Risk Analysis and Management in Rainfed Rice Systems

8/8/2019 Risk Analysis and Management in Rainfed Rice Systems

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Risk Analysisin Rainfed

and ManagementRice Systems

S. Pandey, B.C. Barah, R.A. Villano, and S. Pal, Editors

IRRI


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Contents

Introduction

Units of measurement

Risk and rainfed rice: some conceptual and methodological issues

S. Pandey

Risk and rainfed rice in India: an overview

C. Ramasamy and K. Uma

Decomposition of income variability in rainfed areas: the case of rice

in eastern India

B.C. Barah

Labor use and employment pattern in rainfed rice-producing states of India

G.K. Chadha

Crop insurance: a policy perspective

P.K. Mishra

The nature and causes of changes in variability of rice

production in eastern India: a district-level analysis

S. Pandey and S. Pal

Growth and variability in agriculture revisited: district-level evidence of

rice production in eastern India

S. Pal, S. Pandey, and Abedullah

Rainfed rice and risk-coping strategies: some microeconomic

evidence from eastern Uttar Pradesh

S. Pandex H.N. Singh, and R.A. Villano

Risk and the value of rainfall forecast for rainfed rice in the Philippines

Abedullah and S. Pandey

Characterizing risk and strategies for managing risk in

flood-prone rice cultivation in Assam

B.C. Bhowmick, S. Pandey, R.A. Villano, and J.K. Gogoi

Risk and its management in the rainfed rice ecosystem of Bihar

J. Thakur

Risk and its management in rainfed rice ecosystem of West Bengal

N.K. Saha, S.K. Bardhan Roy, and U.S. Aich

Risk and rice production in Orissa, eastern India

D. Naik, S. Pandey, D. Behura, and R.A. Villano

Risk and rice technology design

L.J. Wade

Participants

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components of farming systems (not just that of

rice), and that help stabilize area (not just yield)

are seen as important for reducing risk. As such

technologies will require farmers to use

information on weather and other factors that

condition crop performance, provision of such

information is seen as an important risk-

reducing strategy. Similarly, dissemination of

such risk-reducing technologies that tend to be

somewhat information-intensive will require

reform of the extension system that is designed,

in most countries, for delivering simple

technology packages.

risk-related literature in the context of rainfed

rice farming in India. While recognizing the

importance of climatic risk, they emphasize risks

associated with the timely supply of inputs and

with prices. The review also indicates that most

of the literature on the study of farm level risk in

rice production in India is somewhat dated.

Perhaps a rapid growth in productivity realized

through the adoption of modem varieties and

other related technologies in irrigated areas

during the Green Revolution period diverted

attention from problems facing rainfed areas.

However, with the increasing attention now

being paid to rainfed rice systems of easternIndia, interest in issues of risk and its

management has come once again to the

forefront. Ramasamy and Uma identify

important areas that require increased research

attention to develop risk-reducing interventions.

variability can be an important source of revenue

variability of rice farmers. Using the method of

variance decomposition, Barah finds that price

variability is more important than yield

variability in irrigated environments, but that theopposite holds true in the rainfed environment.

The implication is that the design of price policy

should vary according to the environmental

conditions including basic infrastructure. Of

course, this poses the challenge of how to design

a differential price policy in an environment

where spatial economic linkages are growing

stronger. Yield-stabilizing and -enhancing

measures such as biotic and abiotic stress-

tolerant varieties and insurance against

Ramasamy and Uma provide an overview of

The paper by Barah shows that price

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calamities are preferred policies particularly in

the poorly endowed regions.

employment in income diversification and,

consequently, in risk management of farm

households in India. Using rural employment

data for India, the author shows that expansion

of rural nonfarm employment has helped

farmers manage risk better. In addition,

expansion of nonfarm rural industry will directly

promote economic growth by better use of

forward and backward linkages associated with

agricultural growth. Improvements in

infrastructure and agrarian reforms are seen as

important policy interventions needed to

stimulate the growth of nonfarm employment in

rural areas.

