Cost-Benefit Analysis of Chemical Use in Cotton Production
Environmental Council of Zambia
Sound Management of Chemicals in Zambia:
A Cost Benefit Analysis of Agricultural Chemical Use in the Kafue Basin
Final Report
Samuel M. Bwalya, Ph.D
Peaks Environmental Management Consultants Limited
Libala Road, Kalundu
P.O Box 35632,
Lusaka, Zambia
August, 2010
1
Contents
Analysis of Chemical Use in Zambian Agricultural Sector
........................................................... 13
Trends in chemical utilization in Zambia
.................................................................................. 15
Trends in Imports and Use of Pesticides
.................................................................................. 17
Cotton Production and Productivity
........................................................................................... 18
Pesticide Use and Cotton Productivity
...................................................................................... 20
Specific objectives of the study
Pesticide and Herbicides Productivity Estimates
..................................................................... 21
Optimal Use of Pesticides and Herbicides in Cotton Production
........................................... 23
Cost-Benefit Analysis of Chemical Use in Cotton Production
................................................ 24
Estimating Loss of Labor Income due to Illness (L)
............................................................... 30
Other Costs (Transport, Food and Others)
.............................................................................. 31
Summary Estimates of the Cost of Illness due to Exposure to Agrochemicals
Traditional Cost-Benefit Analysis
............................................................................................... 32
Conclusion and Policy Recommendations
..................................................................................... 36
Tables
Table 1: Total output and value of agriculture production (2000-2007)
......................................... 14
2
Table 2: Consumption of Chemicals in Zambia (Tonnes)
................................................................... 16
Table 3: Percentage Distribution of Chemical Consumption in Zambia (2002-2007)
Table 4: Consumption of Chemical Nutrients from Fertilizers Use in Zambia (Tons,2002-2007)
Value of imports of selected chemicals in Zambia (2000-2006; in 000 US$)
Table 6: Benefit estimates of pesticide use in cotton in Zambia, Cote D’Ivoire and Argentina
Table 7: Estimated cost of illness in K' millions (2008)
...................................................................... 31
Table 8: Comparison of costs and benefits of using chemicals in cotton production (2008,
Table 9: Cost-Benefits of Chemical Use in Cotton Production with and Without External Costs
Table 10: Determining the Optimal Use of Pesticides and Herbicides in Cotton Production in
Table 11: Production, hectare cotton planted, and value of cotton production
............................. 40
Table 12: Estimates of Cost of Pesticides and Herbicides Use In Cotton and Their Productivity
Table 13:Cotton Productivity Estimates (Cote D'Ivoire) In CFA 000
................................................ 43
Table 14: Pesticide Use and Productivity among Cotton Farmers in Cote D’Ivoire
Table 15: Pesticide Use and Productivity among Cotton Farmers in Argentine (1999/2000)
Table 16: Trends in trade revenues collected on imported chemicals (kwacha)
............................ 44
Figures
Figure 1: Number of companies and chemical import licenses issued by the ECZ, 2004—2007)
Figure 2: Trends in cotton production (kgs) in Zambia (1994-2007)
............................................... 19
Figure 3: Trends in government revenues collected on imports of pesticides and herbicides
3
Figure 4: The pattern of disease incidence in central province (2008)
............................................ 28
Figure 5: Pattern of Disease Incidence on the Copperbelt, 2008
..................................................... 28
Figure 6: Pattern of Disease Incidence in Selected Districts, 2008
.................................................. 29
Figure 7: The Distribution of Estimated Medical Costs by Province (2008, K ‘Millions)
Acronyms
CSO
ECZ
FAO
FNDP
Central Statistical Office
Environmental Council of Zambia
Food and Agriculture Organization
Fifth National Development Plan
4
FSP
GDP
LINTCO
MACO
MoFNP
UNDP
WHO
Fertilizer Support Programme
Gross Domestic Product
Lint Company of Zambia
Ministry of Agriculture and Co-operatives
Ministry of Finance and National Planning
United Nations Development Programme
World Health Organization
Acknowledgements
This report has been made possible with the financial assistance and encouraging support from United
Nations Development Programme and the guidance of Environmental Council of Zambia. I extend my gratitude to Mr. Munthali and Mr. Kapindula D. from ECZ and I am also grateful to Mr. Phiri and Mrs
Katomoyo from Ministry of Health for their assistance in acquiring health data. Further, I would like to recognize the participation and contributions of all individuals from the ministries who attended and participated in the workshops held. Finally, my appreciation is extended to Miss M. Nakamba for her contribution and help rendered in compiling data and finalizing the report.
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Executive Summary
Introduction and Background
Chemicals play an important role in the country’s socio-economic development by supporting production activities in the economy. Although these chemicals are important inputs in production, their use can generate adverse health and environmental costs sometimes large enough to overshadow any productivity benefits. The major challenges in spearheading policies and programs to encourage socially optimal levels of chemical utilization is that while the productivity effects of chemicals used in the production function may be easy to quantify from observable market data, the externalities they
6
generate are more difficult to observe and quantify. This is especially the case for persistent chemicals, which generate both short and long-term health and environmental costs. Even in the midst of great uncertainty about the full range of indirect health and environmental costs (externalities), government is expected to produce optimal policies and demonstrate their effectiveness. Identifying optimal policies when information is scanty and largely unavailable creates challenges in debating and identifying the most suitable government policy interventions in chemicals management in the economy.
The objective of this study was to develop a Cost-Benefit Analysis framework and apply it to evaluate the effects of pesticides and herbicides on cotton production and their attendant health and environmental effects. The study focused on cotton because of its high dependence on hazardous pesticides and herbicides, the vast literature on its health effects, and its importance to small-holder farmers as a source of cash income. The objective of the study was therefore to develop a cost-benefit framework and demonstrate how the approach could be used to evaluate government policy options and to use the estimates of costs and benefits to demonstrate a solid base for supporting the mainstreaming of sound management of chemicals in the development planning processes.
Objectives
The objective of this study was to formulate and design a framework for undertaking a comprehensive
Cost Benefit Analysis (CBA) of government action/inaction with respect to the management of agricultural chemical in Zambia, with specific application to the Kafue Basin. Specifically, the specific objectives were to:
1.
Describe and analyze trends in consumption and use of agrochemicals in agriculture and specifically use of pesticides and herbicides in cotton production;
2.
Identify and utilize economic approaches to estimate the contribution of pesticides and herbicides to cotton production and productivity (yield per hectare) and to determine the optimal level of pesticide and herbicide application on cotton fields that maximizes incomes of small holder cotton farmers;
3.
Estimate external health costs associated with pesticides and herbicides use in cotton production and incorporate them into the cost-benefit framework; and
4.
Identify and highlight critical policy implications of the results, discuss the approaches for mainstreaming sound management of agro-chemicals in national planning.
Methodologies and Data
The study utilized the economic theory, and particularly the welfare theory to development, a Cost-
Benefit Analysis framework, that was then applied to analyze the productivity and health effects of pesticides and herbicides used in cotton production in Zambia. Productivity approach, where pesticides and herbicides are regarded as inputs in the production function was used to compute the contribution of these chemical inputs in the value of total cotton produced. Benefits transfer estimates were obtained from comparable studies in the literature to supplement and fill gaps in the data. This approach enabled the estimation of the benefits net of direct costs associated with the use of chemicals
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in cotton production, which satisfy information requirements on the benefits side of the CBA framework.
The cost of illness approach was used to estimate the external cost associated with occupational health problems that farm households and workers experience as they get exposure to hazardous chemicals applied on their cotton farms. This information was then used on the cost side of the CBA framework to produce a CBA analysis adjusted for external costs (health costs) and further incorporate some modest estimation of external benefits (government revenue) in the analysis.
Key Results and Findings
Productivity Analysis and CBA without Health Costs
From the productivity analysis that does not incorporate external health, costs of chemical poisoning suggests that farmers under-utilize chemicals on their cotton farmers and that they can increase production and productivity by increasing the use of chemicals per hectare. Specifically, productivity analysis indicates that a) Farmers producing 1,280kg/ha currently use an average of 1.26 packs of pesticides per hectare, costing US$49.34. The estimated optimal quantity is 1.85 packs per hectare and would cost US$72.30. This means that for farmers to increase yields per hectare and maximize profits, they should increase use of pesticides by 46.5%. b) For herbicides, farmers currently use an average of 1 litre per hectare costing US$25 per litre. The optimal quantity per hectare is estimated at 1.63 litres, implying that farmers producing 1,280 kg/ha should increase the use of herbicides by 63.2% per hectare in order to maximize productivity and profits. c) However, the sensitivity analysis also shows that those farmers who are less productive, producing 990kg/ha should only increase the use of pesticides and herbicides by 13.3% and
26.2% respectively. d) Further, farmers producing less than 870kg/ha with current average use of chemicals should reduce the use of pesticides and herbicides or adopt better farm management practices to improve cotton yields per hectare. e) This result is based on partial analysis where the farmers only consider the unit cost of chemicals and the contribution of chemicals to the value of production at the margin. No attempt is made to incorporate external health costs associated with chemical exposure; f) This analysis is further supported by results from the traditional Cost Benefit Analysis framework which shows that net benefits of using chemicals in cotton production are positive with the cost benefit ratio of 0.81.
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This analysis generates a policy prescription that farmers should increase use of chemicals in cotton production, which is grossly erroneous because the CBA framework used ignores the external health and environment cost such chemicals generate, thereby over-stating their productivity effects substantially.
CBA Adjusted for Health Costs
An extended cost benefit framework was then developed to incorporate the external costs pesticides and herbicides used in cotton production generate. Results obtained from the cost of illness approach estimates the total health costs associated with exposure to pesticides and herbicides at K16, 484.8 million in 2008 (Kafue basin is K11,286.8 million or 68.5 percent of the total health cost). Of this, medical expenses account for 41%, lost wage income 51% and other costs 8% of the total health cost. Medical costs are mostly internalized by government since health care in government health facilities in rural areas is free. This means that government spent a minimum of K6, 714.5 million to treat illnesses associated with chemical poisoning from cotton fields in 2008. This is a significant share of the total public expenditure.
