fertilizer requirements in 2015 and 2030 revisited · land and plant nutrition management service...

Fertilizer requirements in 2015 and 2030 revisited

INTERNAL WORKING DOCUMENT

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS

Rome, 2004

Land and Plant Nutrition Management Service

Land and Water Development Division

Fertilizer requirements in 2015 and 2030 revisited

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© FAO 2004

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Contents

EXECUTIVE SUMMARY v

ACKNOWLEDGEMENTS viii

GLOSSARY ix

1. INTRODUCTION 1

2. CATEGORIZING COUNTRIES BY ADOPTION LEVEL 3Sub-Saharan Africa 7North Africa and the Middle East 7West Europe, Central Europe and FSU 8North America, Latin America and the Caribbean 9Asia 9Oceania 10Conclusion 10

3. PROPOSED FERTILIZER DEMAND FORECASTING METHODS 11Fertilizer Demand Studies 13Three different approaches 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

1. Projected fertilizer use effi ciency in selected countries for wheat yields < 3 t/ha 4

2. Projected fertilizer use effi ciency for selected countries with a wheat yield > 3 t/ha 5

1. Average fertilizer application rate and paired t-test statistics for the regional categories 6

2. Summary of past fertilizer demand studies 12

List of fi gures

List of tables

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, fi bre 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 specifi c 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.

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 Africaʼs atypical consumption in Africa, are a few examples that testify to this. The following categories have been suggested:

1. SSA (excluding South Africa and Sudan)

2. Oceania (including South Africa)

3. East Asia (all East Asian countries)

4. Rest of Asia (RoA), (excluding East Asian countries).

5. North America (including Mexico)

6. Latin America and the Caribbean (excluding Mexico)

7. EUR (West Europe, Bulgaria, Czech Republic, Hungary, Poland, and Romania)

8. Rest of Europe (RoE) Central Europe and FSU (excluding Bulgaria, Czech Republic, Hungary, Poland, and Romania)

9. Near East – all North 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 modifi cation of past fertilizer demand methodologies; b) time series modelling with Vector Autoregression (VAR); and c) causal production economics approach models (PEA) based on economic duality theory.

The current FAO fertilizer demand model is a starting point for the development 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 both technology and environmental quality effects. The coeffi cients 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 refl ects the effects of cropland change, technology and environmental concern. It indirectly refl ects the 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 are available for the period up to 2030. The estimate does not depend on economic theory and as such, it is easy to model. Cropland, 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 analysed by comparing the estimated coeffi cient 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 model 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 FAO 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 coeffi cient 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 benefi ted 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 Codes of Best Agricultural Practice

CE Central Europe

CT Conventional Tillage

EFMA European Fertilizer Marketing Association

EPA Environmental Protection Agency

EU European Union

FAO Food and Agriculture Organization of the United Nations

FIAP Farm Income and Adaptation Policy

FSU Former Soviet Union

GPS Global Positioning System

IFA International Fertilizer Industry Association

IFDC International Fertilizer Development Centre

INES Increased Nutrient use Effi ciency Scenario

IRRI International Rice Research Institute

LM Lagrange Multiplier

NAFTA North American Free Trade Agreement

NT No-Tillage

OECD Organisation for Economic Co-operation and Development

PA Precision Agriculture

PEA Production Economics Approach

PP Permanent Pasture

PPI Potash & Phosphate Institute

PPIC Potash & Phosphate Institute of Canada

SEM Structural Econometric Models

x

SSA sub-Saharan Africa

SUR Seemingly Unrelated Regression

TFI The Fertilizer Institute

VAR Vector Autoregression

VRA Variable Rate Application

1

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 defi ciencies, there continues to be considerable interest in future agricultural 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 refl ect 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 effi ciency 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 projections of agricultural production in 2015 and 2030. This paper is a follow-up to the FAO publication “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.