Mishra discusses the issues related to crop

insurance as a policy response for stabilization

of crop income. Although the current wisdom is

that publicly funded crop insurance programs

are financially unviable, the author finds the

total social benefit of the comprehensive crop

insurance scheme in India to be higher than the

total cost. Thus, from the social point of view,

the author shows that crop insurance programs

can be desirable, even though they may not be

financially viable without the subsidy. Nevertheless, the author suggests that

opportunities for improving the financial

performance of crop insurance schemes should

be exploited as much as possible to improve

their financial viability. The problems of

adverse selection and moral hazard, however,

continue to erode the viability of crop insurance

schemes.

Two papers (Pandey and Pal, and Pal et al)

have focused attention on assessing the pattern

of changes in productivity and variability ineastern India using district-level data. The

authors report a diverse pattern of change with

variability increasing in some districts,

decreasing in others, but remaining more or less

unchanged in most. Eastern Uttar Pradesh and

West Bengal are the two states where the change

in variability (defined by the CV of production)

has been stabilizing. This is attributed mainly to

the availability of supplemental irrigation that

reduced risk and encouraged farmers to adopt

Chada discusses the role of nonfarm rural


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yield-increasing modem varieties. Parts of

Bihar and Orissa, on the other hand, experienced

an increase in production variability. The

coefficient of variation of district-level yield was

found to be positively related to the coefficient

of rainfall and negatively related to the quantity

of fertilizer used. As the latter is an indicator of

the extent of adoption of improved technologies,

the stability consequences of the adoption of

improved technologies appear to have been

favorable in eastern India. The findings of these

two studies support the view that productivity

gains in parts of eastern India have been

achieved without increasing instability. This

indicates that growth and stability are not

necessarily incompatible goals. The expansion

of irrigation and development of rice varieties

suitable to the environment of eastern India are

likely to be the causal factors that have reduced

instability and increased growth simultaneously.

However, there is an underlying trend toward an

increasing correlation in production across

districts that, if unchecked, could have a

destabilizing effect.

The Pandey et al paper analyzes risk

management strategies using panel data from

two villages with contrasting risk profiles in

eastern Uttar Pradesh. Diversification andmaintenance of flexibility are seen as two major

strategies for reducing risk. The analysis of

panel data permitted the authors to document

changes in cropping patterns, varieties of rice

grown, methods of crop establishment, and input

use over time and relate these changes to

rainfall. The paper shows that area variability is

an important component of variability in rice

production. Most of the biological research on

rice ignores area variability and focuses on yield

variability. One important contribution of this paper is that it shows that the risk benefits of

stabilization of rice yield in the study villages

are quite small. This is mainly due to a very

small share of rice in the total household

income. As a result, stabilization of rice income

will not necessarily translate into stabilization of

total household income. As farmer income

sources are already diversified away from rice,

rice research can have more impact by focusing

on yield improvement rather than on yield

stabilization per se. However, in other areas

where income diversification opportunities are

constrained by infrastructure and biophysical

factors, stabilization of rice yield can result in

substantial income gains.

The paper by Abedullah and Pandey

provides an estimate of the economic value of

rainfall forecast to rainfed rice farmers in the

Philippines. This is the only paper in the

volume that includes data from outside India.

Using a decision-theoretical approach, the

authors estimate the value of three types of

seasonal rainfall forecast (average, below

average, and above average). The economic

value of forecasts arises from farmers being able

to alter crop management practices if they have

access to the forecasts. To get around the

problems related to forecast accuracy, the

authors estimate the economic value of a perfect

forecast as such estimates provide the upper

limit to the value of a forecast. The estimated

value of such a forecast was found to be 1% of

the net returns from rice. For the rainfed rice

area of the Philippines, the total value was

estimated to be $6.6 million per year.

et al, and Naik et al) analyze rice production

and instability in Assam, Bihar, West Bengal,

and Orissa, respectively, the major rice- producing states of eastern India. Although rice

is grown in three different time periods in

eastern India, the papers show that rainfed rice is

grown mainly in July to November and the

variability of total rice production is determined

mainly by the variability during this period. The

Bhowmick et al paper highlights the importance

of flood risk in Assam. Overall, the variability

of rice production in Assam has changed very

little. The results from Bihar show that its

variability of rice production and yield is thehighest of all eastern Indian states. The

interaction between modem varieties and

complex hydrology in Bihar is probably the

main reason for increased production variability

in this state. Highly variable environmental

conditions could also be a reason for the

shrinkage of rice area in this state. The Saha et

al paper on West Bengal provides a more

detailed description of changes in productivity

patterns in West Bengal and how farmers

manage risk by adjusting crop management.