When external health costs are incorporated in the CBA framework, the key observations and policy implications are that:- a) The results show that when health effects of exposure to chemicals are quantified and incorporated into the CBA framework and internalized by the farmer, the decision criteria changes—it is no longer profitable for the cotton farmer to increase the use of pesticides and herbicides as was the case when only private cost and benefits were considered in the CBA or in the optimization section above where results recommended an increase in the use of chemicals to boost productivity and income. b) Although government generates tax and non-tax revenues from the importation of these chemicals into the country and from licensing chemical handlers amounting to K3, 852.8 million, these revenues only represent 23.4 percent of the estimated short-term occupational health costs associated with exposure to chemicals by cotton farmers and their workers. c) Furthermore, since health services are generally free in all rural areas and all illnesses are reported and treated in government owned facilities, the government directly bears the estimated medical expenses incurred in treating illnesses rising from agro-chemical exposure. It is estimated that government revenues (tax and fees) only account for 57.4% of estimated medical costs in the five provinces covered in the study. It is therefore in the government’s interest to streamline the use of hazardous agro-chemicals in cotton production in a manner that reduces government health expenditure and increases yields and incomes of cotton farmers. d) These results also show that the current revenue instruments based on the “polluter pays principle” are largely inadequate to raise the necessary revenues to finance the provision of health services to those affected by these chemicals. This means that both the licensing regime
(regulatory instruments) and taxes (economic instrument) will need to be reviewed in order to
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ensure that health externalities are internalized and sufficient revenues are raised to address any consequent health and environmental impacts. e) The fact that the farmer has free access to health services increases his net private benefit from the use of these toxic agro-chemicals thereby by enhancing his incentives to intensify their application per hectare. This raises important policy implication both for the management of health services as well as management in the country. For instance, government should consider raising chemical taxes as well as license fees if necessary to ensure that enough revenue is raised to support the treatment of illness due to exposure to these chemicals, and to finance other chemicals management programs where chemicals are discharged in the natural environment. f) Smallholder farmers need to be sensitized about the occupational health effects these chemicals have on the appliers, their families as well as their environment impacts and measures taken to induce them to internalize these costs in their cotton production decisions;
Policy Recommendation
The key policy recommendations are that government should actively undertake the mainstreaming of sound management of chemicals in all critical national development plans and processes in order to ensure and provide for a holistic approach to the management and placement of chemicals on the domestic market. The sound management of chemicals mainstreaming programs should embrace among others efforts to; a) Provide targeted farm extension services covering farm management approaches and technologies, sound chemical management including better spraying techniques and chemical handling guidelines, b) provide for clear chemicals regulations, enforcing stricter chemical labeling requirements and clear guidelines for the placement of hazardous agro-chemicals on the domestic market, c) support technological innovation and development including bio-technology engineering projects that are capable of producing more pest resistant cotton varieties (i.e. use of Bt cotton varieties) and thereby reduce dependence on the use of pesticides to control pests on cotton fields and ; d) develop and implement an integrated pest management program that is both cost-effective and able to increase cotton yields per hectare without substantially increasing dependence and use of hazardous pesticides and herbicides on cotton fields and consequently production and income of smallholder cotton farmers;
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e) review regulatory and economic instrument to ensure that sufficient revenues are collected to finance health and environmental mitigation programs associated with chemical pollution from agro-chemicals in the country; f) ensure that within the sound management of chemicals mainstreaming programs capacity to diagnosis, treatment and manage poisoning cases is improved, and more accurate health statistics and scientific information to support future policy analysis is collected and properly documented. g) Develop a more elaborate and durable institutional framework for engaging key stakeholders in the development and implementation of key activities on chemical management in the country.
Sound Management of Chemicals in Zambia: A Cost Benefit Analysis of Agricultural
Chemical Use in the Kafue Basin
1.
Introduction
Chemicals play an important role in the country’s socio-economic development by supporting production activities in the economy. Although chemicals are important inputs in production, their use can generate adverse health and environmental costs sometimes large enough to overshadow any productivity benefits. The major challenges in spearheading policies and programs to encourage socially optimal levels of chemical utilization is that while the productivity effects of chemicals used in the production function may be easy to quantify from observable market data, externalities they generate are more difficult to observe and quantify. This is especially the case for persistent chemicals, which generate both short and long-term health and environmental costs. Even in the midst of great uncertainty about the full range of indirect health and environmental costs (externalities), government is expected to produce optimal policies and demonstrate their effectiveness. Identifying optimal policies when information is scanty and largely unavailable creates challenges in debating and identifying the most suitable government policy interventions in chemicals management in the economy.
The use of hazardous agro-chemicals has continued to exhibit a strong growth trend around the world.
Cotton producers around the world use US$2.6 billion worth of chemicals in production. This represents
10% of the total world consumption of pesticides and 25% of world consumption of insecticides
(PANNA, 2010). It has also been widely documented that cotton producers use some of the most hazardous pesticides on the market including aldicard,phorate, methamidophus and endosulphan which are collectively referred to as organophosphates substances. Increased use of organophospahtes and carbamates (WHO class 1 chemicals) have been widely documented to cause acute poisoning and deaths. Most of these substances have since been banned in most countries and have been replaced by the second class of Endosulphan substances which also cause acute poisoning and deaths especially in
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developing countries where chemicals management programs and health facilities are generally poor
(Karunarathna et. al., 2003).
Ensuring wise use of chemicals has become even more important in recent times when global trade in chemicals has increased on one hand, and production and distribution of counterfeit chemicals and chemical products is on the increase. This is posing great challenges in promoting legitimate trade in chemicals and enforcing sound use of chemicals especially in developing countries where regulatory capacities are low. In addition to industrial chemical use, agricultural chemicals as fertilizers, soil treatment , and weed controls among other uses has increased as countries struggle to increase food production and reduce food, poverty and hunger. This means that use of agricultural chemicals is increasingly being seen as an important part or aspect of agricultural technology and innovation, which is critical to agricultural development and economic growth of developing countries. It has been observed in most literature that the continued use of such chemicals has resulted in adverse effects on the users’ health and the environment (see Karunarathna et. al., 2003; see Cole, 2007; Winchester, et al;
Guillette et al., 1998).
Zambia is one of the countries where the use of agricultural chemicals is increasingly being seen as an important part or aspect of agricultural technology and innovation, and as being critical to agricultural development and economic growth and poverty reduction. In this respect, public expenditures to agriculture are prioritized in the FNDP (including the Fertilizer Support Program) aimed at providing appropriate, efficient and effective technology development and transfer to assist farmers increase agricultural production and productivity. This objective of increasing agricultural production and productivity through technology development is increasingly reliant on the use of agricultural chemicals and mechanization of agricultural production systems. However, the FNDP does not explicitly recognize the diverse environmental and public health effects of agricultural chemicals and as such lacks concrete strategies for promoting wise use of agricultural chemicals in the country.
While the health sector plans clearly identify environmental health as an important area for program interventions aimed at reducing the incidence of water-borne and vector borne diseases, and further the need to alleviate the burden of non-communicable diseases (including those caused by poisoning), no strong linkages are made between pollution (air and water) -especially chemical pollution-and disease burden. Similarly, no specific linkages between pollution and disease burden are made in other sector plans. This may be attributed to lack of policy, relevant information and tools for planners to use to formulate more comprehensive and targeted national development policies, programs and strategies.
This project aims at generating policy, relevant information and tools that policy-makers can utilize to mainstream sound use of chemicals in national development programs and also to influence the adoption of efficient public sector regulations and interventions in the agriculture and health sectors, among others. The study proposes to develop and use Cost-Benefit Analysis approaches to identify the optimal public policy interventions for promoting wise use of agricultural chemicals in Zambia. Benefits will be estimated using (i.e., change in productivity approach) agricultural data available in the Ministry of Agriculture and Cooperatives and Central Statistical Office (CSO), while the public health impacts of
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exposure to agricultural chemicals will be valued using the Cost of Illness Approach, which is elaborated in details in subsequent sections below. A cost-benefit framework will be developed to evaluate the cost and benefits of government action or inaction regarding agricultural chemical pollution and health impacts in the Kafue Basin. The rest of the report discusses the terms of reference, and elaborate the methodologies, data and valuation approaches proposed for the study and expected results and deliverables in the penultimate sections. Concluding remarks are provided in the final section.
2.
Analysis of Chemical Use in Zambian Agricultural Sector
This section briefly reviews trends in agricultural production of selected crops in Zambia between 2000 and 2008 agricultural seasons with specific focus on three crops—Maize, Cotton and Sugarcane. These crops have been selected as being of great importance to the analysis of agricultural production and impacts of agrochemical use on the Kafue River Basin and its ecosystem. The performance of the agricultural sector between 2000 and 2007 has been sluggish and its output increased by 11.2 percent over the period or an average of 1.6 percent per annum. Despite this dismal performance, the sector is the second largest contributor to GDP (12.2% in GDP in 2008) after retail and wholesale sector, which accounts for 16.4 percent and key in the country’s food security and poverty reduction programs.
The forestry sub-sector dominated growth within the sector registering an annual average growth rate of about 4.3 followed by the crop and livestock with 1.2 percent and lastly negative growth rate of 0.5 percent, picking up in 2006 and 2007 at an annual average positive growth of 1.8 percent. Growth in the crop sub-sector has been undermined by decline in production in both food (maize) and cash crop
(cotton, sorghum, tobacco) production in 2007. While declines in food production is caused by unfavorable weather conditions and input supply constraints, a fall in cash crop production in 2006 and
2007 was attributed to a significant decline in agricultural commodity prices (i.e. cotton) and rapid appreciation of the Kwacha which made cash crop production for export uncompetitive and reduced profitability.
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Table 1: Total output and value of agriculture production (2000-2007)
Product 2000 2001 2002 2003 2004 2005 2006
Maize 115914 88788
Sugar cane 33232
Cotton lint 29689
6574
41540
32658
7330 Cotton seed
65193
47771
38151
8317
129438
47771
46538
10339
136537
51925
56336
12077
Value in $US dollars
95011
51925
69547
30336
159567
51925
63090
11507
2007
152800
51925
78677
16958
Maize 1040000 802000 606172 1157860 1214000 866187 1424400 1366158
Sugar cane 1600000 2000000 2300000 2300000 2500000 2500000 2500000 2500000
Cotton lint 20000
Cottonseed 41000
32658
45550
25700
51900
31350
64650
37950
75900
46850
74218
38000
78000
46000
92000
78208 77600 96000 113850 121068 116000 138000 Total cotton
61000
Source: FAO database
More specifically, the value of maize production reached its highest level in 2006, but declined in 2007, while that of sugarcane showed an initial increase from 33,232 tonnes in 2000 to 51,925 tonnes in 2004, maintaining this level of production through 2007. Production of cotton seed increased steadily from
6,574 tonnes in 2000 to 30,336 tonnes in 2005 before declining to 11,507 tonnes in 2007, following the appreciation of the Kwacha in 2005/6 agricultural season and a fall in cotton prices on the international market. The country needs to increase the growth rate of the agricultural sector in ways that both achieves food security and wide-economic growth imperatives of employment and income generation, and poverty reduction while at the same time addressing negative environmental and health concerns.
Critical in this regard are measures to improve productivity of small-holder agriculture, and increase household incomes from both food and cash crop production. Policies designed to achieve this objective include input subsidies in the maize sector, extension service delivery, farm mechanization and infrastructure development. Productivity increases are envisaged to result from better use of inputs, including use of chemical fertilizer and adoption of conservation farming practices to improve and conserve soil fertility especially in the maize sub-sector. In cotton production, use of chemicals to control pests and herbicides as growth regulators is critical in improving yields and raising incomes from cotton by small-scale farmers who are the major producers of cotton seed in Zambia (over 90% of cotton seed is produced on small-holder farms and largely through the out-grower schemes).
