of differences in nutrient ef ficiency among countries. Using consumption characteristics, countries found to be outliers in their current categories will be re-categorized into appropriate groups. Characteristics include the level of consumption and fertilizer use growth pattern. The overall expected low growth rate of fertilizer use stems from the following factors: reduction in consumption due to environmental concerns, non-increasing consumption in the developing world, and improved ef ficiency in fertilizer use in the developed world. Figures 1 and 2 show that some countries
FIGURE 1 Projected fertilizer use ef ficiency in selected countries for wheat yields < 3 t/ha 4.5
4.0
India USA 3.5
Greece Pakistan
China Spain
3.0 a h / s e2.5 n n o t
Romania
Bangladesh Uruguay
Latvia
China
Canada Nepal
Iran
Turkey Paraguay Lebanon Greece Israel Romania USA Israel India Uruguay Argentinaa Argentin Portugal Brazil Guatemala Spain Canada Argentinaa Argentin Latvia Guatemala Paraguay Pakistan Peru Australia Lebanon Madagascar Bangladesh Australia Turkey Madagascar Brazil Iran Portugal Nepal
2.0
1.5
Peru
1995–97
1.0
2030 1995–97
0.5
2030
0.0 0
5
10
15
20
25
30
kg wheat per kg fertilizer nutrient Source: Adapted from Fertilizer use by crop (FAO/IFA/IFDC, (FAO/IFA/IFDC, 2002).
35
40
4
5
Chapter 2 – Categorizing countries by adoption level
FIGURE 2 Projected fertilizer use ef ficiency in selected countries for wheat yields > 3 t/ha 14.0
12.0
Ireland UK
Belgium/Luxembourg
Denmark
10.0
Netherlands
Germany
France
Egypt Ireland
a 8.0 h / s e n n o t 6.0
Sweden
Netherlands
UK
Belgium/Luxembourg Austria Germany France Zimbabwe Czech Republic Hungary Mexico Sweden Finland Egypt Poland Austria Croatia Italy Saudi Arabia Zimbabwe Czech Republic Saudi Arabia Mexico Croatia Finland Denmark
Chile Japan Japan Chile
4.0
Italy
Hungary
Poland
1995–97 2030
2.0
2030 1995–97
00 0
10
20
30
30
50
60
kg wheat per kg fertilizer nutrient Source: Adapted from Fertilizer use by crop (FAO/IFA/IFDC, (FAO/IFA/IFDC, 2002).
achieved higher wheat yields than others with the same or even lower fertilizer application rates. For instance, the United States of America has been able to increase lower fertilizer application through precision (PA) andyields otherwith ef ficiency enhancing technologies. However, in agriculture SSA, low application rates mean low yields. This is an indication that the expected growth in fertilizer consumption will not be the same across countries. Table 1 shows the differences (un-shaded boxes) and similarities (shaded boxes) of fertilizer application rates between regions. The effects of the factors cited above vary among countries. These differences, in addition to the fact that some countries in some categories may have to be re-grouped into different regions, makes it imperative to examine the current fertilizer consumption characteristics and the expected response of the various regions and countries, and re-categorizing them based on their expected consumption pattern where necessary.
6
Fertilizer requirements in 2015 and 2030 revisited
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7
Chapter 2 – Categorizing countries by adoption level
Forecasting future fertilizer demand with such categorization would not have been a problem had all countries in their current categories been at par in regards to consumption, and technological know-how. The characteristics of some countries distinguish them from the other members of their respective categories. These distinct countries are at different adoption levels compared to the other member countries. As a result, their inclusion in the current fertilizer consumption categories needs to be reconsidered. This is crucial for accurate prediction of future fertilizer use. A second look is taken at the present categorization to see whether there is the need for re-categorization.
SUB-SAHARAN AFRICA Because of the characteristics of agriculture in SSA, fertilizer consumption is low and expected to increase only slowly in the next decade or two. A distinct country in the region is South Africa. It accounts for about 38 percent of the ʼ
region s total fertilizer consumption. South Africa has maintained a fairly stable consumption of about 0.8 million tonnes per year for over a decade now (IFA (IFA,, 2002). Improved agricultural practices such as variable rate fertilizer application, variable rate seeding, yield monitoring, which are found in North America and Europe, exist in South Africa. Correlation analysis of fertilizer consumption in the sub-regions in SSA shows that South Africa s consumption is always significantly different from the other sub-regions (Naseem and Kelly, 1999). Therefore, in terms of fertilizer application and agricultural practices, South Africa is miles ahead of the other SSA countries. It ranks similarly to Central European countries or Australia and New Zealand. Hence, it is not included with other SSA countries. South Africa can be re-categorized among the other ʼ
countries of the Southern Hemisphere in Oceania. South Africa shares in particular with Australia Australia a legacy of very old weathered soils, a well developed economy and easy access to technology from North America and Europe.
NORTH AFRICA AND THE MIDDLE EAST Because of harsh conditions in this region, only 38 percent of the 1.1 billion ha of land is fit for human habitation. Irrigation is a necessity in the region because the humid and semi-humid areas cover only two percent of the land (FAO, 2001). Five million ha out of the six million ha land equipped for irrigation are under cultivation. Forty percent of the 296 million people live in rural areas. The region was one of the strongest in agricultural technology but now lags
8
Fertilizer requirements in 2015 and 2030 revisited
behind. Fertilizer consumption in the region is about five percent of the world total. Consumption is on an upward and steady trend. Although fertilizer consumption in North Africa (except Egypt) is not comparable to that of the Middle Eastern countries, it makes sense to categorize them together as the Near East because of their common sub-regional interest and the alignment of the North African countries to the Middle East. Appendix B provides a list of the countries in this category.