Chapter 1

Introduction

3

Chapter 2

Categorizing countries by adoption level

The review of literature identifi ed 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 identifi ed. This chapter uses qualitative differences between regions and some simple statistical tests to identify separate regions.

In West Europe, fi ve countries account for 80 percent of the region s̓ 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, 2002). Fertilizer consumption in Central 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 Americaʼs 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. Sub-Saharan 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 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.

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).

Fertilizer requirements in 2015 and 2030 revisited 4

The differences in average fertilizer application rates between regions are tested for with a student t-test at a fi ve percent level of signifi cance. 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 effi ciency 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 effi ciency in fertilizer use in the developed world. Figures 1 and 2 show that some countries

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FIGURE 1Projected fertilizer use effi ciency in selected countries for wheat yields < 3 t/ha

Source: Adapted from Fertilizer use by crop (FAO/IFA/IFDC, 2002).

Chapter 2 – Categorizing countries by adoption level 5

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 yields with lower fertilizer application through precision agriculture (PA) and other effi ciency enhancing technologies. However, in 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.

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FIGURE 2Projected fertilizer use effi ciency in selected countries for wheat yields > 3 t/ha

Source: Adapted from Fertilizer use by crop (FAO/IFA/IFDC, 2002).


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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 regionʼs 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, 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 Africaʼs consumption is always signifi cantly 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 countries of the Southern Hemisphere in Oceania. South Africa shares in particular with 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 fi t 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


behind. Fertilizer consumption in the region is about fi ve 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 fi ve years. There is no reason to eliminate any country from this category.

CE and the FSU have similar total consumption patterns in terms of total nutrients. However, their application rates differ. CEʼs application rates are over 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 West Europe and North America in terms of agricultural technology such as conservation agriculture to improve their agriculture (FAO, 2001). The CE countries are also motivated to follow the standards of West Europe because of their desire to join the EU. The FSU accounts for a greater proportion of the decline in this groupʼs fertilizer consumption. With time, the FSU s̓ fertilizer consumption will probably increase 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 confi rmed by the signifi cant differences in their fertilizer consumption patterns (Table 1). Although Table 1 also shows that the difference between CE and West Europeʼs application rate is signifi cant, for the above reasons, it is appropriate to put the CE countries that are more similar to West 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.


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 consumption in Canada 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. Mexicoʼs contiguity with the USA increases the research and technology spillover. Through the North American Free Trade Agreement (NAFTA) there is also an increasing alliance between Canada and Mexico. As a result, 80 percent of Mexicoʼs exports go to the USA, and this has boosted Mexicoʼs economy with a GDP growth of three to fi ve percent per annum (IFA, 2002). A stronger alliance and more technology spillover can be foreseen in the future. For these reasons, it is proposed to put Mexico, the USA and Canada in the same category.

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 fi ve percent while Malaysia and India use over 50 percent. China, Korea PDR and 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 regions are at different stages in fertilizer


adoption. As a result, different measures are required to solve each regionʼs problems. 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.

OCEANIA

Fertilizer application rates in the region are larger than in SSA. In terms of total nutrient consumption, Oceaniaʼs consumption is more than SSA̓ 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 fi ne-tune the FAO estimates mentioned earlier.

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 fertilizer forecast, the following categorization is recommended:

1. SSA (excluding South Africa and Sudan)2. Oceania (including South Africa)3. East Asia (all East Asian countries)4. Rest of Asia (RoE) (excluding East Asian countries) 5. North America (including Mexico)6. Latin America and the Caribbean (excluding Mexico)7. EUR (West Europe and Bulgaria, Czech Republic, Hungary, Poland, and

Romania)8. Rest of Europe (RoE) Central Europe and FSU (excluding Bulgaria, Czech

Republic, Hungary, Poland, and Romania)9. Near East – all North African and Middle Eastern countries

Appendix B shows the full list for each 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) based on modifi cation of past fertilizer demand methodologies;

2. time series modeling with Vector Autoregression (VAR);

3. causal models based on production 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 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 products in specifi c countries is not feasible given the 11 to 26 year offset from the forecast targets (i.e. 2015, 2030).