Chemicals used as pesticides and herbicides in cotton production are poisonous and have been documented to cause deleterious environmental and health effects). Most cotton growers around the world use the most hazardous pesticides on the market including aldicard, phorate, methamidophos, and endosulfan, which are cotton pesticides from a broad category of organophosphates—initially produced for World War II. The WHO classifies these cotton pesticides into three broad categories or classes according to toxicity; Class I (1a and 1b) pesticides such as organophosphates, organochlorides and carbamates are highly poisonous chemicals and are often banned or highly regulated. Class II and III pesticides are rated moderately hazardous and slightly hazardous, but use of these chemicals in agriculture including those in class I category in most developing countries is not firmly controlled and
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regulated leading to widespread concern about their environmental and health effects (Karunarathna et. al., 2003).
Cotton is susceptible to pests and hence highly dependent on the use of hazardous pesticides to control pests that evade most cotton varieties grown in Zambia. Studies that examine their health and environmental impacts are rare and have constrained optimal policy and regulatory decision-making aimed at promoting sound use of agrochemicals in Zambia, and in cotton production in particular. This study is probably the first attempt to develop and use a cost-benefit framework to examine the use of agrochemicals in cotton production in Zambia, providing economic estimates of benefits and costs in order to inform policy making and promote sound use of chemicals in the agriculture sector.
A brief overview of chemical consumption in Zambia is provided, including flow of imports and exports of such chemicals over the last decade. The contribution to productivity among cotton farmers is assessed using scanty data available and information derived from similar studies conducted in other developing countries. Benefits of using pesticides and herbicides in cotton production are estimated and discussed and whether or not there is over or under application of chemicals in cotton production is determined, at this point without considering the external costs. External costs are examined using the cost of illness approach in part two of the report, and policy discussions are provided in the final section of the report.
Trends in chemical utilization in Zambia
Table 2 below shows the general consumption of chemicals in Zambia between 2002 and 2007. The chemicals are grouped in five major categories, namely nitrates, phosphates, potassium, super phosphates and Urea. These are mostly agro chemically used as fertilizers and data is obtained from the
FAO websites. The data indicates a steady increase in total chemical consumption between 2002 and
2004, when consumption increased from 149,901 tonnes in 2002 to 222,438 tonnes in 2004, reaching
158,330 tonnes in 2006. Urea accounts for over 45% of chemical fertilizer consumption followed by phosphates (33%) and potassium and nitrates accounting for 13% and 8% respectively. The average annual consumption of chemical fertilizer between 2002 and 2006 was 167,447 tonnes. Ninety percent of these chemicals are actually imported into the country, as operating local production capacity is low to produce and meet current demand for chemical fertilizer in the sector.
For environmental assessments, what really matters is the type and quantities of nutrients being consumed rather than the absolute quantities. Table 3 shows the total nutrients consumed over the same period summarized in three major categories, namely nitrogen (N nutrients), potassium (P205) and potash (K20) chemical fertilizers. Over the period 2002 and 2007, the average annual consumption of these nitrogen, potassium and potash fertilizers averages 52,906 tonnes, 11,825 tonnes, and 21,008 tonnes respectively. Nitrogen fertilizers account for over 60% of total nutrient consumption, followed by potash (28 %) and phosphates (12%) fertilizers. Large amounts of these chemical nutrients applied on farms often end up in surface and underground water systems polluting water bodies from which municipal and industrial water supplies come from, and increasing nutrient loading in rivers that causes nitrification and disturbing riverine ecosystems.
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The effects of the nitrification and general discharge of untreated pollutants into the Kafue River systems are evident in terms of water quality, infestation and spread of weeds and fish mortality among others. Secondary health and environmental effects of exposure to water pollution both in humans, wildlife and livestock have been noticed but not systematically studied and confirmed. With increasing use and reliance on chemical fertilizer in agricultural production, the levels of nutrient consumption are expected to increase in future to levels where active monitoring of these chemicals in terms of use and impacts on the Kafue Basin should become of immediate policy priority in order to ameliorate their environmental and health effects. A detailed discussion of the visible impacts of agrochemical use in the
Kafue Basin is discussed in the later section of the report.
Table 2: Consumption of Chemicals in Zambia (Tonnes)
Chemicals 2002 2003 2004
Nitrates
Phosphates
Potassium
Supper phosphates
Urea
13,652
78,302
4,286
-
53,661
19,666
76,791
10,224
502
54,240
66,283
56,145
9,438
226
90,346
Total 149,901 161,423 222,438
2005
19,661
25,709
27,776
71,996
145,142
2006
13,230
52,340
19,929
594
72,237
158,330
2007 prov.
-
67,762
19
2,785
-
70,566
Table 3: Percentage Distribution of Chemical Consumption in Zambia (2002-2007)
Chemicals
Nitrates
Phosphates
Potassium
Supper Phosphates
Urea
Total
Source: FAO database.
2002
9.1%
52.2%
2.9%
0.0%
35.8%
100.0%
2003
12.2%
47.6%
6.3%
0.3%
33.6%
2004
29.8%
25.2%
4.2%
0.1%
40.6%
100.0% 100.0%
2005
13.5%
17.7%
19.1%
0.0%
49.6%
100.0%
2006
8.4%
33.1%
12.6%
0.4%
45.6%
100.0%
2007 prov.
0.0%
96.0%
0.0%
3.9%
0.0%
100.0%
Table 4: Consumption of Chemical Nutrients from Fertilizers Use in Zambia (Tons, 2002-2007)
Chemical nutrients 2002 2003 2004 2005 2006 2007 prov
Nitrogen Fertilizers (N total nutrients)
Phosphate Fertilizer (P205 total nutrient)
Potash Fertilizers (K20 total nutrients)
Total
4033
5
1347
0
1352
0
67325
42816
14559
17846
75221
60177
11414
13996
85587
42436
5977
20051
68464
44909
8534
19629
73072
86762
16995
41004
144761
16
Nitrogen Fertilizers (N total nutrients)
Phosphate Fertilizer (P205 total nutrient)
Potash Fertilizers (K20 total nutrients)
Total
Source: FAO database. percentage distribution
59.9%
20.0%
20.1%
100.0% 100.0% 100.0% 100.0%
Trends in Imports and Use of Pesticides
56.9%
19.4%
23.7%
70.3%
13.3%
16.4%
62.0%
8.7%
29.3%
61.5%
11.7%
26.9%
100.0%
59.9%
11.7%
28.3%
100.0%
The table below shows value of imports of selected chemicals (pesticides, insecticides, fungicides and herbicides) between 2000 and 2006. Pesticides and herbicides are the major chemical inputs in cotton production and negligible amounts of conventional fertilizers are applied. The value and quantity of these chemicals imported in the country has more than doubled, increasing to US$ 35.9 million in 2006 from US$ 15.9 million in 2000. This increase is proportionally distributed across the various types of chemicals listed in the table, although in absolute terms pesticides account for the largest share of the total value of imports of these chemicals at 50 percent followed by herbicides at about 21 percent.
Exports of these chemicals outside Zambia averaged US$ 600,000 annually between 2000 and 2006, meaning that the country is a net importer of pesticides, insecticides, fungicides and herbicides. Local production of agrochemicals is not known, but should be minimal share of total consumption of agrochemicals in the economy.
In cotton production, pesticides and herbicides are applied complimentarily, as the former are used to control pests and the latter largely as growth regulators. It is therefore expected that to the extent that most of these pesticides and herbicides are imported as inputs in cotton production, their demand should be complementary and almost jointly determined for most cotton varieties grown in the country.
In fact, our preliminary estimates indicate that about 50% of all pesticides and herbicides imported in the country are used in cotton production. Insecticides are more likely to be imported for multiple uses including domestic spraying, and disinfectants for general purpose use both domestic and industrial purposes.
Table 5: Value of imports of selected chemicals in Zambia (2000-2006; in 000 US$)
Item
Pesticides
Insecticides
Fungicides
Herbicides
Disinfectants,etc
Total
Item
Pesticides
Insecticides
2000
7,955
2,978
1,439
2,885
653
15,910
Percentages
2000
50.0%
18.7%
2001
10,292
3,497
2,015
4,059
721
20,584
2001
50.0%
17.0%
2002
12,515
4,068
3,341
4,478
628
25,030
2002
50.0%
16.3%
2003
15,354
4,882
4,333
5,444
696
30,709
2003
50.0%
15.9%
2004
18,462
5,672
4,834
7,171
784
36,923
2004
50.0%
15.4%
2005
18,350
5,924
4,449
6,892
1,085
36,700
2005
50.0%
16.1%
2006
17,954
4,856
4,292
7,407
1,399
35,908
2006
50.0%
13.5%
17
Fungicides
Herbicides
Disinfectants, etc
Total
Source: FAO database.
9.0%
18.1%
4.1%
100.0%
9.8%
19.7%
3.5%
100.0%
13.3%
17.9%
2.5%
100.0%
14.1%
17.7%
2.3%
100.0%
13.1%
19.4%
2.1%
100.0%
12.1%
18.8%
3.0%
100.0%
12.0%
20.6%
3.9%
100.0%
As indicated above, most these chemicals are highly toxic and hazardous to both human health and the environment and therefore their importation and use are regulated, both by use sectors and by the
Environmental Council of Zambia. For most of these, the Environmental Council of Zambia issues import licenses to eligible companies. The number of licenses issued for importation of chemicals increased significantly from 319 licenses in 2004 to 565 in 2007, representing an increase of 77 percent. Similarly, the number of companies issued with such licenses increased by 88.2 percent, and most companies had multiple import licenses (see figure 1). However, the average number of licenses per applicant/company has remained same over the period at 6 licenses, meaning that the increase reflects new licenses given to new entrants or applicants.
Figure 1: Number of companies and chemical import licenses issued by the ECZ, 2004—2007)
Source: Environmental Council of Zambia database.
Cotton Production and Productivity
Cotton production has emerged to be one of the most important sectors, contributing both to export revenues and employment and income of rural households. Almost all the cotton seed is produced by small-holder farmers, each planting approximately two hectares per year. At the time of privatization of
LONRHO which was the sole ginnery in the country, cotton production had by then dropped to its lowest
18
level of 20 metric tonnes in 1994 when LINTCO a monopoly ginnery owned by government was sold to
LONRHO. Liberalization of the cotton sector attracted new entrants in the sector; notable early entrants included Swarp Textiles and Clark Cotton, who largely operated in geographical markets where they operated as monopsonies rather than national oligopsonies (Brambilla and Porto, 2006). LONRHO and
Clark Cotton developed an out-grower scheme, where they provided inputs to small-scale farmers to grow cotton on credit and guaranteed to purchase the produce less the cost of inputs supplied to the farmer. This, coupled with extension services they provided, encouraged a lot of rural farmers to join the cotton out-grower schemes and cotton production increased to 50 metric tonnes in 1996 and by 1998 two more companies, Amaka Holdings, Continental Textiles and Dunavant later entered the industry, increasing the demand for seed cotton in the country. Consequently, the number of small-holder farmers growing cotton increased from about 100,000 in 1999 to over 280,000 in 2005. Cotton production steadily increased from 2001, reaching 120 metric tonnes in 2006.