Four papers (Bhowmick et al, Thakur, Saha

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West Bengal is the only state with the least

variation in productivity over time. The paper

shows that low variability has resulted mainly

from stabilization of rainy-season rice

production even though the importance ofsummer rice has grown over time. The Naik et

al paper analyzes the instability of rice

production in Orissa. The paper shows that the

variability in rice yield across districts is not

related to the adoption of modern varieties but

mainly to soil/climatic factors. In the case of

Orissa, opportunities to reduce risk by

manipulating crop management practices of rice

seem circumscribed by hydrological factors,

especially in the coastal belt.

From a biological perspective, the Wade paper brings out clearly opportunities to reduce

risk through a better understanding of the

genotype by environment interactions.

Experimental data and crop simulation are seen

as important in understanding the nature of risk

and its management through manipulation of

varieties and crop management. The paper also

provides some insights into the more

downstream aspects of technology adaptation

and dissemination through farmer participatory

methods and involving nontraditional extension

agencies such as farmer organizations and

nongovernment organizations.

Summary and synthesis

The papers presented during the workshop and

the discussions that ensued covered many issues

related to agricultural growth in eastern India

and risk management. While most of the papers

used the concepts and methods that were

popularized when the study of interaction

between agricultural risk and technologyadoption was popular during the late 1970s and

1980s, the findings reported during the

workshop provide new insights into conceptual

and research issues. Some of these major issues

are summarized below.

Yield stabilization

Rice production is an important economic

activity in eastern India. Despite its importance,

the share of rice in the total household income

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may not be as high as often believed. If rice

contributes to only a small proportion of the

total income of farm households, interventions

that stabilize rice income are not necessarily

effective in stabilizing total household income.Thus, yield-stabilizing technologies for a single

crop are not likely to be effective in reducing

risk, even for an important crop such as rice.

However, in areas where income diversification

is limited due to limited infrastructure or less

favorable agroclimatic conditions, the economic

cost associated with instability in rice production

can be substantial. Thus, there is a need to

delineate rainfed rice environments in terms of

the current extent of income diversification and

the possibilities for diversification in the future.Interventions designed mainly for stabilizing

yield and incomes (such as technologies with

higher yield stability and crop insurance) are

likely to be less useful in environments with

ample opportunities for diversification.

Naturally, technologies that improve the average

yield of rice are always important, irrespective

of the nature of the environment.

Data needs

One of the major gaps in the current empirical

work on risk analysis in the context of rainfed

rice systems is the lack of information on the

relative importance of various risk-coping

mechanisms and how they change with

increasing commercialization of agriculture.

Very little information is available on the

determinants of various strategies that farmers

employ to cope with risk and their associated

cost. One of the difficulties has been the lack of

panel data to study the correlation between

climatic fluctuations and farmers responses.Village-level studies such as the ones conducted

by the international Crops Research Institute for

the Semi-Arid Tropics (ICRISAT) can be

important in bridging the information gap for

rainfed rice systems also.

Risk and externality

Most of the current studies on risk for rainfed

rice areas focus on the farm or household as the

unit of analysis. Little information is available


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at a higher level of aggregation such as the

community, district, or state. Often, the source

of increased risk in downstream areas may be

the result of inappropriate land use in the

upstream areas. For example, deforestation andthe use of soil-eroding practices in the upper

parts of a watershed can increase the flood risk

in downstream areas. Such risks are better

managed through the use of more sustainable

land use systems in the upper slopes than by

other means. In this regard, collective

institutions can play an important role in the

management of overall risk for the whole

watershed. However, the study of interactions

between collective institutions and risk

management remains a relatively unchartedterritory.

Macroeconomic instability and risk

Another area of research that deserves adequate

attention is the effect of macroeconomic

instability on risk in agriculture. As agriculture

changes from subsistence to commercial

orientation, the agricultural sector becomes more

prone to macroeconomic shocks of fluctuations

in foreign exchange rates and interest rates.With the anticipated trend toward globalization

of trade following the WTO agreement, the

macroeconomic shocks are likely to be

transmitted more easily across countries.

Policies and institutional mechanisms needed to

stabilize food production and farmers income

under such conditions are yet to be adequately

scrutinized.