WEST EUROPE, CENTRAL EUROPE AND FSU Almost all countries in West Europe have experienced a decline in fertilizer consumption over the last five years. There is no reason to eliminate any an y country from this category. CE and the FSU have similar total consumption patterns in terms of total nutrients . However, their application rates differ. CE s application rates are over nutrients. 100 kg/ha, while those of the FSU are less than 30 kg/ha. Population growth rate in this region is very low, with countries such as Bulgaria, Hungary and Croatia having negative growth rates. Agricultural transformation in CE is more advanced compared to the FSU. CE is following W West est Europe and North America in terms of agricultural technology such as conservation agriculture to improve their agriculture (FAO, (FAO, 2001). The CE countries are also motivated to follow the standards of West West Europe because of their desire to join the EU. The FSU accounts for a greater proportion of the decline in this group s fertilizer consumption. With time, the FSU s fertilizer consumption will probably increase ʼ
ʼ
ʼ
while the consumption in the other European countries is expected to decline. Based on these differences putting CE and the FSU in the same category is not appropriate. This is confirmed by the significant differences in their fertilizer consumption consumpti on patterns (Table 1). Although T Table able 1 also shows tthat hat the differenc differencee between CE and West Europe s application rate is significant, for the above reasons, it is appropriate to put the CE countries that are more similar to W West est Europe in the same category as the latter, and refer to the new group as EUR (Appendix B). The CE countries not grouped with West Europe and the FSU will form another category. Data of decentralization are available from 1990 onwards for the FSU and for the Czech Republic and Slovakia from 1993 onwards. ʼ
9
Chapter 2 – Categorizing countries by adoption level
NORTH AMERICA, LATIN AMERICA AND THE CARIBBEAN Canada and the USA are major exporters of crop products. Increased crop yield has been achieved with declining increments of applied fertilizer. Although mineral fertilizer fertil izer consumption in Cana Canada da is far less than that in the USA (mainly because of less cultivated land), both countries have access to, and utilize the most improved fertilizer application methods. Their similarities as well as being neighbours put them in the same category. The expected increase in food production in Latin America and the Caribbean will come mainly from increasing the cultivable land, which will lead to increased fertilizer consumption in the region. This has been forecasted to be about four percent per annum (IFA, 2002). The region is made up of 42 countries, which share similar agricultural development and environmental protection issues. The region looks forward to improved economies due to an expected increase in agricultural performance. The countries in the region are listed in Appendix B. The outlier among these countries is Mexico. Mexico s contiguity with the USA increases the research and technology spillover. Through the North American Free Trade Agreement (NAFTA) (NAFTA) there is also an increasing alliance between Canada and Mexico. As a result, 80 percent of Mexico s exports go to the USA, and this has boosted Mexico s economy with a GDP growth of three to five percent per annum annu m (IF (IFA, A, 2002). A stronge strongerr alliance and more technology te chnology spillover can be foreseen in the future. For these reasons, it is proposed to put Mexico, the USA and Canada in the same category. ʼ
ʼ
ʼ
ASIA Asia can be divided into three subregions: South Asia, Southeast Asia and East Asia. Many Southeast Asian countries are overusing fertilizer. They have exceeded their theoretical maximum levels. All countries in South Asia are using three to 70 percent of their maximum. Countries such as Cambodia and Laos use three to five percent while Malaysia and India use over 50 percent. China, Korea PDR and Vietnam Vietnam have room for expansion. China consumes only 62 percent of its theoretical maximum. Although the countries in the regions collaborate on eliminating the environmental impact of fertilizers, the th e regions are at different stages in fertilizer
10
Fertilizer requirements in 2015 and 2030 revisited
adoption. As a result, different measures are required to solve each region s problems. Two Two categories are proposed for Asia: East Asia and the Rest of Asia (RoA). This is because East Asia is the only sub-region where the majority of countries are overusing fertilizer fertilizer.. ʼ
OCEANIA Fertilizer application rates in the region are larger than in SSA. In terms of total nutrient consumption, Oceania s consumption is more than SSA s by about one million tonnes. However, unlike SSA, Australia and New Zealand are high-income countries, and have the available improved fertilizer application methods such as VRA. The region consumes more P than N and K. As mentioned above, South Africa is out of place in the SSA group. Therefore, categorizing South Africa with Australia and New Zealand is suggested. The suggested recategorization will fine-tune the FAO estimates mentioned earlier. ʼ
ʼ
CONCLUSION The current categorization is by geographical location. The literature review has shown that not all countries in the same category exhibit similar fertilizer consuming characteristics. Overlooking such outliers can have serious implications when modeling fertilizer demand data. Because of the importance for the future FAO FAO fertilizer forecast, the following categorization is recommended: 1. SSA (excluding South Africa and Sudan) 2. 3. 4. 5. 6. 7.
Oceania (including South Africa) East Asia (a (all ll East Asian countries) Rest of Asia (RoE) (excluding E East ast Asian countries) countries) North America (including Mexico) Latin America aand nd the Caribbean (excluding Mexico) EUR (W (West est Europe and Bulgaria, Czech Rep Republic, ublic, Hungary, Hungary, Poland, and Romania) 8. Rest of Europe (RoE) Central Europe and FSU (excluding Bulg Bulgaria, aria, Czech Republic, Hungary, Hungary, Poland, and Romania) 9. Near East – all North African and Middle Easte Eastern rn countries Appendix B shows the full list for each category category..