Figure 2: Trends in cotton production (kgs) in Zambia (1994-2007)
Source: CSO post harvest (1992—1998), and FAO (2000—2008).
Although production increased mainly due to new entrants, productivity per hectare remained low and of great concern to the sustainability of the industry especially in years when cotton prices were low or returns were dampened by the appreciation of the local currency. Cotton production per hectare averaged 830 kg/ha in the nineties. Upon entry, Dunavant embarked on measures to improve input credit delivery to its network of farmers and substantially increased its distribution system across the main cotton growing districts and are now the most dominant player in the cotton sector. But most
19
importantly, in 2005 Dunanvant embarked on a pilot project called YIELD, whose main objective was to improve cotton yields on small-holder farms by teaching its farmers better cotton production practices.
The results of the pilot project demonstrated over 100 percent increase in productivity by farmers who adopted improved cotton seeds and production techniques. Yields per hectare increased from as low as
600 kg per hectare to 1,413 kg per hectare. Currently, cotton production per hectare varies across farmers, ranging from about 600 kg/ha to 1,413 kg per hectare.
Pesticide Use and Cotton Productivity
This section analyzes pesticides utilization in cotton production and estimates the contribution of pesticides to productivity. Firstly, cotton yields on small-holder farms which produce over 90 percent of total cotton production is examined, and then the contribution of pesticide use to productivity is derived from available data. Typically, yields are determined by estimation of a production function that relates cotton output to its inputs, where one of the inputs included in the estimation would be the quantity of pesticides used at each farm for a period of time. Since the analysis would be at farm level, the post harvest data captures output and input information required for productivity analysis (see Brambilla and
Porto, 2006).
3.
Specific objectives of the study
The objective of this study was to formulate and design a framework for undertaking a comprehensive
Cost Benefit Analysis (CBA) of government action/inaction with respect to the management of agricultural chemical in Zambia, with specific application to the Kafue Basin. Specifically, the paper:
1.
Describes the trends in consumption and use of agrochemicals in agriculture and specifically use of pesticides and herbicides in cotton production;
2.
Uses an economic approach to estimate the contribution of pesticides and herbicides to cotton production (yield per hectares) and by implication on smallholder cotton farmers incomes
(profitability);
3.
Determines the optimal level of pesticide and herbicide application on cotton fields per hectare that maximizes incomes of small holder cotton farmers at prevailing output and input prices and uses a conventional cost benefit framework to derive policy implications and recommendations;
4.
Uses the Cost of Illness Approach to estimate the minimum external (environmental costs) of pesticides and herbicides use in cotton production and incorporates these costs to conduct an environmentally adjusted cost benefit analysis of chemical use in cotton production.
5.
Highlights policy implications of the results, discuss approaches for mainstreaming sound management of agro-chemicals in national planning, and provides insights on areas for future research.
4.
Data and Measurements
20
There were several data limitations to the study. Available data does not comprehensively capture information on pesticide application at the farm level thereby making it difficult to directly observe and obtain information required to estimate the contribution of pesticides to cotton production and productivity. Much of the data used is derived from studies conducted on cotton production in Zambia and other countries where the results are relevant to this case study. Using productivity estimates from another study where the context and features are similar is called Benefit Transfer and is a widely accepted practice in environmental economics research and is applied to obtain some point estimates reported in this report.
Two studies on pesticides use and cotton productivity in Cote D’Ivoire and Argentina are reviewed.
These studies estimated a production function for cotton with pesticides included as one of the production inputs (or using a damage function specification). Table 6 below shows productivity estimates for Cote D’Ivoire, Argentina and Zambia estimated as follows:
Estimates for Cote D’Ivoire were obtained from Ajayi (2000) and converted to dollars using the exchange rate reported in the study;
Estimates for Argentina were obtained from Qaim and de Janvry (2005) and are reported in dollars;
Output, price and quantities of pesticides and herbicides used in cotton production in Zambia are obtained from Tschirley, Zulu and Shaffer (2004) and were converted in dollars. Other data sources include Post-Harvest Survey Data from CSO and FAO;
Output per hectare is computed by dividing output produced by hectare planted under cotton, and output-pesticide ratio is obtained by dividing value of cotton produced by expenditure on pesticides;
Marginal physical product (MPP) of pesticides and herbicides is calculated as follows:
MPP (pesticides) =β * (Q c
/Q p
)=P p
/P c
where MPP is the marginal physical product of pesticides, β is a production elasticity on pesticides, Q c
is the quantity of cotton produced, P c
is the price of cotton per kg, and P p
is the price of pesticide per unit. This definition also applies to the computation of the MPP of herbicides.
The optimal amount of pesticide (herbicide) use is computed as Optimal profit maximizing level of pesticide use = β * (Q c
* P c
)/P p
Pesticide and Herbicides Productivity Estimates
Table 6 summarizes the key productivity estimates for Zambia and two reference countries, Cote
D’ivoire and Argentina. The estimate of output per hectare in Zambia is quite comparable with those obtained in the two reference countries. Note that output per hectare across Zambian farmers varies quite widely and these variations largely depend on seed varieties used and farm management practices
21
adopted by the farm household. Output per hectare normally ranges from 550kgs/ha for the least efficient to 1,413kg/ha for those participating in the DUNAVANT YIELD enhancement program. For the purpose of the analysis, 1,280 kg/ha is used as an average cotton yield per hectare (which admittedly is quite optimistic, but a sensitive analysis will be performed to examine the robustness of the results to alternative assumption of cotton productivity).
The average expenditure on pesticide and herbicide per hectare is estimated at US$39.18 per/ha and US
$19.97 respectively. These estimates are slightly higher than those documented in Cote D’Ivoire and
Argentina. In any case, these estimates are not expected to be the same as there are legitimate countryspecific costs that influence the landed cost of these inputs such as transportation costs and differences in profit margins across the distribution chain. One of the most important estimate in cost-benefit analysis and analyses of optimal chemical input use is the marginal value product (which is the monetary value of the additional output produced from an extra unit of chemical applied), which is estimated at
US$3.98 and is 33.6 percent lower than those estimated in Cote D’Ivoire and Argentina. This means that chemical uses in Zambia are less efficiently used than in the other two comparator countries and measures to improve efficient use of pesticides and herbicides should be implemented in order to increase productivity and output per unit of chemicals applied per hectare.
Table 6: Benefit estimates of pesticide use in cotton in Zambia, Cote D’Ivoire and Argentina
Zambia Cote D'Ivoire Argentina
Variables
Output kg/ha in kg
Price of cotton in US dollars
Pesticide expenditure/ha in US dollars
Herbicide expenditure/ha in US dollars
Value of output per ha in US dollars
Output-pesticides ratio in US dollars
Output-herbicides ratio in US dollars
Marginal Value Product (pest) in US dollars
Marginal Physical Product (pest) in kg
Marginal physical product (herb) in kg
MPP pesticides ( elasticity)
MPP herbicides (elasticity)
1280
0.19
39.18
17.97
238.93
6.10
13.30
3.98
387.31
218.56
0.30
0.17
1136
0.26
36.78
19.04
299.49
8.14
15.73
5.97
517.13
258.56
0.40
0.20
1285
0.18
25.85 na
236.44
9.15
10.90
6.00
216.78
117.84
0.17
0.09
Two additional estimates are computed from the data, the cotton production elasticity of pesticides and herbicides and their respective marginal physical products in kilograms of cotton computed. These two estimates enable computation of the contribution of pesticides and herbicides to yields per hectare, not so much directly as would be the case for other inputs (fertilizers on maize yield) but in terms of reducing the crop damage due to pesticides, and growth regulation induced by herbicide use. In other words, pesticides do not generally influence yield directly but mostly non-linearly through the damage function (Ajayi, 2000; Qaim M and De Janvry A, 2005). The cotton production elasticity of pesticides and herbicides are estimated at 0.30 and 0.17 respectively, and when multiplied by average yield per
22
quantity of pesticide (
Q c
/Q p ) and herbicides(
Q c
/Q h ) currently yields marginal physical products of pesticides and herbicides use per hectare of 387.31 kg and 218.56 kg of cotton per ha respectively.
These estimates are conditioned on an average yield of 1,280 kg per ha, and even at this level, there is significant scope to increase yield through improved spraying and improved pesticide management techniques. The scope is even greater for farmers with lower yields per hectare as is discussed in the subsequent sections below.
Optimal Use of Pesticides and Herbicides in Cotton Production
In this section, an attempt is made to look at how farmers use their pesticides and herbicides in cotton production in order to understand whether these chemicals are used efficiently or not. This is important in the sense that regardless of the external costs these chemicals impose on public health and the environment, initial practical responses to reduce these external costs should be instituted at the farm level by ensuring that farmers use these chemicals wisely. To determine the optimal use (without considering external costs), it is assumed that farmers seek to maximize yield and consequently profits from cotton production. Based on this assumption, the static optimal use of pesticides (and similarly herbicides) can be determined as follows (all the variables as defined earlier) 1 :
Optimal use of pesticides = β * (Q c
* P c
)/P p
Since all the variables are known from Table 6, the optimal and profit-maximizing pesticide and herbicide use can easily be determined and compared with the reported actual use of these chemicals by cotton farmers. If the optimal level of use is less than the actual (reported) use, then farmers can increase yields and profits by increasing use of pesticides and herbicides, the reverse is equally true.
Table 10 below shows the optimal level of pesticide and herbicide use at different levels of cotton production per hectare. It is assumed here that the average yield per hectare is 1,280kg/ha, but sensitive analysis is made to capture policy implications for farmers with different production efficiencies or yield rates. This enables us to make well targeted recommendations, depending on farm efficiency and productivity. The table shows that:
Farmers producing 1,280kg/ha currently use an average of 1.26 packs of pesticides per hectare, costing US$49.34. The estimated optimal quantity is 1.85 packs per hectare and would cost US$72.30. This means that for farmers to increase yields per hectare and maximize profits, they should increase use of pesticides by 46.5%.
For herbicides, farmers currently use an average of 1 litre per hectare costing US$25 per litre. The optimal quantity per hectares is estimated at 1.63 litres, implying that farmers producing 1,280 kg/ha should increase the use of herbicides by 63.2% per hectares in order to maximize productivity and profits.
1 For these approaches, also see Nguyen Huu Dung and Tran Thi Thanh Dung (2003).
23
However, the sensitivity analysis also shows that those farmers who are less productive, producing 990kg/ha should only increase the use of pesticides and herbicides by 13.3% and
26.2% respectively.
Further, farmers producing less than 870kg/ha with current average use of chemicals should reduce the use of pesticides and herbicides or adopt better farm management practices to improve cotton yields per hectare.