Upscaling and extrapolation

A methodological issue relates to the

quantification of the impacts (production losses,

income, and welfare) of unpredictable shocks at

different geographic scales. Farm-level losses

can be quantified through a sample survey and

other traditional methods. However,

geographically referenced spatial information on

factors that affect production losses are needed

to extrapolate such information to the regional

and subnational levels. Such databases are now

becoming increasingly available with the

growing popularity of geographic information

systems (GIS). Nevertheless, methods and

approaches are needed to upscale farm level

effects of risk to a higher level of aggregation.

Economic value of forecasts

Opportunities for reducing the economic cost of

risk by providing of forecast information have

not been adequately addressed in the agricultural

sector worldwide, except for some specific high-

value crops. In most developing countries,

climatic forecasts are rarely available in a form

useful to farmers for planning agricultural

operations. Similarly, opportunities for reducing

risk through the better use of information of

forecast prices are rarely available.Policymakers and farmers alike have not

adequately appreciated the importance of

information acquisition and use for risk

management. Perhaps the value of information

is low in traditional subsistence-oriented

agriculture. But with increasing

commercialization, the use of forecast

information can be an important strategy for

reducing price and weather risk.

Risk analysis of rainfed rice systems

In rainfed rice areas, the overall theme of risk

analysis and management has not been

adequately studied. Parallels are drawn from

earlier work conducted in irrigated areas where

the interaction between risk and technology

adoption was widely discussed. While

theoretical and conceptual advances made in

such studies are relevant to rainfed environments

also, empirical applications for analyzing

farmers decisions regarding the choice of

technology, their risk-coping strategies, and the

overall effect of farmers risk management

strategies on production instability at the

aggregate level have been far too few. Due to

the high degree of heterogeneity in rainfed

environments, the domain of a specific

technology is likely to be much narrower. A

careful delineation of rice production

environments, based on risk profile and farmers

socioeconomic conditions, is needed to target

technology development. With the availability

of more powerful computer technology, many


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powerful tools ranging from crop simulation to

GIS are now in the hands of analysts. More

studies that use such tools to quantify effects of

risk at the production systems level are needed.

Increasing the adoption of modem varieties ofrice even in rainfed areas and the growth in

income of farm households observed during the

1990s in eastern India indicate that farm

households have been able to mitigate the effect

of risk to a certain extent.

S. Pandey*

B.C. Barah

R.A. Villano

S. Pal

*Sushil Pandey and Renato Villano are agricultural economist and assistant scientist, respectively, at the

International Rice Research Institute, Los Baos, Laguna, Philippines; B.C. Barah and S. Pal are principal

scientist and senior scientist, respectively, at the National Centre for Agricultural Economics and Policy Research

(NCAP), New Delhi, India. The editors acknowledge support and encouragement from Dr. Mahabub Hossain, head,

Social Sciences Division, IRRI; Dr. D. Jha, national professor and exdirector, NCAP and Dr. Mruthyunjaya, director,

NCAP. Editorial assistance provided by Dr. Bill Hardy, Ms. Teresita Rola, Ms. Millet Magsino, Ms. Erlie Putungan,

Mr. Juan Lazaro IV, and Mr. George Reyes of the Communication and Publications Services, IRRI, is gratefully

acknowledged.


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Units of measurement

All data on rice and production in this report are expressed in terms of rough rice. The conversion

factor used is 1 kg of rough rice = 0.66 kg of milled rice.

All monetary values for studies in India are expressed in Indian rupees. In 1997, the exchange

rate was 1US$ = Rs 36.31.

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Risk and rainfed rice: some conceptual andmethodological issues

S. Pandey

The study of risk and its interaction with technology is an important topic in agricultural

development. The paper provides a review and synthesis of conceptual and empirical issues

in risk analysis in the context of rainfed rice farming. Various strategies employed by farmers

for managing risk are discussed and implications of these strategies for designing and

disseminating technologies are derived. Methodological and measurement issues that require

further development are highlighted.

Rainfed rice farmers, like farmers everywhere,

have to carry out production activities in an

inherently uncertain environment. Production is

affected by drought, flood, and pests and

diseases, which occur in an unpredictable way.

In addition, farmers income and welfare also

depend on uncertainty related to economic

parameters such as price and marketing.