11
Chapter 3
Proposed fertilizer demand forecasting methods
Long term forecasting is at best an inexact science, which must make the best of both formal estimation methods and the informal observations of those in the fertilizer and related industries. This section focuses on potential improvements on the formal quantitative methods used by FAO to forecast fertilizer demand. The three methodologies proposed are described below: 1. simple structural econometric models (SEM) bas based ed on modification of past fertilizer demand methodologies; 2. time series modeling with V Vector ector Autoregr Autoregression ession (VAR); (VAR); 3. causal mo models dels based on productio production n economics approach (PEA) and duality theory. Parthasarthy (1994) reviews the basic issues in fertilizer demand forecasting. He divides forecasting into three steps: i) assessment of potential; ii) forecasting demand; and iii) forecasting sales. The focus of this section is on step ii) forecasting demand. For public and private planning, fertilizer demand potential potenti al based on agronomic needs may be a useful upper limit, but this estimation omits key factors in economic demand (e.g. price relationships, national and international fertilizer policies, trends). Forecasting sales of particular fertilizer fi
products in speci countries not feasible given the 11 to 26 year offset from the forecast targetsc (i.e. 2015,is2030). Parthasarthy also specifies four categories among forecasting methods: 1. measurement of pot potential ential based based on biological requirements; 2. time series analysis; 3. casual models; 4. qualitative approach. As noted above, the biological potential estimates may be useful, but they are not adequate for private and public planning. The qualitative approach relies on expert opinion and can be useful in sparse data environments, but in this case it is more of a complement to the quantitative approaches than a
12
Fertilizer requirements in 2015 and 2030 revisited
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K d n a N r e o c f i r p h g i p h o r ; c s e c i i t t i s c t a i l s e a n l i e e t y i c v i i c t t s i a t a g s l a e e l n N e I
0 s 8 t r 9 e 1 d 2 , b y 8 o d n 9 R a a 1 s , , e t l y a H r j e d n d b a u o e a R H G n e m a s o c t i t e s s s a n p l e o o p r e c s c i c i t e g s r n r p l d o e a n m r e n a a a i m s K e e r , i c d f e P , r r f i N p e d : : s z i r e r l i o i z e t t r c a h e a t F f O M
3 9 d 9 n 1 a , y n l e a b m n o e o r D V
2 5 . 0 : y t i c i R t n t s L . e l a d e e n t m e e u l a t i p c R t s i r S b m o p u c c i t s : n s : r w a d o o l b e n a a R n L I L S
d n a l e r n r I f e t o s c e i l w b , s u e p A t a e S t U s R
K U
n o i t s t s c t n n n u e f e i i r t r s t t u a o g u n s n l l u l c a o a g u D u l o - d d i s i b v v i b i n d o d a r n T n C I I
e s e r p h t o r f l e l - c o e s e e p d R s v r o r m fi c U ) o i a r m e f s S c z r o m i ; l c l i e i & t s s s c r e t t r l p s t d t e e s n ( n r e f : c l e e n o e l e e e r i a m l d i r m i r a r e a t r n t o t o c u o n u a o o m u q u n i p i n e n d t r l o t t s l m l t : c c ; t l n e a e t a c n e n e a s u i u m u a e l e r d n s e a i d d m o e i i g p g f i o s s l v n i v t s p e i v a f o o i i r r c o t t m d e o R d n i i i d d o r r c y n e I n s C c A I n d N S N 3 I
y g o l o n h m r c e a t e f c ; ; i x x e r p e d r d i n n e i z y i e t i i l t c v i r i t r e p c f u t d d ; o n a r o s L p C
l a r u t l u c i r g a ; N f e o t i c e n r p c e i r r p p ; x o e e r c v i ; t d n e a i c v e i i r r e i c r p D p N
K U
s n o i g e r , a d a n a C
A S U
A S U
p c i c i o r m m c o o ; e c n n e i r o o z p e c i c a i c e e r r r m p o p e f r r e h o c e h t t o n e i z o ; ; o g l i i t t a e e r c c r c i e i u e r r f v d a ) p ; p o r r r d s e p n i e z s e z s i ; r i l r l r a a i i t o i t o e r v a r . r e t c e r t c a a A e ( F f r P F f
d n 9 1 a 9 0 0 n 9 2 e 1 , s , n n n e e e s t n s s i r n a K J e H
A S U
e c i r p r e z i l i t r e f ; r u o b a l ; d n a L
k r a m n e D
s e i t i c i t s a l e m r a f e c i r p n w o f o n 5 . a 4 e 0 M –
k r a m n e D
; n t fi o i o t r c p h n - c u f y r a t o o s r e p o h c t p ; l a s n o a u i t n e a d t t a i t ; r t a n a . z a ) u d e i d n l g m 3 l l e o i e 0 a n t r x n 0 t o a i a a ( 2 T p N m p
e i c r P
d n a l ; e c i r p N ; x e d n i e c i r p t u p t u O
P A I F d n a s e l c i t r a d e w e i v e r : e c r u o S
Chapter 3 – Proposed fertilizer demand forecasting methods
13
competing method. The qualitative approach can help fill in gaps for countries where data is inadequate for quantitative estimation and it can help decision makers understand the context of quantitative forecasts. This chapter will focus on time series analysis and causal models.
FERTILIZER DEMAND STUDIES Table 2 summarizes past fertilizer demand studies. One can trace back fertilizer demand studies to at least 1958 when Griliches (1958) studied the impact of fertilizer prices, crop prices and regional effects on the fertilizer demand in the USA. Some country level studies include Burrell (1989) for the United Kingdom (UK); Bonnieux and Rainelli (1987) for France; Dubgaard (1986) for Denmark; Boyle (1982) for Ireland; Binswanger (1974), Shumway (1983) and Frink et al. (1999) for the USA; Green and Ng ong ola (1993) for Malawi; and Naseem and Kelly (1999) for SSA. In general, the primary objective of these studies was estimation of demand elasticities, not forecasting long-term fertilizer demand. The type of causal models used for elasticity estimation does not necessarily provide useful long-term forecasts. ʼ
ʼ
A few studies have focused on forecasting demand. Bumb and Baanante (1996) used food production requirements, agronomic needs and behavioral models to forecast 1.2 percent annual growth rate of fertilizer demand for the period between 1990 and 2020. Alexandratos (1998) forecasted 3.8 percent growth rate per annum for 1989 to 2010. Gilland (1998) predicted 1.89 percent growth rate per annum for nitrogen for the next 50 years to produce 3.6 billion tonnes of cereals (world total). One of the most current estimations is the joint effort of FAO, TFI and USDA. In this study, FAO (2000) forecaste forecasted d fertilizer requirements requi rements in 2015 and 2030 using a baseline scenario and an increased nutrient use ef ficiency scenario (INES). The INES produced lower fertilizer consumption for 2015 and 2030 (151.2 and 165.7 million tonnes compared with 174.7 and 199.2 million tonnes produced by the baseline scenario) because it captured captur ed the ef ficiency of fertilizer use over time (FAO, 2000). Based on the nutrient ef ficiency assumption, an annual growth of 0.7 to 1.3 percent is expected between 1995/97 and 2030. This is in line with the current trend resulting from improved timing, split applications, site-specific management etc. in most developed countries. Currently, FAO (2000) uses this study to support its projected crop yields.