The important policy implication of this analysis is that farmers need to adopt better farming techniques to improve yield and to improve chemical spraying techniques in order to increase productivity and increase returns from investments in cotton production. This analysis is incomplete in the sense that it ignores the external costs chemicals impose on human health and the environment, a matter that we pick up in the later sections of the report. However, the information so far generated enables us to provide cost-benefit analysis of use of chemicals in cotton production (unadjusted for environmental costs).
Cost-Benefit Analysis of Chemical Use in Cotton Production
In order to conduct an unadjusted cost-benefit analysis of chemical use in cotton production, information on quantity produced by province and total for the whole Zambia is used, with particular focus on Eastern, Central and Southern provinces which collectively account for more than 85 percent of the total cotton produced in the country. Aggregate cotton production figures from 2002 to 2008 are obtained from FAO, and split into three provincial figures using provincial data obtained from the Post-
Harvest Survey data for 2001/2002 agricultural season. Production figures are converted into monetary units using the price of US $0.225 per kg of seed cotton. Hectares cultivated are calculated using a more optimistic yield per ha figure of 1280 kg/ha, and this is compared with the average yield rate of
780kg/ha reported in the 2008 Economic Report (MOFNP, 2009). The difference is almost 40%, but for the purpose of this analysis, an optimistic figure is used to ensure the estimated benefits approximate the best production frontier and chemical use by relatively more productive farmers.
Direct Benefits
The contribution of pesticides and herbicides to the value of cotton production is obtained by multiplying the share of the marginal physical product in average production by price of cotton per kg then multiplied by the total quantity of cotton produced in the country and in each of three provinces.
Table 11 shows production, hectare cotton planted, and value of cotton production. By our estimates, eastern province accounts for 68.2% of total cotton produced in the country, followed by central province (16.9%) and southern (2.2. %). The remainder comes from other provinces. The benefit of using chemicals (pesticides and herbicides) in 2008 is estimated at US$ 14.6 million, which is 47.3 percent of the total value of seed cotton produced in the country. For the Kafue basin, the benefits are estimated by adding production in Southern and Central provinces, which gives US$ 2.8 million in 2007/8 agricultural season, and represents 19.1% of the total value of cotton attributed to productivity increase due to use of agro-chemical (pesticides and herbicides).
24
Direct Costs
Data on the area planted and average chemical use per hectare is used to estimate the direct costs borne by the farmer for using chemicals in the production of cotton production. The area planted used is calculated by dividing value of seed cotton produced by cotton yield per ha (1280kg/ha). Then the expenditure on chemicals (pesticides and herbicides) is obtained by multiplying area in hectares under cotton production by average expenditures on pesticides and herbicides per hectare respectively. These estimates are presented in Table 12. The total direct cost or expenditure on pesticides and chemicals by cotton farmers is estimated at US $4.98 million and US$ 11.85 million (K55, 696 million) in 1997/8 and
2007/8 agricultural seasons respectively. These estimates compare quite favorably with the actual value of imported pesticides and herbicides presented in Table 6 above, and show that about 83.2% of these chemicals are used in cotton production. The total cost or expenditure on these chemicals in cotton production in the Kafue Basin is estimated at K10, 622 million (US $ 2.26 million) in 2007/8 agricultural season.
Estimating External Benefits from Chemicals Used in Cotton Production
In addition to the private benefits that accrue to farmers using pesticides and herbicides on their cotton farms, there are other external benefits that should be quantified that accrue to society. Just like the case of external costs, quantifying all the external benefits from the use of these chemicals is not practically feasible given the limited data available. However, direct benefits in terms of government revenues collected on pesticides and herbicides can be calculated from available readily data on taxes and license fees and charges. These revenues are appropriated as government revenues and are used to finance the provision of public goods and services that generate social benefits to society and the environment. In this context, taxes collected on imported chemicals and licenses and charges collected by the Environmental Council of Zambia are computed to provide some minimum estimate of the direct social benefits derived from these chemicals, in addition to revenue accruing to farmers and others agents along the distribution chain.
Revenues on Licenses and Imports of Pesticides and Herbicides
Table 16 shows trends in tax revenues collected on imports of chemicals into Zambia by the Zambia
Revenue Authority on behalf of Government. These revenues are mainly collected on import duty and
Value Added Taxes. Government collected K6, 890.4 million in taxes on imports in 2005 and K30, 660.2 million kwacha in 2008. Taxes on industrial chemicals accounted for 86.1% of the revenues in 2008 compared to 20.8% in 2005. Herbicides, pesticides and insecticides collectively accounted for 8.3% of total revenue collection on chemical imports in 2008, and were valued at K2,540.9 million
Figure 3: Trends in government revenues collected on imports of pesticides and herbicides
25
Not herbicides and pesticides imported into the country are used in cotton production. It is shown above that about 80% of pesticides and herbicides are used in cotton, meaning that K2, 039 million is attributed to chemical imports for cotton production. Similarly, ECZ is mandated to collect license fees and charges on pesticides and toxic substances (Fig 1).
In 2008, ECZ collected K1, 864 million kwacha for pesticides and toxic substances and was retained as part of its annual budget. In total, K3, 902 million kwacha was collected as government revenue in 2008 and used as the minimum direct estimate of the external benefits associated with agrochemical use in the cotton production.
Taxes, subsidies and fees and other charges can be imposed either to raise government revenue or to promote/discourage production and consumption of certain goods and services in the economy or both.
For instance, taxes may be imposed to correct negative externalities associated with chemical pollution from cotton fields and the amount of revenue raised from such a tax may be contrasted with the estimated external costs (cost of illness) to determine whether or not tax revenues collected are adequate to cover at least the known external costs. Such taxes would be imposed on the pigouvian tax principle as corrective economic instruments for pollution control and justified by the “polluter pays principle”. Revenue collected from licenses and imports of pesticides and herbicides is critical in the evaluation of government actions and policies to promote sound chemicals management and identifying chemicals mainstreaming activities to be included in the national development plans.
Estimating External Costs from Chemical Use in Cotton Production
To value this external cost associated with use of agro-chemicals in cotton production in Zambia and in the Kafue Basin, the Cost of Illness Approach is used. The cost of illness framework allows us to quantify the cost of illness related to water pollution from agricultural chemical runoffs or contamination of ground water systems, exposure to agro-pesticides (occupational health) and food poisoning due to improper use of agrochemicals (pesticides) using clinical data from the Kafue Basin catchment.
26
Specifically, we estimate the cost of illness (F) as the sum of lost income per illness episode plus medical costs (Dwight et al, 2005) as follows:
( 1 ) F
M
L
( 2 )
( 3 )
L
i n
1 iA ij
W
M
C j
E
From equation (2), total medical costs of treating illness attributed to agricultural chemical pollution
(i.e., water pollution) is calculated, with A ij
denoting persons in the catchment area experiencing i days of normal activity for each illness (j), and W is income per work day. Summing across all illness types and persons experiencing illness in the study area give the total amount of income lost due to illnesses arising from exposure to agricultural chemical pollutants.
Similarly, equation (3) measures the total medical expenses incurred to treat associated diseases, with C measuring the proportion of persons exposed to agro-chemical pollution requiring medical attention per illness type (J) and E denotes medical expenses per episode. The cost of illness approach only provides minimum cost estimate of the effect of agricultural chemical pollution in the Kafue Basin, and in cotton production in the country.
Description of the Data and Model Application
In order to apply this method, detailed information on household exposure to chemicals from cotton fields is required together with data on actual illnesses caused by exposure to pesticides and chemicals, medical cost of treating each disease episode, and the number of days its takes to recuperate from a given illness. Such information is currently unavailable and the estimates produced here are based on more aggregated data and information obtained from similar studies. The following information is used to estimate parameters in equations 1-3 above.
Medical Costs (M)
To estimate the medical costs, there is need first to determine the number of people exposed to chemicals used in cotton productions (herbicides and pesticides), identify the illnesses and how these are treated and at what cost. There is no information linking use of chemicals on cotton farms to exposure to chemicals, this information can be obtained through scientific analysis of blood samples of farm households and workers including those living around cotton farms (on-site) that may in one way or the other become exposed to chemicals. There also are those that may be exposed off-site, through for instance contamination of surface and underground water sources through run-offs.
In the absence of such scientific data, estimates are derived from best available disease incidence data.
Data on disease incidence was collected from the Ministry of Health database for each district in
Central, Copperbelt, Lusaka and Southern provinces—these are the main provinces that fall within the
Kafue Basin. The disease incidence data shows attendances by diseases at district level for 2007 and
2008. One drawback with the disease incidence data in the manner it is generated and reported is that the diagnosis is not detailed to identify diseases that are caused by exposure to chemicals or chemical poisoning.
27
The data on poisoning is not very reliable, and is the case for other diseases determined opportunistically or clinically with taking detailed laboratory assessment of samples. All infectious diseases were excluded and only the following diseases were included, Diarrhoea (suspected dysentery), Diarrhoea ( non-blood), Respiratory infections (non- infectious), Digestive disorder ( noninfections), Ear, Nose and Throat ( ENT), EYE, Nervous system disorder ( not epilepsy), poisoning, pulmonary diseases, Skin diseases, Substance abuse, and accidents. Figures 4, 5 and 6 show the pattern of incidence of these diseases in selected provinces.
Figure 4: The pattern of disease incidence in central province (2008)
Figure 5: Pattern of Disease Incidence on the Copperbelt, 2008
28
Figure 6: Pattern of Disease Incidence in Selected Districts, 2008
29
The data suggests that diarrhoea (non-blood, and suspected dysentery), respiratory diseases (noninfectious) and digestive disorder (non-infectious) together account between 71.4 for the Copperbelt
Province to 81.3 percent in southern province of the total incidence of non-infectious diseases identified to be associated with chemical poisoning.
The proportion of persons exposed to chemicals from cotton fields is calculated by multiplying the proportion of cotton growing household in total number of all agricultural households by the total noninfectious (Non-Communicable Diseases (NCD)) that may be caused by chemical poisoning. This yields
5-6% of the identified NCD, attributed to exposure to chemicals mostly occupationally and by drinking water or foods contaminated with agro-chemicals used in cotton production.
Medical costs are calculated using estimates obtained from a study on costing the essential health care package in Zambia (Kabaso, et al, 2006). This study estimates cost of treating each illness episode and captures such costs as cost of medical supplies including medicines, non-medical supplies, human resources and capital costs. The average cost of treating an illness at health centre was estimated at US
$7.40 in 2005 and per-capita costs of US$17.58 per-capita costs for the basic health care package. The average cost of treating an illness of US$7.40 (converted at the exchange rate of K4700 per US dollar) is used. Formally, average medical cost per episode equals US$7.40 time disease incidence times (cotton household/agric household) time the exchange rate (K4, 700).
Figure 7: The Distribution of Estimated Medical Costs by Province (2008, K ‘Millions)
The figure above summarizes the distribution of estimated medical costs in the four provinces in the
Kafue Basin that were incurred in treating persons exposed to chemicals used in cotton productions.