Efficient management of risk is hence the

essence of rainfed agriculture. For poor farmers,risk considerations may loom large in their

choices of crops and the method of production.

Hence, a study of how farmers are likely to

respond to technological and policy

interventions in the face of risk is critical in

designing these interventions.

Definition and measurement of risk

Although the word risk is used in all walks of

life to describe the chances of some undesirable

outcome, defining it precisely and

unambiguously is not easy. This is reflected in

the following statement: Risk is like love; we

have a good idea of what it is, but we cant

define it precisely (Stiglitz as quoted in

Roumasset et a1 1979). The Macquarie

Dictionary defines risk as exposure to the

chance of injury or loss. As injury or loss is

a subjective concept with its consequence

depending on the person as well as the

circumstance, what is considered to be risky by

one individual may not be seen to be so by

another person. Risk, hence, is subjective.

It is essential to draw a clear distinction

between risk and variability. The latter term

merely implies that a variable of interest is not

fixed but has different values. No risk is

involved if the value of the variable can be

known with certainty. For example, farm size

may vary from farmer to farmer but can be

known with certainty. Similarly, soil type withina farm can vary from paddock to paddock but

can be known with a fair degree of certainty.

Uncertainty about the likely values, not the

variability per se, is the source of risk.

A notion such as risk that is intrinsically

subjective obviously cannot be measured by an

objective indicator. Subjective probability

distribution of an uncertain outcome of concern

to the decision maker, hence, is considered a

suitable indicator of risk. Under this definition,

risk can be measured by (1) the chance of an

undesirable outcome, (2) the variability of

outcome (or the converse of stability), and (3)

the probability distribution of outcome. The first

measure implies that a situation in which the

chance of an undesirable outcome is greater is

riskier. Although intuitively appealing, the

measure is problematic because it is not clear

when an outcome is unacceptable.

distribution-suchas the variance and the

coefficient of variationhave found common

Statistical descriptors of the probability

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use as a measure of risk. However, Rothschild

and Stiglitz (1970) showed that none of the

statistical descriptors adequately measure risk.

They contend that it is impossible to devise a

universally valid statistical descriptor of risk

without simultaneously considering both the

probability distribution of outcomes and the risk

attitude of the decision maker.

Given these difficulties in devising the

adequate measure of risk, it has been argued that

the ambiguous terms more risky or less risky

should be avoided. If no satisfactory measuring

scale exists, then it is not possible to consider

risk as being more or less. What is

theoretically appealing is to view a decision as

being risk efficient or risk inefficient. Such

decisions may lead to an increase in the mean

income and/or a reduction in the dispersion of

income around the mean. Risk efficiency can be

best ascertained by comparing the whole

probability distributions of the uncertain

outcomes that correspond to different decisions.

Risk and its impact on technologyadoption

The impact of risk and risk aversion on the

choice of agricultural production techniques andinput use has been a topic of extensive

investigation (Feder et al 1985, Anderson and

Hazel1 1994). Theoretical studies on farmer

behavior under risk indicate that, in the absence

of a perfect market for insurance, resource

allocation for risk-averse farmers differs from

that for risk-neutral farmers (Sandmo 1971,

Anderson et al 1977).

The effect of risk is considered to depend on

risk perceptions and risk attitudes. Farmers may

be reluctant to adopt technologies that they perceive to be riskier. Risk perception depends

on the quality of the information they have and

their information- processing capabilities. To the

extent that farmers perceive a technology to be

riskier than it actually is, activities such as on-

farm research to generate more accurate

information and investment in educating farmers

are warranted.

Assuming farmers perceptions of risk

associated with a technology to be reasonably

accurate, whether adoption occurs also depends

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on risk attitudes. Variability of income is

irrelevant to risk-neutral farmers. A technology

that generates a higher level of mean income

would be preferred by such farmers. However,

risk-averse farmers are likely to consider

simultaneously both the level of income and risk

and to reject a technology that they consider too

risky.

Empirical evidence indicates that farmers in

developing countries are generally risk-averse

(Binswanger 1980, Walker and Ryan 1990). If

poorer farmers are more averse to risk, rainfed

rice farmers who are mostly poor are likely to be

reluctant to adopt technologies that increase risk.