14
Fertilizer requirements in 2015 and 2030 revisited
Three different approaches
The three methodologies proposed to forecast fertilizer demand are: (1) a simple structural econometric model (SEM) based on the modification of past fertilizer demand methodologies; (2) a time series model using Vector Autoregression (VAR) (V AR) (Hamilton, 1994); and (3) demand systems analysis using econometric regression techniques (Chambers, 1988; Capalbo, 1988). All approaches use data for many countries over several time periods. All models allow for temporal correlation. Because of the likelihood of the existence of spatial effects (see Appendix A for details) among country level data on fertilizer consumption, the presence of spatial autocorrelation will be tested. If diagnostics indicate the presence of spatial dependence, then each of the three proposed methodologies will be adjusted to account for this. In the SEM, VAR and PEA approaches, temporal error correlation will also be diagnosed and corrected when appropriate. 1. Simple Structural Econometric Model (SEM) The strength of a simple SEM approach is its simplicity while incorporating insights from economic theory. Griliches (1958, 1959) studied the impact of fertilizer prices, crop prices and regional effects on the fertilizer demand in the USA. Many other studies have followed afterwards. From the summary Table Table 2, the most important variable variabless in fertilizer demand are fertilizer price, prices of other inputs and crop prices. The economic theory states that the demand for a factor input depends on its own price, the price of other inputs (substitutes/complements), and the output. To be consistent with economic a demand function for a normal input must be in its own theory price, non-decreasing in output and homogeneous ofnon-increasing degree zero in prices. 1.a. The SEM model specification FAO s (2000) model for estimating fertilizer demand is a useful starting point for the development of the SEM equation outlined here. This is: ʼ
(1) where: F = = unadjusted fertilizer application rate (by nutrient) Y = t =
yield (area weighted average of major fertilizer consuming crops) a time index
Chapter 3 – Proposed fertilizer demand forecasting methods
15
Rearranging FAO s model as a log-log model gives: ʼ
(2a) where: and , and the index i represents a country.. Rearranging equation 2a and then taking the natural log of both sides country of the equation, the following relation between current fertilizer use, nutrient depletion, and the inter-period change in yield is obtained:
(2b)
Equation (2) is fit using regression. The interaction between Y it and describes nutrient depletion (Jomini, 1990), whereas is the inter-period change in yield. The review of literature identified expansion or contraction in area of agricultural land as being the potential driver of change. If the country level estimates of agricultural land for 2015 and 2030 can be obtained for the forecasting, agricultural land (represented by Z ) can be included in the model. If data are available, including projections for 2015 and 2030, then Z could include population, price changes for fertilizers and crops, environmental quality indexes, and fertilizer-ef ficiency technology indexes. The estimated model would then be:
(3a)
impact pact of nutrient depletion on the current use of fertilizer; βi1 = the im
βi2 = the impact of the the change between periods of fertilizer fertilizer used in the current period; βi3 = the impact of the change between periods of yield in the current period;
γ =
a time trend capturing environmental and technological change (T ); );
θ =
captures changes in land expansion by the inclusion of available land (or proportion available);
i =
country index.
Thus, fertilizer use in the current period is explained by agricultural land, crop production, the change in crop production, the change in fertilizer use over the previous period (essentially lagged fertilizer use) and a trend variable. The coef ficients of interest in this model are θ, βi1, βi2, βi3, and γ. The coef ficient θ
16
Fertilizer requirements in 2015 and 2030 revisited
is the percentage of change in fertilizer demand, with respect to a percentage of change in agricultural land. Coef ficient βi1 is the percentage of change in fertilizer demand with respect to a one percentage of change in crop production. It relates to soil nutrient depletion or build up. If the coef ficient is substantially less than one, depletion is probably occurring. If it is greater than one, the amount of fertilizer applied is greater than the amount required and nutrient build up is probably happening. Coef ficient βi2 captures the effect of lagged fertilizer use. Coef ficient βi3 captures the technical ef ficiency changes in fertilizer use. It is the percentage of change in fertilizer demand with respect to a percentage of change in production. product ion. If βi3 is less than one, each increment of yield requires less than an increment of fertilizer. fertiliz er. The coef ficient γ captures other technologies, regulations and other trends. fi
Nutrient depletion build up on is the dif type cult of to crop, capture directly a simple model. This is becauseor it depends type of soil,inand initial soil fertility. Soil test information is available only on a few locations even in developed countries, and often not at all in developing countries. In addition, it is dif ficult to know the quantity of fertilizer applied on each crop, and how much was the residual effect from one crop to another, especially under crop rotation. This model will be estimated using seemingly unrelated regression (Zellner (Zellner,, 1962). There is reason to believe that the error terms in the model are correlated across regions. The specified model will also use panel data: there is information about yield and fertilizer use for each country over time. Panel data helps to control for the effects of unobserved variables (Solon, 1989). This is useful since not all relevant variables can be included in the model. 1.b. Variables used in the model
The following variables will be used in this analysis: percentage of available arable land, crop yields (FAO (FAO projected yields), fertilizer application rates (total nutrients and individual nutrients), and the time series (T )).. 1.c. Diagnostics for spatial correlation
Since the data is inherently spatial, the presence of spatial autocorrelation between observations is likely. There are many tests that detect the presence
Chapter 3 – Proposed fertilizer demand forecasting methods
17
of spatial dependence between observations (Anselin, 1988). The Lagrange multiplier (LM) test is one such test. If the LM test indicates the presence of spatial correlation, then the model (3) will be re-specified as a spatial lag or spatial error model (Anselin, 1988 and 1992), depending on this diagnostic. For example, if spatial lag dependence exists between observations, then the model (3) is re-specified as: (4) with W an n x n exogenous spatial weights matrix describing contiguous relationships between countries within regions (that is, border-sharing countries), and ρ is an autoregressive (AR) parameter. The AR parameter ρ captures spillover effects of technology technology,, trade, or other unobserved effects that may exist between countries. If spatial error is detected in the residuals, then the error term εit in equation (3) is respecified as, where uit ~N(0,σi2). Conceivably, but rarely, lag and error effects may be present. In this case, a hybrid of these corrections can be specified to model spatial lag and error processes in equation (3). 1.d. Estimation
If no spatial dependence is detected, then equation (3) is estimated using seemingly unrelated regression (SUR). If spatial dependence is detected, then AR terms in the spatial correction models are estimated using maximum likelihood. Generally, Generally, maximum likelihood is used to estimate the above model if it is done by region.