Estimating Loss of Labor Income due to Illness (L)
The opportunity cost of falling sick due to exposure to agro-chemicals can be calculated by first computing the number of days stayed away from work due to illness, and then determining the
30
foregone income of wage per day. Partial labor losses are ignored as estimates are made for full eight hours workday. A study of smallholder cotton farmers in Zimbabwe estimates that farmers exposed to chemical poisoning on their farms take an average of 3 days to recuperate. This is estimate is adopted and used in this study. Government estimates the average monthly rural labor income at K378, 320, which is slightly higher than an almost outdated minimum monthly wage of K268, 000. With this information, lost labor income due to illness is calculated by multiplying (3 days/26 days) times monthly wage rate times disease incidence. The daily wage rate is estimated at K15, 385, and the total labor income lost is provided in Table 7 below.
Other Costs (Transport, Food and Others)
Transport costs, cost of food and other costs associated with seeking health care in respect of illnesses caused by exposure to chemical use on cotton fields is calculated based on information summarized in
Kabaso et al (2006). The study estimated that transport accounts for 10% of the total medical cost while food and other costs are estimated at 7 % and 3 % respectively. This translates to about 20% of the total cost of medical cost and is computed by obtaining 20% of the medical cost calculated above (that is other costs = 0.2* medical costs). The estimates are summarized in Table 7 below.
Summary Estimates of the Cost of Illness due to Exposure to Agrochemicals
Table 7 below shows the total costs of illness due to exposure to chemicals on cotton fields. These estimates indicate that external cost of using pesticides and herbicides, especially where extension services are on proper use of pesticides is missing or inadequate, can be substantial. The estimates show that the highest costs are in terms of lost labor income due to illness (51.1%) and medical costs (40.7%) and lastly transport and other cost (8.1%).
Table 7: Estimated cost of illness in K' millions (2008)
Province medical expenses labor income lost
Central 2,499.12 3,136.65
Copperbelt
Lusaka
219.96
385.60
276.08
483.96
Southern
Kafue basin
Eastern
1,492.62
4,597.31
2,117.19
Total 6,714.51
Source: own calculation based on secondary data
1,873.39
5,770.08
2,657.29
8,427.36 other costs Total
499.82 6,135.60
43.99
77.12
540.03
946.68
298.52 ,664.54
919.46 1,286.85
423.44 5,197.92
1,342.90 16,484.77
31
Rural farm households seek health care from publicly managed health facilities—clinics and hospitals.
Currently, treatment at rural clinics is 100 percent free while treatment at urban facilities attract modest fee with most of the cost of health care borne by government. This implies that in policy simulation exercise, lost labor income and other costs will natural be borne by the farmer while medical costs are largely borne by government. The total cost in terms of human health impacts in the Kafue Basin is estimated at K11, 286.85 million. Outside the Kafue Basin, eastern province has the largest concentration of cotton farmers and accounts for over 68.2 percent of the total cotton produced in the country. In order to ensure a more representative analysis and estimate of the effects of pesticides and herbicides applied on cotton farms on human health, data on cotton production and illness incidence for households in eastern province was collected and analyzed.
This analysis shows that:
The estimated health costs associated with exposure to pesticides applied on cotton farms by farm households and cotton workers in five main cotton producing provinces is K16, 484.8 million in 2008. Of this health impacts on farm households in the Kafue basin is K11, 286.8 million or 68.5 percent. Eastern province, accounts for the remaining 31.5 percent;
Medical expenses account for 41% of the illness costs, and lost wage income due to sickness by cotton farmers and workers accounts for the highest percentage of 51%. Other costs represent about 8% of the total health cost. Medical costs are mostly internalized by government since health care in rural areas is free;
The estimated human health costs are by no means comprehensive and exhaustive as they are not based on scientifically verified short and long-term human health effects of exposure to agro-chemicals applied on cotton farms. These estimates apply on to occupational health exposures and exclude off-site health impacts due to chemical runoffs from cotton farms; and
The health costs estimates do not include environmental impacts of these chemicals both onside and off-site. Thus the cost of illness approach to valuation as applied in this study provides the minimum economic costs of pesticides and herbicides used in cotton production. This information is used in the cost-benefit analysis of government action and inaction with regard to chemicals management in the agriculture sector.
Traditional Cost-Benefit Analysis
The traditional cost-benefit analysis looks at the direct costs and benefits attributed to chemical use in cotton production, which accrue to the farmer, are compared to determine whether or not it is worthwhile for a farmer to use pesticides and herbicides on cotton farms. The decision rule is that when the cost/benefits ratio is less than one or private benefits exceed private costs, the farmer as the incentive to continue to increase the use of such chemicals on his farm until the direct costs and benefits are equalized. The reverse is equally true when the net benefits are negative or additional incremental cost of a unit of chemicals applied exceeds the increment of value of the increase in yield.
The farmer is indifferent when the benefits just equal the costs.
32
Table 8: Comparison of costs and benefits of using chemicals in cotton production
(2008, Kwacha millions)
Value of production due to chemicals use
Cost of Chemicals (pesticides and herbicides)
Net Private Benefits
Zambia 69,076.4 55,696.2 13,380.2
Central
Eastern
Southern
Lusaka
11,669.4
47,128.6
1,498.6
1,149.4
9,409.0
37,999.7
1,208.3
926.8
2,260.4
9,128.8
290.3
222.6
Copper belt 16.6
Source: authors own calculation.
13.4 3.2
The last four rows of Table 8 shows that net private benefits of using agro-chemicals in cotton production are positive (K13,380.2 million kwacha for the whole country) and the cost/benefit ratio is
0.81. This means that it is actually profitable for the farmer to increase the quantity of pesticides and herbicides applied per hectare of cotton until when the cost-benefit ratio is equals to one (when private costs =private benefits). Two key commentaries can be derived from this analysis:
There is substantial scope to increase cotton yield per hectare by cotton farmers through increased and improved application of pesticides and herbicides. This observation is consistent with the optimization results that show that farmers can increase yields and income by increasing farm efficiencies and use of agro-chemicals;
Both the CBA and optimization results only take into account direct private costs and benefits at the farm-level and ignores the externalities associated with the use of toxic organophosphate chemicals on cotton farms.
The conventional CBA and the optimization results above do not take into account health and environmental externalities (external costs and benefits) associated with the use of organophosphate chemicals by cotton farmers;
To provide insights on why considering external costs and benefits in the Cost Benefit Analysis is important for policy analysis and project evaluation, an environmentally adjusted CBA framework is developed to incorporate the external occupational health impacts estimated using the Cost of Illness
Approach. External benefits are estimated based on the revenues that accrue to government from licensing fees and taxes collected on imports of pesticides and herbicides used in cotton production.
Cost-Benefit Analysis Adjusted for Externalities
The social or environmentally adjusted cost-benefit analysis framework incorporates external costs and benefits of chemical use in cotton production. An attempt is made to include estimates of the external benefits and external costs associated with chemical use in cotton production in the country. The social
CBA framework therefore incorporated the following information in the adjusted CBA framework:
33
Private costs incurred by farmers in cotton production are estimated as the amount of money farmers spend on pesticides and herbicides as estimated in Table 12 in the appendix;
Private benefits are calculated as the value of productivity increase associated with the use of pesticides and herbicides as shown on Table 12
External costs associated with use of organophosphate chemicals on cotton fields have been estimated using the Cost of Illness Approach and are incorporated in the CBA below;
Indirect costs of pesticides and herbicides used in cotton production are estimated by quantifying the health impacts these impose on those exposed to them either occupationally on-site or through environmental contamination of water bodies and foods that people consume (on-site and off-site).
External benefits are estimated based on the revenues derived from licensing on pesticides and toxic chemicals imposed by ECZ plus the tax revenues collected on imports of such chemicals into the country.
Table 8 below shows estimates of costs and benefits when indirect costs and benefits are included.
Gross value of increased production attributed to chemical application on cotton fields is shown in the first column and the cost of agro-chemicals in the second column. Columns 3, 4 and 5 show estimated of net private benefits, external costs estimated using the Cost of Illness Approach, and external benefits
(government revenue on cotton chemical imports and licensing). The last column shows the net benefits after adjusting for external costs and benefits. These estimates are shown for each of the five provinces included in the study, including eastern province which accounts for over 68% of the total cotton production.
Table 9: Cost-Benefits of chemical use in cotton production with and without external costs
Province
Central
Copperbelt
Eastern
Gross productivity due to chemical
11,669.4
16.6
47,128.6
Cost of pesticides and herbicides
9,409.0
13.4
37,999.7
Net
Benefits
2,260.4
3.2
9,128.8
External costs
6,135.6
540.0
5,197.9
External benefits
663.3
0.9
2,653.4
Net social
CBA
-3,211.9
-535.9
6,584.3
Lusaka
Southern
Kafue Basin
Total
1,149.4
1,498.6
14,333.94
61,462.52
926.8
1,208.3
11,557.44
49,557.18
222.6
290.3
2,776.50
11,905.34
946.7
3,664.5
11,286.9
16,484.8
267.6
267.6
-456.4
-3,106.7
1,199.5 -7,310.9
3,852.84 -726.6
Source: own calculation based on data.
The analysis clearly shows that the aggregate net social benefits (private benefits plus external benefits) derived from using chemicals in cotton production are less than estimated total social costs (private costs plus external costs) that society incurs as a result of applying pesticides and herbicides on cotton
34
fields. However, results are different across the five provinces in the sample, but only cotton production in Eastern Province seems to pass the extended cost-benefit criteria with net social benefits being positive. This means that cotton production is only welfare enhancing in Eastern Province and this is largely on account that disease incidence associated with chemicals and consequently the estimated health costs are disproportionately lower than in the other four provinces. It important to note that this conclusion should be applied and interpreted with caution as the data used to estimate health costs need further scientific verification and as such only provides a minimum measure of external costs these chemicals impose on health and the environment. This limitation notwithstanding, the following key commentaries can be derived from this analysis:
The results show that when health effects of exposure to chemicals are quantified and incorporated into the CBA framework and internalized by the farmer, the decision criteria changes—it is no longer profitable for the cotton farmer to increase the use of pesticides and herbicides as was the case when only private cost and benefits were considered in the CBA or in the optimization section above where results recommended an increase in the use chemical to boost productivity and income.
Although government generates tax and non-tax revenues from the importation of these chemicals into the country and from licensing chemical handlers amounting to K3, 852.8 million, these revenues only represents 23.4 percent of the estimated short-term occupational health costs associated with exposure to chemicals by cotton farmers and their workers.
Furthermore, since health services are generally free in all rural areas and all illnesses are reported and treated in government owned facilities, the government directly bears the estimated medical expenses incurred in treating illness rising from agro-chemical exposure. It is estimated that government revenues (tax and fees) only account for 57.4% of estimated medical costs in the five provinces covered in the study. It is therefore in government’s interest to streamline the use of hazardous agro-chemical in cotton production in a manner that reduces government health expenditure and increases yields and incomes of cotton farmers.