In addition to this direct effect, risk aversion also

indirectly affects technology adoption through

its impact on the credit market (Binswanger and

Sillers 1983). Risk-averse lenders may demand

greater collateral and may charge a higher

interest rate, depressing credit use by poorer

farmers. Similarly, more risk-averse farmers are

less likely to demand credit. To the extent that

credit use is essential for adoption of

technologies that require purchased inputs (such

as fertilizers), risk aversion discourages

technology adoption. This indirect effect of risk

aversion is often considered to be more

important than the direct effect (Binswanger andSillers 1983).

The study of risk basically consists of two

aspects: risk analysis and risk management

analysis. Risk analysis consists of the study of

the nature, magnitude, and sources of risk and

how technology affects these characteristics.

Risk management, on the other hand, involves

the use of methods that reduce risk and its

impact. Even if a technology is risky, farmers

may adopt it if adequate means for diffusing risk

are available.Risk could be studied at the micro (or farm)

level or at the macro (region or nation) level.

The purpose of farm-level analysis is to study

adoption decisions in the face of risk.

Macroeconomic parameters are assumed to be

given and farmers responses to risk are studied.

In the case of macro analysis. the purpose is to

study the implications of fluctuating production

for food security at the regional or national level.

Although farmers may adopt improved

technologies because they are profitable, the


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instability of production at the aggregate level

may increase as a consequence. Appropriate

technological and policy interventions are

required to reduce such adverse effects on food

security.

Sources of risk

Income of farmers from agricultural production

can fluctuate as a result of fluctuations in yield,

price of output, area planted, price of input, and

input supply. Agricultural scientists are mainly

concerned with yield risk as it is often a major

component of risk, especially under rainfed

conditions. If a farmer is ultimately interested in

reducing the uncertainty of income (as income

and consumption in rural societies are highlycorrelated), other components of risk can also be

important. For example, a negative correlation

between the price of rice and yield tends to

stabilize the income from rice compared with a

situation when these two variables are positively

correlated. Hence, if the interest is in stabilizing

farmers incomes, it is necessary to evaluate the

consequence of price instability and how it

affects income stability. Evaluation of

technology in terms of instability of yield alone

will not be adequate. The importance of price

uncertainty is likely to increase as rice

production systems become more

commercialized.

income risk, especially in commercialized

systems. When the use of purchased inputs is

minimal and output is mainly for subsistence,

market prices are less relevant for resource

allocation by farmers. However, in

commercialized systems where traded inputs are

substituted for nontraded inputs and output is

mainly for the market, fluctuations in prices of both inputs and outputs can have a major impact

on farmer welfare.

A negative correlation between price and

yield is an important feature of agricultural

production, which helps in stabilizing farm

income. Prices tend to be high when production

is low and they tend to be low when production

is high. Stabilizing prices in this situation can

actually raise farm income instability. Market-

level analyses and the study of price policy are

Price risk is an important component of

required to help design price policies that

enhance farm income stability.

can lead to fluctuations in output as farmers

adjust input levels to prevailing conditions.

Marketing infrastructure is important indetermining input supply risks. Similarly,

government policies on production and

marketing of agricultural inputs determine input

supply and price risks.

Variability in input supply and input prices

Risk at the aggregate level

Even if aggregate food production is increasing,

wide fluctuations in total supply can seriously

affect food security at the household level,

especially that of the poor. The nature of public-sector interventions in the food market depends

on the instability of aggregate production. The

economic costs of maintaining a bigger stock of

food grain to deal with higher instability can be

substantial. In addition, the effect of instability

at the national level also spills over to

international markets and can cause wide swings

in prices, thus affecting food security in other

countries also. Analysis of the patterns of

instability in food grain production is hence

relevant in the context of food security.

of improved technology consisting of high-

yielding varieties (HYVs) and associated crop

management practices has increased food

production in Asia over the last 20 years. What

is still debatable is the effect on variability of

production. The adoption of modern varieties

and improved crop management techniques can

make aggregate production more variable by

increasing interregional correlation. When

farmers grow similar varieties and use similar

management practices, adverse climaticconditions over a large area can lead to a large

drop in production. Similarly, when the supply

of major inputs is unreliable and/or input prices

change, farmers are likely to adjust their input

use in the same direction, leading to covariate

movement in output. This economic response

can lead to increased production instability even

if the yield of modem varieties is more stable

than that of traditional varieties.