1.e. Foreca Forecasting sting Forty percent of the data will be reserved as out-of-sample data so that the forecasting ability of the model can be validated. The estimated model will be used to generate fertilizer quantities for the withheld years, which will be compared with the actual quantities to test the forecasting power of the model. Afterwards, the whole sample will be re-estimated, and the estimated model used to forecast fertilizer demands for 2015 and 2030 based on projected dependent variables, including FAO s crop yield projections. ʼ
2. Vector Vector autoregressive (VAR) (VAR) model One of the major strengths of VAR VAR models is their forecasti forecasting ng ability (Hamilton, 1994). According to Longbottom et al. (1985), time series models often produce
18
Fertilizer requirements in 2015 and 2030 revisited
better forecasting results than SEM models. This gears the analysis towards the explanation of a variable by its past values, and the past values of other relevant variables. Another reason for using V VAR AR is its simplicit simplicity y. The structu structure re of a V VAR AR model does not depend on economic or plant growth theory per se, but VAR models make use of the idea that economic variables variable s have a propensity to move together over time (Johnston and DiNardo, 1997). Therefore, there are fewer problems with model misspecification. 2.a. Speci fi Speci fication cation of the VAR model
In this analysis, the VAR model is specified as:
(5)
where:
γk = the coef ficient explaining the relation between current-period fertilizer use in country k and and the cross-lag effect of fertilizer use of country k on fertilizer use in country i; δi = the o own-lag wn-lag effect of fe fertilizer rtilizer u use se in country i; gi = the lag effect of country i s yield on their current use of fertilizer; εit = a disturbance term. ʼ
The VAR VAR model incorporates the key forces driving change in fertilizer use. Agricultural land expansion and contraction, technology and environmental trend effects are embodied in the lagged values of the fertilizer demand and crop production variables. Depletion or build up of soil fertility can be analyzed by comparing the estimated coef ficient of crop production to average crop removal parameters (PPI, 1995). Given estimates of the quantities of each crop produced and average crop removal rates, total crop removal can be estimated by nutrient and region. If the removal is substantially larger than the amount replaced with fertilizer (the estimated coef ficient of Y), then soil nutrient depletion is likely to occur with eventual effects on crop productivity. If the effect of production on fertilizer demand (the estimated coef ficient) is larger than the removal, then soil nutrient build up is occurring. 2.b. Estimation
Each equation for country i will be estimated simultaneously using SUR. Estimation procedures for VAR models are available in many commercial
Chapter 3 – Proposed fertilizer demand forecasting methods
19
regression software packages. The analysis will be done for both total nutrients and individual nutrients. 2.c. Spatial VAR
If the LM test for spatial dependence shows that spatial dependence is present, then spatial VAR VAR will be used instead (Dowd and LeSage, 2000). The implication of this is that the lag of F k,t is also relatively as important as the lag of F it in country i. 2.d. Foreca Forecasting sting
One-step-ahead forecasts will be generated and compared with out-of-sample data. Model adjustments, in terms of lag length, will be made when necessary to obtain the most accurate forecasts. Dickey-Fuller tests are commonly used in time-series economic analysis to determine the appropriate number of lags to include in VAR VAR models. Additional Additionally ly,, unit-roo unit-roott tests will be co conducted nducted to check che ck stationarity in the fertilizer and yield time series. This is important to ensure that parameters are correctly estimated, and forecasts are robust. 3. Production economics economics approach (PEA) mode model l In the production economics theory, growers use fertilizer as an input in the production process to optimize some objective, often profit. It can be shown that maximizing profit is equivalent to minimizing cost using the duality theory at the profit maximizing yield level (Chambers, 1988). The duality theory attempts to create systems that accurately capture reality, and that are also applicable to multiple systems in multiple stages of development. Using the mathematical results of Hotelling s Lemma and Shepard s Lemma, a system of equations representing demands for inputs for a given output can be constructed. It is then possible to estimate the system of equations using time series data of prices, yields, input quantities, and other factors. This approach is data intensive, but it has been widely used to estimate input demand elasticities, elasticities , welfare changes, and other economic questions. For example, it has been used to analyze the fertilizer demand in Denmark with data from a cross section of farms (Hansen, 2001). ʼ
ʼ
The use of duality concepts is proposed in order to estimate the conditional demand for fertilizer given a cost minimization objective. This asks for estimates of fertilizer demand that are consistent with the FAO agricultural production estimates for 2015 and 2030. Profit maximization and risk management objectives
20
Fertilizer requirements in 2015 and 2030 revisited
typically assume that production quantities are choice variables, but the classic cost minimization problem takes the production quantity as given, while input quantities change with prices and other factors. Thus the cost minimization paradigm fits the FAO requirement. A translog function is convenient for empirically estimating cost functions, conditional input demands, and marginal cost of production over time. Since true demand functions are generally unknown, the translog is convenient since it is a second order Taylor Taylor series expansion representing a local approximation of any function. Capalbo (1988) used the translog functional funct ional form to estimate industrylevel demands for input factors over a time series. Christensen et al. (1973) used the translog production function to estimate the demand for labour and other inputs for the domestic private economy in the USA. Linking the production economics theory to a demand-forecasting model entails the following. Using duality results, a set of conditional demand functions is obtained by ʼ
solving the first order conditions of the producer s cost minimization problem: . C ( ) is an indirect cost function, q is a vector of outputs, w is a vector of input prices, x are levels of input, and f*( ) is a production function evaluated at optimal input levels. Conditional input demands are derived from the partial derivative of C ( ) with respect to w. Marginal cost of production is derived from the partial derivative of C ( ) with respect to q. The most relevant conditional input demand functions to this study are those of nitrogen (N), phosphorous (P) and potassium (K).