These results also show that the current revenue instruments based on the “polluter pays principle” are largely inadequate to raise the necessary revenue to finance the provision of health services to those affected by these chemicals. This means that both the licensing regime
(regulatory instruments) and taxes (economic instrument) will need to be reviewed in order to ensure that health externalities are internalized and sufficient revenue is raised to address any consequent health and environmental impacts.
The fact that the farmer has free access to health services increases his net private benefit from the use of these toxic agro-chemicals thereby by enhancing his incentives to intensify their application per hectare. This raises important policy implication for both the management of health services as well as management in the country. For instance, government should consider raising chemical as well as license fees if necessary to ensure that enough revenues are raised to support the treatment of illness due to exposure to these chemical and to finance other
35
chemicals management program chemical discharged in the nature environment. Proposals of how these reviews can be made are discussed in the section below.
Smallholder farmers need to be sensitized about the occupational health effects these chemicals have on the appliers, their families as well as their environment impacts and measures taken to induce them to internalize these costs in their cotton production decisions;
It is important to emphasis that the Cost of Illness Approach only provides a minimum estimate of the health impacts associated with the use of organophosphate pesticides and herbicides on cotton farms, and no long-term health effects have been captured in the estimate. In addition, the data used is not based on scientifically verified health incidence and therefore the monetary estimates of health impacts are more illustrative than indicative of the exact magnitude of the health burden. Furthermore, no onsite or off-site environmental impacts have been incorporated in the valuation of external costs associated with use of pesticides and herbicides in cotton production in the sampled provinces. Given the data limitations, the findings and conclusions highlighted in this study should be interpreted and used more cautiously.
5.
Conclusion and Policy Recommendations
The Cost of Illness Approach is one of the several environmental economics valuation techniques used to estimate environmental health liabilities and has been used in this study and the literature to value health effects of exposure to agro-chemicals (Maumbe and Swinton, 2003;). To value the contribution of the pesticides and herbicides to cotton productivity, a market-based crop productivity technique was adopted and applied to available market data on yield and input and output costs, complimented by data derived using a Benefit Transfer technique. Using this data, the optimal and profit maximizing level of pesticides and herbicides was computed, with taking into account the adverse health costs of exposure to chemicals.
The analysis shows that farmers can actually increase yields and returns/income from cotton production by increasing the quantity of pesticides and herbicides applied per hectare. This observation is consistent with theory and empirical results in the literature that excluding external costs associated with pesticides, application will tend to over-state the productivity effects of pesticides and herbicides used in cotton production thereby leading to sub-optimal policy prescription to increase chemical use per hectare. Should such a policy prescription be adopted as optimal, proper chemical management and mitigation effects need to be initiated in order to avert or reduce external health costs associated with treating illnesses associated with chemical poisoning. It is important that the Government and cotton merchants through their cotton out-grower schemes should help smallholder cotton farmers to increase yield per hectare at much lesser health and environmental impacts. This can be accomplished among others by:- h) providing targeted farm extensions services covering farm management approaches and technologies, sound chemical management including better spraying techniques and chemical handling guidelines,
36
i) providing clear chemical regulations, enforcing stricter chemical labeling requirements and clear guidelines for the placement of hazardous agro-chemicals on the domestic market, j) supporting technological innovation and development, including bio-technology engineering projects that are capable of producing more pest resistant cotton varieties (i.e. use of Bt cotton varieties) and thereby reducing dependence on the use of pesticides to control pests on cotton fields and ; k) developing and implementing an integrated pest management program that is both costeffective and able to increase cotton yields per hectare without substantially increasing dependence and use of hazardous pesticides and herbicides on cotton fields and consequently production and income of smallholder cotton farmers (also see Cole, 2007).
These interventions have the potential to raise yields per hectare by 50-100 percent for most smallholder farmers and would almost double current production and income levels of rural cotton farmers in a more sustainable function. At the macro-level, this would also help greatly to reduce rural poverty and significantly contribute to meeting the millennium development goals on poverty, health and environmental protection.
The failure by government to initiate sound policies and programs within its development programs to promote productivity and sound management of chemicals in the agriculture sector and cotton production in particular is expected to induce unacceptably high economic, social and environmental losses in the medium to long-term. Therefore the potential for improvement in cotton production and productivity by current estimates is quite substantial, meaning that inadequacies in government policies towards actualizing the sector’s growth potential would induce significant economic and environmental losses to the national economy in the medium to long-term. Increased production and utilization of class
I and II organophosphates, which are highly toxic to human health and the environment is also costly in the long-term and its use should be effectively regulated and controlled especially in cotton production.
The foregone increase in yield per hectare from an average of 600-800kg per hectare for most farmers to 1413 kg per hectare that can be realized as demonstrated by DUNAVANT’s YIELD program should be interpreted as losses attributed in part to inadequate government policy actions in the sector.
While the productivity approach was used to generate information needed on the benefit-side of the
CBA framework, the Cost of Illness Approach was used to deliver estimates of the occupational health impacts (costs) associated with exposure to toxic agro-chemicals by cotton farm households and workers. In particular, the Cost of Illness Approach was used to generate fairly reasonable estimates of medical costs of treating illnesses arising from exposure to agro-chemicals, computation of the opportunity cost of getting sick estimated in terms of lost wages and income, and the estimation of other costs associated with seeking health care services.
Medical expenditure incurred to treat agro-chemical poisoning cases and other related illness in 2008 is estimated at K6, 174.5 million using data on five main cotton producing provinces, which produces over
80 percent of the country’s production of seed cotton. The income foregone by farmers and workers when they fall sick is estimated at K8, 427.4 million and other associated costs at K1, 342.9 million.
37
These estimates suggest that the government directly bears 41 percent of the total estimated occupational health costs associated with the use and exposure to pesticides and herbicides from cotton fields. The other costs indirectly affect the level and contribution of labor income to the GDP.
Given that government provides free health care services in rural areas, the medical costs for treating illnesses (K6, 174.5 million in 2008) are therefore borne by the government, and exceed the total revenues collected on these chemicals through license fees and taxes on chemical imports. This has two critical policy implications a) that the current set of taxes and fees imposed on chemicals is inadequate to cover the provision of health services to affected households and b) that the government should review economic and regulatory instruments to ensure that enough compensatory revenues are collected and support optimal utilization of chemicals in the agriculture sector. Regulating and providing clear guidelines on placement of chemicals on the domestic market should be an important aspect of the mainstreaming program at the national level.
Another area of importance in the broader chemicals mainstreaming program relates to diagnosis, treatment and management of poisoning cases, and compilation of scientific information to enable more detailed analysis of the short, medium and long-term health effects of exposure to toxic agrochemicals. This information is invaluable in supporting economic analysis and the use of cost-benefit analysis to evaluate alternative government policies and programs in the areas of chemicals management, and in the evaluation of environmental and health impacts of these chemical in the economy.
6.
References
Ajayi Oluyende O.C. (2000), “Pesticide Use Practices, Productivity and Farmers Health in Cote D’Ivoire”,
Pesticide Policy Project Special Publication Series, Faulty of Horticulture, University of Hannaver.
Brambilla I, Porto G (2006), “Farm Productivity and Market Structure: Evidence from Cotton Reforms in
Zambia”, World Bank Policy Observer paper 3904, World Bank.
Cole Donald C, Stephen Sherwood, Myriam Paredes, Luz Helena Sanin, Charles Chrissman Patricio
Espinosa, Fabia Munoz (2007), “Reducing Pesticide Exposure and Associated Neurotoxin Burden in an
Ecuadorian Small Farm Production”, International Journal of Occupational Environmental Health, 13 (3)
July/Sept.
CSO (various years) Post Harvest Survey Data, CSO database, Lusaka.
38
FAO database, www.fao.org/
Guillette Elizabeth A, Maria Mercedes Meza, Maria Guadalupe Aquilar, Alma Delia Soto, and Idalia
Enedina Garcia (1998) “An anthropological approach to the evaluation of preschool children exposed to
pesticides in Mexico”, 106 number 6 pp1-8
Haggblade and Tembo Gelson (2003), “Conservation Farming in Zambia”, EPTD Discussion paper, IFPRI,
Washington DC.
Karunarathna Darren M Ayanthi, Nick A Buckley, Gamini Manuwera, M.H Rezvi Sheriff and Micheal
Eddleston (2003), “Influence of Pesticides Regulation on Acute Poisoning Deaths in Sri Lanka”+. Bulletin of the World Health Organization, 81(11).
MoFNP (2009), Economic Report, Government printer, Lusaka.
Mumbe Blessings M; Swinton M Scott (2003), “Hidden Health Costs of Pesticides Use in Zimbabwe’s
Smallholder Cotton Growers”, Social Science and Medicine, 57 pp1559—1571.
Nguyen Huu Dung and Tran Thi Thanh Dung (2003), “Economic and Health Consequences of Pesticide use
in Paddy Production in the Mekong Delta”, Vietnam, Research report, IDRC, Otawa.
PANNA (2010): Problems with Conventional Cotton Production. www.panna.org
(dated 21/02/10)
Qaim M an De Janvry A (2005) Bt Cotton and Pesticide Use in Argentina: Economic and Environmental
Effects, Environment and Development Economics, 10, p179—200.
Tschirley David, Balland Zulu, James Schaffer (2004), “Cotton in Zambia: An Assessment of its
Organization, Performance, Current Policy Initiatives and Challenges for the Future”, working paper No.
10, Food Security Research Project, Lusaka.
Winchester Paul, Jordan Hustins and Jun Ying (2009), “Agricultural Chemicals in Surface Water and Birth
Defects in the United States”, Acta Paediatrica, 98 pp664—669.
7.