It is now widely accepted that the adoption

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Empirical evidence shows that production

variability in the aggregate has increased with

the adoption of improved varieties in India

(Hazell 1982). Much of the increase in

production variability in food grains has been

attributed not to the adoption of improvedvarieties per se but to fluctuations in input

supply such as irrigation (due to power outages)

and fertilizers (Hazell 1982). On the other hand,

Walker (1989) found the adoption of HYVs of

sorghum and pearl millet to be a major factor

contributing to increased production variability

of these crops. Using district-level data from

India, Singh and Byerlee (1990) found that

variability in wheat yield, measured by the

coefficient of variation, has decreased over time,

mainly as a result of expansion in irrigated area.Rao (1968), Mehra (1981), and Pandey (1989)

have also discussed the effect of irrigation on

production variability.

In a more recent study covering three major

cropsrice, wheat, and maizeNaylor et al

(1997) found that the variability in global output

of rice and wheat, measured by the average

percentage deviation from the trend, increased

initially with the adoption of modem varieties

but then declined subsequently. The probability

of a significant shortfall below the trend also

decreased between the pre-

and post-Green

Revolution periods for these two crops. In the

case of maize, production variability was higher

during 1980-94 than during 1950-64. The

dominance of U.S. maize production in global

output and a greater downside sensitivity of

yield to climatic conditions when yields are

close to the ceiling have been attributed to the

increase in variability in global maize

production. Although the rapid growth in yield

of rice and wheat may have swamped the

increase in Variability during the GreenRevolution period, instability may be more

pronounced in the future as yield ceilings for

these crops are also approached. More evidence

on the aggregate effects of technology adoption

is contained in papers by Pal et al (2000) and

Pandey and Pal (2000).

4

Coping mechanisms of farmers

As a result of their exposure to risk, farmers

have developed various strategies over time to

avoid the negative consequences of

unpredictable variations in agricultural output. Agood understanding of these strategies is needed

to assess the likely responses of farmers to new

technologies or policies. Uptake of technologies

that complement and reinforce farmers coping

strategies is likely to be quite rapid. On the other

hand, interventions that undermine key

components of risk management strategies are

likely to be rejected.

Farmers risk-coping strategies can be

classified into ex ante and ex post, depending on

whether they help reduce risk or reduce theimpact of risk after a production shortfall has

occurred. Because of the lack of efficient

market- based mechanisms for diffusing risk,

farmers modify their production practices to

provide self-insurance so that the chances of

negative consequences are reduced to an

acceptable level. Ex ante strategies help reduce

fluctuations in income and are also referred to as

income-smoothing strategies. These strategies

can be costly, however, in terms of forgone

opportunities for income gains as farmers select

safer but low-return activities.

categories: those that reduce risk by

diversification and those that do so through

greater flexibility. Diversification is simply

captured in the principle of not putting all eggs

in one basket. The risk of income shortfall is

reduced by growing several crops that have

negatively or weakly correlated returns. This

principle is used in different types of

diversification common in rural societies.

Examples are spatial diversification of farms,diversification of agricultural enterprises, and

diversification from farm to nonfarm activities.

Maintaining flexibility is an adaptive

strategy that allows farmers to switch between

activities as the situation demands. Flexibility in

decision making permits farmers not only to

Ex ante strategies can be grouped into two


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reduce the chances of low incomes but also to

capture income-increasing opportunities when

they do arise. Examples are using split doses of

fertilizers, temporally adjusting input use to crop

conditions, and adjusting the area allocated to a

crop depending on climatic conditions. While postponing agricultural decisions until

uncertainties are reduced can help lower

potential losses, such a strategy can also be

costly in terms of income forgone if operations

are delayed beyond the optimal biological

window.

shortfall in consumption when family income

drops below what is necessary for maintaining

consumption at its normal level. They are also

referred to as consumption-

smoothing strategiesas they help reduce fluctuations in consumption

even when income is fluctuating. These include

migration, consumption loans, asset liquidation,

and charity. A consumption shortfall can occur

despite these ex post strategies if the drop in

income is substantial.

strategies in different combinations to ensure

survival. Over a long period of time, some of

these strategies are incorporated into the nature

of the farming system and are often not easilyidentifiable as risk-coping mechanisms. Others

are employed only under certain risky situations

and are easier to identify as responses to risk.