To estimate the conditional forform fertilizer using the translog function econometrically econometrically, , the followingdemand functional is speci fied as:
(7) where: q = yield (a crop-area weighted index) i = country index; k= an index for input prices, k = N, P, K; t= w =
time subscript; input price;
Chapter 3 – Proposed fertilizer demand forecasting methods
T= C= CL =
21
a time trend; total cost of production; cropland;
β, α, γ, δ, θ are parameters to be estimated. Conditional input demands are is given by
and the marginal cost of production
.
These derivatives form a system of demand equations that can be estimated econometrically. The necessary homogeneity and symmetry restrictions are imposed to ensure concavity of the cost function and the behavioral assumptions assumpt ions of profit maximizing producers. Empirically, the left hand side of the demand system of equations are cost shares based on the data. Input prices and output quantities are considered the exogenous variables in the regression. The production economics approach reflects the key drivers of change in fertilizer use noted in the literature litera ture review. The agricultural land can be include included d as an independent variable. The trend variable captures technology change and growth in environmental concern. Depletion or build up of soil fertility can be analyzed by comparing the estimated coef ficient of crop production (γ) to crop removal parameters (φ), as in the VAR model. 3.a. Estimation
The system of conditional demands, marginal cost, and the cost function in equation (7) are estimated using SUR. 3.b. Estimation of elasticities and foreca forecasting sting future fertilizer demand
The responsiveness of fertilizer demand to output production is useful in determining the amount of fertilizer needed to produce a given level of output. Since the data set is a time series, fertilizer demand elasticities can be projected to 2015 and 2030. If the LM test for spatial dependence detects spatial error or spatial lag, then a spatial SUR as proposed by Anselin (1988) will be used for the estimation.
23
Chapter 4
Conclusions and next steps
The literature review suggests that the drivers of change in fertilizer demand may differ substantially from country to country. Demand for food, fiber and other crop products is likely to grow rapidly in Asia, Africa and Latin America because of population growth and economic development, while in Europe and North America the crop product demand is likely to grow slowly. In SSA and Latin America, the area of agricultural land is likely to expand substantially before 2015 and more by 2030, while in Europe and North Am America erica agricultural land may decline slightly as land is diverted to urban and recreational uses. On some land, fertilizer applications will be discontinued as it is used for organic agriculture. This is likely to remain a niche market for premium products, but in some countries, particularly particul arly in Europe, is having an important effect on fertilizer demand. Technology for more ef ficient fertilizer use is being developed mainly in Europe and North America, and environmental environment al concern is encouraging its use there. This same technology is available in Latin America and Oceania, but the economic and regulatory factors are not as favorable for its use as in Europe and North America. Some of the ef ficiency technology is being bein g adapted in Asia; only rarely can it be used directly there because the t he farm structure differs substantially from that of Europe and North America (i.e. very small farms). Some of the new lands opened for cultivation (e.g. Cerrados of Brazil) require substantial initial fertilizer applications to build up soil fertility for crop production. Many soils in Europe, North America and Oceania have experienced a century of build up (particularly of phosphate) and growers may draw on that invested fertility to help cope with tight profit margins and environmental concern. In Africa, many farmers will be using fertilizer for the first time in the study period, while whi le in most of the world fertilizer use has been common for decades. A fertilizer demand forecasting method must deal with these drivers of change and the difference among regions as to their importance. Between 1960 and 2001, total fertilizer consumption increased from about 30 to 137 million tonnes. The highest annual consumption of 145 million tonnes was recorded in 1989. Between 1988/89 and 1993/94 consumption fell by 20 percent due to environmental in of many countries and the economic problems following concerns the breakup the developed FSU. The developing world,
24
Fertilizer requirements in 2015 and 2030 revisited
however, experienced over 100 percent increase in i n fertilizer consumption. Asia s consumption increased by 300 percent. In all, Asia accounted for almost half the world s fertilizer use in 2000/01. Africa and Oceania are the two regions with the lowest fertilizer consumption (2 percent of the world total each). The ʼ
ʼ
share of the developing countries of theby consumption 662002). percent. Consumption is expected to increase 2.3 percent in in 2001/02 2002/03was (IFA, (IFA, Fertilizer misuse has the potential of degrading the environment and affecting human health. Inef ficient application can lead to reduction in soil fertility, water pollution, and NH3 emissions. Nitrogenous compounds are sources of environmental hazards in rice growing countries in Asia. About 60 percent of applied mineral nitrogen (N) is lost through leaching, denitrification, volatilization, and run off, which pollutes the atmosphere and water systems. Reduction in fertilizer consumption in developed countries has been successful due to improved agricultural production technologies such as denitrification inhibitors, polymer coated slow-release fertilizers, and precision agriculture. It is now possible to achieve higher crop yields with less fertilizer. However, higher yields in most developing countries imply application of more fertilizers. In SSA, fertilizer adoption is still at the grass root level due to economic instability and high fertilizer cost. Soil fertility buildup was used to claim marginal soils in Europe and North America many years ago, and large areas in Australia in the early twentieth century.. P and K build up is used to create agricultural land in Brazil. Africa and century Asia will benefit a lot from such activities. activi ties. The USA and Argentina are currently curre ntly believed to mine soil nutrients. Long term forecasting is at best an inexact science, which must make the best of both formal estimation methods and the informal observations of those in the fertilizer and related industries. The three formal methodologies methodologies proposed are: a) simple structural econometric models (SEM) based on modification of past fertilizer demand methodologies; b) time series modeling with Vector Autoregression (VAR); and c) casual production economics approach (PEA) models based on economic duality theory. The current FAO fertilize fertilizerr demand model (F (FAO, AO, 2000) is a starting point for the development of the simple structural econometric model. Fertilizer use in the current period is explained by agricultural land, crop production, the change in crop production, the change in fertilizer over the previous period (essentially lagged fertilizer use) and a trend variable. The yield change variable captures
25
Chapter 4 – Conclusions and next steps