Appendix
39
Table 10: Determining the Optimal Use of Pesticides and Herbicides in Cotton Production in Zambia
1,413kg/ha
Quantity
1,280 kg/ha
Value
Value US$ Quantity US$
990kg/ha 700 kg/ha value
Quantity Value US$ Quantity US$ profit max level pesticides optimal
(profit max) current chemical use level
Increase use of pesticides by (%) herbicides optimal use current herbicide use level
Increase use of herbicides by (%)
2.04
1.26
61.7%
1.80
1.00
80.1%
79.81
49.34
45.04
25.00
1.85
1.26
46.5%
1.63
1.00
63.2%
72.30
49.34
40.80
25.00
1.43
1.26
13.3%
1.26
1.00
26.2%
55.92
49.34
31.55
25.00
1.01
1.26
-19.9%
0.89
1.00
-10.8%
39.54
49.34
22.31
25.00
Quantity value US$
0.72
1.26
-42.8%
0.64
1.00
-36.3%
500kg/ha
28.24
49.34
15.94
25.00
Table 11: Production, hectare cotton planted, and value of cotton production
Total quantity of pesticides applied on cotton farms in packs
40
1997/8
Zambia
Central
Eastern
Southern
72,560,478
12,257,940
49,505,651
9,318,152
Lusaka
Copperbelt
17,525
1,207,575
Value of total cotton production US $
2000/1
61,000,000
10,304,981
41,618,313
1,323,356
1,015,040
14,640
2001/2
78,208,000
13,211,999
53,358,771
1,696,673
1,301,381
18,770
2002/3
77,600,000
13,109,287
52,943,953
1,683,483
1,291,264
18,624
2003/4
96,000,000
16,217,675
65,497,673
2,082,659
1,597,440
23,040
2004/5
113,850,000
19,233,149
77,676,147
2,469,904
1,894,464
27,324
2005/6
121,068,000
20,452,515
82,600,753
2,626,494
2,014,572
29,056
Zambia
Central
Eastern
Southern
Lusaka
13,060,886
2,206,429
8,911,017
1,677,267
3,155
13,725,000
2,318,621
9,364,121
297,755
228,384
17,596,800
2,972,700
12,005,724
381,751
292,811
Copperbelt 217364 3294 4223
Benefit analysis of pesticide use in Zambia using provincial statistics (US $)
17,460,000
2,949,590
11,912,389
378,784
290,534
4190
Zambia
Central
Eastern
6,182,198
1,044,384
4,217,912
6,496,547
1,097,488
4,432,382
Southern
Lusaka
793,912
1,493
140,938
108,103
Copperbelt 102,886 1,559
Total hectares cultivated using 1280kg per/ha yield
Zambia
Central
Eastern
10,204
1,724
6,962
10,723
1,811
7,316
8,329,212
1,407,088
5,682,750
180,697
138,598
1,999
13,748
2,322
9,379
8,264,460
1,396,149
5,638,572
179,292
137,521
1,983
13,641
2,304
9,307
Southern
Lusaka
1,310
2
233
178
298
229
296
227
Copperbelt 170 3 3 3
Total hectares cultivated using 780kg per/ha yield in the 2007 economic report, 2008
Zambia
Central
Eastern
Southern
Lusaka
Copperbelt
16,745
2,829
11,424
2,150
4
279
17,596
2,973
12,005
382
293
4
22,560
3,811
15,392
489
375
5
22,385
3,782
15,272
486
372
5
21,600,000
3,648,977
14,736,977
468,598
359,424
5184
10,224,074
1,727,195
6,975,553
221,805
170,129
2,454
16,875
2,851
11,513
366
281
4
27,692
4,678
18,894
601
461
7
25,616,250
4,327,458
17,477,133
555,728
426,254
6148
12,125,113
2,048,345
8,272,570
263,047
201,762
2,910
20,013
3,381
13,654
434
333
5
32,841
5,548
22,407
712
546
8
27,240,300
4,601,816
18,585,170
590,961
453,279
6538
12,893,835
2,178,209
8,797,044
279,724
214,553
3,095
21,281
3,595
14,520
462
354
5
34,923
5,900
23,827
758
581
8
Table 12: Estimates of Costs of Pesticides and Herbicides Use In Cotton and Their Productivity Effect (In US $)
2006/7
116,000,000
19,596,357
79,143,022
2,516,547
1,930,240
27,840
26,100,000
4,409,180
17,807,180
566,223
434,304
6264
12,354,090
2,087,027
8,428,793
268,014
205,572
2,965
20,391
3,445
13,912
442
339
5
33,462
5,653
22,830
726
557
8
Total quantity pesticides (packs) and herbicides(litres) used in cotton production
1997/8 2000/1 2001/2 2002/3
Zambia 10,204 10,723 13,748 13,641
2003/4
16,875
2004/5
20,013
2005/6
21,281
2006/7
20,391
2007/8
24,258
2007/8
138,000,000
23,312,908
94,152,906
2,993,823
2,296,320
33,120
31,050,000
5,245,404
21,184,404
673,610
516,672
7452
14,697,107
2,482,843
10,027,357
318,844
244,560
3,527
24,258
4,098
16,550
526
404
6
39,808
6,725
27,159
864
662
10
41
Central
Eastern
Southern
Lusaka
1,724
6,962
1,310
2
1,811
7,316
233
178
Copperbelt 170 3
Total quantity herbicides in litres used in cotton production
2,322
9,379
298
229
3
Zambia
Central
Eastern
255,095
43,094
174,043
268,066
45,286
182,893
343,688
58,061
234,487
2,304
9,307
296
227
3
341,016
57,609
232,664
Southern
Lusaka
32,759
62
5,816
4,461
7,456
5,719
7,398
5,675
Copperbelt 4,245 64 82 82
Total cost of pesticide application on cotton farms in three main provinces in US $
Zambia
Central
Eastern
Southern
Lusaka
Copperbelt
399,786
67,537
272,761
51,340
97
6,653
399,786
67,537
272,761
51,340
97
6,653
399,786
67,537
272,761
51,340
97
6,653
399,786
67,537
272,761
51,340
97
6,653
2,851
11,513
366
281
4
421,875
71,269
287,832
9,152
7,020
101
399,786
67,537
272,761
51,340
97
6,653
Total cost of herbicides application on cotton farms in three main provinces in US $
Zambia 4,584,915 4,818,047 6,177,210 6,129,188 7,582,500
Central
Eastern
Southern
Lusaka
Copperbelt
774,549
3,128,138
588,791
1,107
76,304
813,932
3,287,196
104,524
80,172
1,156
1,043,541
4,214,509
134,011
102,789
1,483
1,035,429 1,280,943
4,181,745 5,173,293
132,969
101,990
1,471
164,498
126,173
1,820
3,381
13,654
434
333
5
500,317
84,521
341,350
10,854
8,325
120
399,786
67,537
272,761
51,340
97
6,653
8,992,371
1,519,118
6,135,202
195,084
149,633
2,158
3,595
14,520
462
354
5
532,037
89,879
362,992
11,542
8,853
128
399,786
67,537
272,761
51,340
97
6,653
9,562,480
1,615,429
6,524,169
207,452
159,120
2,295
3,445
13,912
442
339
5
509,766
86,117
347,796
11,059
8,483
122
399,786
67,537
272,761
51,340
97
6,653
9,162,188
1,547,806
6,251,062
198,768
152,459
2,199
4,098
16,550
526
404
6
606,445
102,449
413,758
13,156
10,091
146
399,786
67,537
272,761
51,340
97
6,653
10,899,844
1,841,355
7,436,608
236,465
181,373
2,616
Table 12 continued ………………..
Total cost of pesticides and herbicides used in cotton production (US $)
1997/8 2000/1 2001/2 2002/3 2003/4 2004/5 2005/6 2006/7 2007/8
42
Zambia
Central
Eastern
Southern
Central
Eastern
Southern
Lusaka
Copperbelt
4,984,701
842,086
3,400,899
640,131
Lusaka
Copperbelt
1,204
82,957
87,163
1,257
111,752
1,612
110,883
1,599
137,175
1,978
Net-productivity benefits of use of pesticides and herbicides in Cotton production in Zambia in US $
Zambia 1,197,497 1,258,387 1,613,375 1,600,833 1,980,412
202,298
817,013
153,781
289
19,929
5,238,161
884,904
3,573,826
113,639
212,584
858,556
27,300
20,940
302
6,715,837
1,134,534
4,581,997
145,696
272,554
1,100,753
35,001
26,847
387
6,663,627
1,125,714
4,546,376
144,563
270,435
1,092,196
34,729
26,638
384
8,243,663
1,392,636
5,624,382
178,841
334,559
1,351,170
42,964
32,954
475
Annex
Table 13:Cotton Productivity Estimates (Cote D'Ivoire) In CFA 000
Cost/value
Mean (long history of growers)
Mean (short history of growers)
Output/ha 206.85 146.19
Average
176.52
9,776,468
1,651,579
6,670,166
212,094
162,680
2,346
10,396,289
1,756,288
7,093,049
225,541
172,994
2,495
9,961,092
1,682,768
6,796,129
216,100
165,753
2,391
11,850,265
2,001,914
8,085,050
257,084
197,188
2,844
2,348,644
396,766
1,602,404
50,952
39,081
564
Input/out na
2,497,547
421,921
1,703,995
54,183
41,559
599
2,392,997
404,259
1,632,664
51,915
39,819
574
2,846,842
480,929
1,942,307
61,760
47,371
683
43
Fertilizer/ha
Labor
35.54
83.61
28.43
69.57
31.99
76.59
Herbicides ka/ha 10.47 3.21 6.84 0.05
Insecticides kg/ha
Source Ajayi, 2000.
20.23 21.6 20.92
Table 14: Pesticide Use and Productivity among Cotton Farmers in Cote D’Ivoire
0.10
Production function estimates Marginal Value Productivity
Short history
0.17
0.40
Intercept
Fertilizer
Labor
Herbicides
Insecticides
Long history
5.34
0.22
-0.02
0.02
0.40
Short history
2.73
0.16
0.31
0.03
0.46
Long history
1.366
0.05
0.46
3.286
0.932
0.05
1.12
2.04
Table 15: Pesticide Use and Productivity among Cotton Farmers in Argentine (1999/2000)
Bt All conventional plots
Only small farms Large farms
Only bt adopter
Number of sprays
Amount of insecticide (ka/ha)
Active ingredients(kg/ha)
2.14
1.85
0.64
3.74
2.43
1.15
3.02
1.41
0.69
4.75
3.88
1.79
Yield raw cotton(kg/ha) 2032 1291 1111 1546
Table 16: Trends in trade revenues collected on imported chemicals (kwacha)
4.51
4.15
1.9
1537
Items
Industrial chemicals
Disinfectants
Fungicides
Herbicides
Pesticides and Insecticides
2005
1,432,298,986
1,359,781,824
610,254,990
449,066,424
3,039,041,476
2006
4,154,651,443
1,921,138,776
1,420,131,840
651,695,039
7,912,603,325
2007
22,684,636,937
1,581,676,043
169,291,665
10,505,429
3,434,148,843
2008
26,389,639,345
1,593,632,282
130,274,332
5,799,053
2,540,860,158
2009
20,559,539,620
2,126,746,936
61,941,127
31,133,674
2,702,880,406
44
Total
Item
Industrial chemicals
Disinfectants
Fungicides
Herbicides
6,890,445,705
20.8%
19.7%
8.9%
6.5%
16,060,222,429
25.9%
12.0%
8.8%
4.1%
Pesticides and Insecticides
Total
44.1%
100.0%
Taxes and fees collected on pesticides and herbicides used in cotton production (k millions)
49.3%
100.0%
27,880,260,924
Percentages
81.4%
5.7%
0.6%
0.0%
12.3%
100.0%
Taxes on imports of pesticides and herbicides*
License fees and charges on pesticides and toxic substances
Total
2005
2,790
1,118
3,909
Note: *=*80% of all pesticides used in cotton production.
2006
6,851
1,371
8,223
2007
2,756
1,644
4,400
30,660,207,178
86.1%
5.2%
0.4%
0.0%
8.3%
100.0%
2008
2,037
1,864
3,901.8
25,482,243,772
80.7%
8.3%
0.2%
0.1%
10.6%
100.0%
2009
2,187
1,748
3,935
45