Parthasarthy also specifi es four categories among forecasting methods:

1. measurement of potential 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


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Chapter 3 – Proposed fertilizer demand forecasting methods 13

competing method. The qualitative approach can help fi ll 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 Ngʼongʼola (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.

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) forecasted fertilizer requirements in 2015 and 2030 using a baseline scenario and an increased nutrient use effi ciency 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 the effi ciency of fertilizer use over time (FAO, 2000). Based on the nutrient effi ciency 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-specifi c management etc. in most developed countries. Currently, FAO (2000) uses this study to support its projected crop yields.


Three different approaches

The three methodologies proposed to forecast fertilizer demand are: (1) a simple structural econometric model (SEM) based on the modifi cation of past fertilizer demand methodologies; (2) a time series model using Vector Autoregression (VAR) (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 2, the most important variables 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 theory a demand function for a normal input must be non-increasing in its own price, non-decreasing in output and homogeneous of degree zero in prices.

1.a. The SEM model specifi cation

FAO s̓ (2000) model for estimating fertilizer demand is a useful starting point for the development of the SEM equation outlined here. This is:

(1)

where: F = unadjusted fertilizer application rate (by nutrient)Y = yield (area weighted average of major fertilizer consuming crops)t = a time index


Rearranging FAOʼs model as a log-log model gives:

(2a)

where:

and , and the index i represents a country. Rearranging equation 2a and then taking the natural log of both sides 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 fi t using regression. The interaction between Yit and

describes nutrient depletion (Jomini, 1990), whereas is the inter-period change in yield. The review of literature identifi ed 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-effi ciency technology indexes. The estimated model would then be:

(3a)

βi1

= the impact of nutrient depletion on the current use of fertilizer;

βi2 = the impact of the change between periods of 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 coeffi cients of interest in this model are θ, β

i1, β

i2, β

i3, and γ. The coeffi cient θ


is the percentage of change in fertilizer demand, with respect to a percentage of change in agricultural land.

Coeffi cient 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 coeffi cient 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.

Coeffi cient βi2 captures the effect of lagged fertilizer use. Coeffi cient β

i3

captures the technical effi ciency changes in fertilizer use. It is the percentage of change in fertilizer demand with respect to a percentage of change in production. If β

i3 is less than one, each increment of yield requires less than an increment

of fertilizer. The coeffi cient γ captures other technologies, regulations and other trends.

Nutrient depletion or build up is diffi cult to capture directly in a simple model. This is because it depends on the type of crop, type of soil, and 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 diffi cult 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, 1962). There is reason to believe that the error terms in the model are correlated across regions. The specifi ed 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 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


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-specifi ed 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-specifi ed 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, trade, or other unobserved effects that may exist between countries. If spatial error is detected in the residuals, then the error term ε

it in equation (3) is respecifi ed as, where u

it

~N(0,σi2). Conceivably, but rarely, lag and error effects may be present. In this

case, a hybrid of these corrections can be specifi ed 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, maximum likelihood is used to estimate the above model if it is done by region.

1.e. Forecasting

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 FAOʼs crop yield projections.

2. Vector autoregressive (VAR) model

One of the major strengths of VAR models is their forecasting ability (Hamilton, 1994). According to Longbottom et al. (1985), time series models often produce


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 VAR is its simplicity. The structure of a VAR model does not depend on economic or plant growth theory per se, but VAR models make use of the idea that economic variables have a propensity to move together over time (Johnston and DiNardo, 1997). Therefore, there are fewer problems with model misspecifi cation.