the effect of technical improvements in fertilizer use on fertilizer demand. The trend variable combines both technology technol ogy and environmental quality effects. The coef ficients of this model would be estimated on cross section time series data for each macronutrient using econometric techniques to deal with the temporal and fl
spatial correlation.and Theenvironmental model directlyconcern. re ects the of change agricultural land, technology It effects indirectly reflectsinthe build up or depletion of soil fertility through the crop production variable. The suggested VAR approach uses past observations of the variable in question and crop production. Past observations of other variables could be included if the historical data are available for estimation and the projections available for the period up to 2030. The model does not depend on economic theory and as such, it is easy to model. Agricultural land, technology and environmental trend effects are embodied in the lagged values of the fertilizer demand and crop production variables. Depletion or build up of soil fertility can be analyzed by comparing the estimated coef ficient of crop production to crop removal parameters. Researchers have found that VAR models produce more accurate forecasts than other econometric estimates. The VAR can be estimated with a spatial error structure if diagnostic tests show that this is needed. The forecast is generated by repeatedly estimating forecasts one period ahead out to 2015 and 2030. The PEA model is based on duality theory. theory. Estimating a system of the cost function and macronutrient demand equation as a function of input prices and other factors is suggested. A cost minimizing approach is used providing a direct mechanism for the estimated FAO 2015 and 2030 crop production to be incorporated into the model; the target production level is a parameter in the cost function. Because of the theoretical base, it requires strict assumptions, but its results tend to provide more intuition about the mechanisms of the fertilizer market than the other two approaches. The agricultural land can be included as an independent variable. The trend variable captures technology change and growth in environmental concern. Depletion or build up of soil fertility can be analyzed by comparing the estimated coef ficient of crop production to crop removal parameters, as in the VAR model. The forecasts are generated by inserting projected fertilizer prices and crop production into the macronutrient demand equations.
27
References and background reading
AAFC. 2003. Agriculture and Agri-Food Canada. http://www.agr.gc.ca/index_ e.phtml. AgBrazil. 2003. AgBrazil: Bringing investors and Brazil s agriculture frontier together , assessed , assessed 19 Nov., 2003, http://www.agbrazil.com/index.htm. ʼ
Ahmed, S. 1996. Fertilizer use in Asia: How high can it rise? Appropriate use of fertilizers in Asia and the Pacific. APO/FFTC. Akridge, J.T. & Whikper, L.D. 1997. 1997 precision agricultural services dealership survey results. Purdue Univ. Cent. for Agric. Business Staff Paper No. 97-10. Alexandratos, N. 1998. World food and agriculture outlook to year 2010. Proceedings of the FAO/IFA/IFDC Informal meeting on fertilizer use by crop , 2-3 March, Rome. Alkemade, J.R.M., van Grinsven, J.J.M., Wiertz, J. & Kros, J. 1998. Towards integrated national modeling with particular reference to the environmental effects of nutrients. Envir Environmental onmental Polluti Pollution on 102, S1, Wageningen. pp. 101–105. Amisano, G. & Serati, M. 1999. Forecasting For ecasting contiguited series with BVAR BVAR models. New Jersey, USA. Journal of For Forecasting, ecasting, Vol.18. pp. 463–476. Anselin, L. 1988. Spatial econometrics: methods and models. Dordrecht: Kluwer. Anselin, L. 1992. Space Stat Tutorial. Technical Report S-92-1 of the National Center for Geographic Information Informatio n and Analysis, University of California, Santa Barbara, California, USA. Anselin, L., Bongiovanni, R. & Lowenberg-DeBoer, J. A spatial econometric approach to the economics of site-specific nitrogen management in corn production. American Journal of Agricult Agricultural ural Economics (forthcoming). Arnholt, M., Batte, M. T. & Prochaska, S. 2001. Adoption and use of precision farming technologies: a survey of Central Ohio Pre Precision cision Farmers. Department of Agricultural, Environmental, and Development Economics, Ohio State University. USA. Attandand ana, T. & Y Attandandana, Yost, ost, R R.S. .S. 2003. A site-specific nutrient management approach for maize. Potash and Phosphate Institute, Switzerland. Bett Better er C Crrops, 17. pp. 3–7.
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Appendix A
Overview of analysis of data with spatial structure
Spatial econometrics have come a long way since they were first used by Paelink in his description of multiregional econometric models in the early 1970s (Anselin, 1988). They have become popular because of the realization of dependence (spatial autocorrelation) autocorrelation ) and heterogeneity (spatial structure) inherent in aggregate spatial data. Spatial autocorrelation, autocorrelation , the more acknowledged effect, is the result of lack of independence in cross-sectional data observations, which is usually the result of measurement errors. Spatial heterogeneity is related to the lack of stability over space (Anselin, 1988). Spatial heterogeneity becomes more evident when cross-sectional data are combined with time series data. Spatial correlation, which may be presented in the form of spatial lag or spatial error, however, is often unaccounted for. This is because previously, spatial data were not available, and even though such data are now available by courtesy of PA, PA, analyzing such data has been a challenge (Bullock et al., 2002). There is a wide gap between data analysis and site-specific recommendations of agricultural inputs such as seed, fertilizers and pesticides that will maximize profits and at the same time minimize the negative negat ive environmental effects of these inputs (Lambert et al., 2003). Ignoring this spatial correlation is tantamount to the assumption of independence of crop yields among countries, which if found not to hold, can lead to inef ficient estimates (due to spatial error) and biased and inconsistent estimates (due to spatial lag) (Anselin, 1992). The categorization of countries is an indication of interdependence, and reviewed literature shows that countries within a specific FAO categorized region have many similarities in terms of crop yield, fertilizer consumption, level of technology, population growth rates etc. Of late, spatial effects have begun to receive consideration in time series analyses (Azomahou, 1999; Dowd and LeSage, 1997) especially when the data have cross-sectional dimension. Spatial regression has been used in many fields including epidemiology, environmental science, image analysis, analysi s, oceanography, and econometrics among others (Hallin et al., 2002). The basic feature among these fields lies in the presence of spatial effects. It has also been used extensively in the analysis
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Fertilizer requirements in 2015 and 2030 revisited
of site-specific farm level data (Anselin et al., forthcoming; Bongiovanni and Lowenberg-DeBoer, Lowenberg-De Boer, 2000, 2001 and 2002; Lambert et al., 2003). Concerning fertilizer consumption, spatial autocorrelation is more likely to be present among consumption levels of countries belonging to the same consumption category, and heterogeneity is more likely to be present among consumption levels in different consumption categories.