2.a. Specifi cation of the VAR model

In this analysis, the VAR model is specifi ed as:

(5)

where:

γk = the coeffi cient explaining the relation between current-period fertilizer

use in country k and the cross-lag effect of fertilizer use of country k on fertilizer use in country i;

δi = the own-lag effect of fertilizer use in country i;

gi = the lag effect of country iʼs yield on their current use of fertilizer;

εit = a disturbance term.

The 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 coeffi cient 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 coeffi cient 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 coeffi cient) 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


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 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. Forecasting

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 models. Additionally, unit-root tests will be conducted to check 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 approach (PEA) model

In the production economics theory, growers use fertilizer as an input in the production process to optimize some objective, often profi t. It can be shown that maximizing profi t is equivalent to minimizing cost using the duality theory at the profi t 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 Hotellingʼs Lemma and Shepardʼs 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, 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).

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. Profi t maximization and risk management objectives


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 fi ts 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 series expansion representing a local approximation of any function. Capalbo (1988) used the translog functional form to estimate industry-level 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 solving the fi rst order conditions of the producerʼs 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 demand for fertilizer using the translog function econometrically, the following functional form is specifi ed as:

(7)

where:q = yield (a crop-area weighted index)i = country index;k = an index for input prices, k = N, P, K;t = time subscript;w = input price;


T = a time trend;C = total cost of production;CL = cropland;

β, α, γ, δ, θ are parameters to be estimated.

Conditional input demands are and the marginal cost of production

is given by .

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 of profi t 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 refl ects the key drivers of change in fertilizer use noted in the literature review. 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 coeffi cient 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 forecasting 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, fi ber 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 America 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 in Europe, is having an important effect on fertilizer demand. Technology for more effi cient fertilizer use is being developed mainly in Europe and North America, and environmental 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 effi ciency technology is being adapted in Asia; only rarely can it be used directly there because the 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 profi t margins and environmental concern. In Africa, many farmers will be using fertilizer for the fi rst time in the study period, while 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 concerns in many developed countries and the economic problems following the breakup of the FSU. The developing world,


however, experienced over 100 percent increase in fertilizer consumption. Asia s̓ consumption increased by 300 percent. In all, Asia accounted for almost half the worldʼs 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 share of the developing countries of the consumption in 2001/02 was 66 percent. Consumption is expected to increase by 2.3 percent in 2002/03 (IFA, 2002).

Fertilizer misuse has the potential of degrading the environment and affecting human health. Ineffi cient application can lead to reduction in soil fertility, water pollution, and NH

3 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, denitrifi cation, 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 denitrifi cation 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 Asia will benefi t a lot from such activities. The USA and Argentina are currently 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 proposed are: a) simple structural econometric models (SEM) based on modifi cation 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 fertilizer demand model (FAO, 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

Chapter 4 – Conclusions and next steps 25

the effect of technical improvements in fertilizer use on fertilizer demand. The trend variable combines both technology and environmental quality effects. The coeffi cients 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 refl ects the effects of change in agricultural land, technology and environmental concern. It indirectly refl ects the 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 coeffi cient 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. 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 coeffi cient 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

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39

Appendix A

Overview of analysis of data with spatial structure

Spatial econometrics have come a long way since they were fi rst 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) and heterogeneity (spatial structure) inherent in aggregate spatial data. Spatial 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, analyzing such data has been a challenge (Bullock et al., 2002). There is a wide gap between data analysis and site-specifi c recommendations of agricultural inputs such as seed, fertilizers and pesticides that will maximize profi ts and at the same time minimize the negative 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 ineffi cient 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 specifi c 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 fi elds including epidemiology, environmental science, image analysis, oceanography, and econometrics among others (Hallin et al., 2002). The basic feature among these fi elds lies in the presence of spatial effects. It has also been used extensively in the analysis


of site-specifi c farm level data (Anselin et al., forthcoming; Bongiovanni and Lowenberg-DeBoer, 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


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Appendix B – List of fertilizer consuming countries by new categories 43

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fertilizer requirements in 2015 and 2030 revisited · land and plant nutrition management service...

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