41
Appendix B
List of fertilizer consuming countries by new categories
42
) A o R ( a i s A f o t s e R
Fertilizer requirements in 2015 and 2030 revisited
h s e d a l g n a B
a s a i s i A e n t o s d a n E I
) n a d u S d n a a c i r f A h t u o S . l c x e ( a c i r f A n a r a h a S b u S
n a t u h B
a i d o b a n m i a h C C
. p n a p a J
e R a e r o K
a i d n I
R D P a e r o K
s r i a a i n e s m p y n p a a i l l a y i h M M P
n o i n u é R
l a g e n w S e R a d n a
s o a L
a n i h C f o e c n i v o r e p , o r n p a a g i n w i a S T
s e l l e h c y e S
a i b i m a N
r e g i N
a i p o i h t E
u a s s i B a a a a a n i b e n n e o n y b m a i i n a a h u u e G G G G G K
a l o g n A
n i n e B
a i r e g i N
a n a w t s o B
o s a F a n i k r u B
e d r e V e u r a a p u m B C C n i o d o r n e
a i l o l g a n p o e M N
e n o e L a r r e i S
o h t o s e L
n a t s i k a P
a k n a L i r S
m a n t e i V
d n a l i a h T
d n a a i l l i a z m a w o S S
a i r e b i L
. p e R n a c i r f A l a r d t a n e h C C
. p e R d e t i n U , a i n a z o n g a T o T
e w a a b d i a n b b a m m a i g Z Z U
r e a u c a q i s i s n u b a i i m a t g w t a a i i r a r i d l l u u z a a a a a o M M M M M M
. . p R . e s D R o , , r o o g o g m o n o n o C C C
e r i o v i I ’ t d u o i t e b j ô C D
a e t i r r E
43
Appendix B – List List of fertilizer consumin consuming g countries by new categories
t s a E r a e N
o c c o r o M
n a t s i n a h g f A
a i b a r A i n r a t a d u m a a O Q S
a i r e g l A
n i a r h a B
s u r p y C
c i l b u p e R b a r A n a d u S
t p y g E
n a i r y S
n a r I
a s i n u T
q a r I
s e t a r i m E b a r A e y k r u T
l e a r s I
d e t i n U
n a d r o J
o r g e n e t n o M & i a b r e S
e n m e Y
t i a w u K
n o n a b e L
a y i r i h a m a J b a r A n a y b i L
) E o R ( e p o r u a E i f g o r o t e s e G R
a m a n a P
s i v e N d n a s t t i K . t S
y a u g a r a P
u r e P
r o d a v l a S l E
a n a e y a p l u u a G a l o m a h d e e n c a d t a i n n a a y t i e e r r u u u a F G G G G H
a i c u L . t S
y a u g u r U
s d n a l s I n i g r i V S U
s a r u d n o H
e a u u a i q g c i n a i a t r a m r c a a i J M N
a l e u z e n e V
o c i x e M . l c x e ( n a e b b i r a C e h t & a c i r e m A n t i a L
r o d a u c E
. p e R n a c i n i
a c s s a i a i a o c i R b d a l n m a e i i a i m t a a e i l l a h i v z r i z l e g o m u m o b o o s r o a l e a b r h A B B B B B C C C C D D a n i t n
R U E
n a t s z y g r y K
n a t s i k e b z U
n o i t a . r e p d e e a R i n a F n o a d v i o e d s c l s a o u M M R
a i v t a L
s u r a l e B
a n i v o g e z r e H / a a t i i n a s o o r B C
. p e R h c e z C
o n i a i r n a a M i n n m o a a p R S S
d n a n l r e e d z e i w t w S S
m o d g n i K d e t i n U
y r a g n u H
d n a l e c I
s d n a l r e a t h l t a e M N
y a w r o N
a i r t s u A
g r u b m e x u L a m i u r a i g l g u e l B B
l a g u t r o P
)
n a t s h k a z a K
n a t s i n e e n m i a k r r k u T U
a i n a u h t i L
a i n a b l A s e n i d a n o e g r a G b / t o n e T / e c m d n a a i n d i V i r n . t u i r S S T
. p e R i a n a t s k a e k v n v i i j o o l l a S S T
n a j a i i a n b e r m e z r A A
d n a l e r I
y l a t I
k r a d m n a n n e l i D F
a i n o t s E
d n a l o P
y n e a e c c r n a m r e e r F G G
44
Fertilizer requirements in 2015 and 2030 revisited
o c i x e M + a c i r s e e t m a A a S d d o h c t a t e i r n i x e a U n M o C N
a c i r f A h t u o S + a i n a e c O
a i l a r t s u A
s d n a l s i I j i F
a i s e n y l o P h c n e r F
d n a l a e Z w e N
a e n i u G a w c i e r f N A a a o h t u p o a u a m P S S
a n g o T