Ex ante coping mechanisms

Ex ante coping mechanisms are designed to

exploit low correlation among activity returns

for stabilization of total income. These operate

through various types of diversification that

characterize traditional agriculture.Diversification may be considered as horizontal

or vertical. The former refers to scattering of

agricultural fields, growing of several crops,

growing of several varieties of the same crop,

and engaging in different income-generating

activities. The latter relates to spreading

agricultural operations over time. This refers to

strategies such as staggered planting, spreading

input use over a period of time, planting many

seeds per hill, and temporally diverse planting.

Ex post strategies are designed to prevent a

Farmers who are exposed to risk use these

Vertical diversification is a way of maintaining

flexibility to adjust agricultural operations to the

evolving uncertainty. Similarly, share cropping

is viewed as a way of reducing risk through

sharing of risk between the landlord and the

tenant.Spatial diversification of fields. Agricultural

fields vary from location to location in attributes

such as soil moisture retention and fertility. In

rainfed areas, these soil characteristics can vary

widely even across fields. Similarly, rainfall

distribution can also vary among fields in

different locations. These variations in soil

characteristics and rainfall across locations

create an opportunity for fanners to stabilize

agricultural output through spatial scattering of

fields. Although output from fields in onelocation can decrease because of poor rainfall, it

can increase in fields in other locations that

receive higher rainfall. Weakly or negatively

correlated crop yields across fields result in

these compensating movements so that total

farm output is more stable than output from

individual fields. Spatial scattering of fields is a

way of exploiting this stabilizing effect. In

addition, this strategy may also help farmers to

better exploit specific niches of different

microenvironments to enhance productivityenhancement. In spite of these potential gains,

spatial diversification of fields can cause an

efficiency loss because of the increased costs of

moving inputs across and marketing outputs

from widely separated fields. Whether or not

farmers use spatial scattering depends on the net

effect of these factors. In addition, local

institutions such as the inheritance law may

condition the prevalence of such a practice.

In rice-growing regions of Asia, it is not

uncommon to find a farm household operatingseveral parcels of land that are either spatially

scattered or differ in their location along the

toposequence. While risk considerations may

have played a role in determining the extent of

land fragmentation, casual observation indicates

that land fragmentation is driven mainly by the

desire to exploit different environmental niches

that are suitable for different crops. In parts of

eastern India, ail parcels of land are divided

among legal heirs so that everybody gets an

5


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equal share of all types of environmental niches.

The desire for an equitable distribution of land

of different quality among heirs is often

considered to be a factor constraining efforts at

land consolidation.

If land fragmentation is an effective way ofreducing risk, one would expect to observe a

greater degree of fragmentation in areas where

environmental conditions are less stable.

However, such a pattern may not be observed

due to other counteracting factors. For example,

the extent of fragmentation in the more risky

Sahel region of Africa has been less than in the

more favorable Sudan region (Matlon 1991).

This is attributed to the differences in

environmental factors in these two regions. In

the Sahel, low rainfall prevents farmers from

cultivating a wider range of field types. As a

result, cropping is restricted to only certain field

types where crop success is more assured. In

Sudan zone, higher rainfall and generally better

soil conditions enable farmers to use a range of

field types. In this example, the lack of feasible

alternatives in the highly constraining

environment of the Sahel reduced the value of

spatial diversification as a risk management tool.

Even if the inheritance law may have a big

role in determining farm size and extent of

fragmentation, farmers can and do alter their

land resource base through land rental markets.

Field experience from eastern India indicates

that tenants with a given endowment of land

types prefer to rent a different land type. Renting

a better quality land increases average income. It

may also simultaneously achieve the objective

of risk reduction.

diversification, farm output can be stabilized by

growing several crops with poorly or negatively

correlated yields. Environmental conditions lessfavorable to some crops may be more favorable

to others, so that compensating variations in

yields of different crops would impart stability

to total output. In addition to risk reduction,

there are several other potential benefits of crop

diversification, such as a better exploitation of

environmental niches, staggering of labor

demand, and meeting the demand for a range of

outputs. Mixed cropping and intercropping,

Crop diversification. As with spatial

6

which are a common feature of traditional

agriculture in Asia, are a form of crop

diversification that reduces output variability

(Walker and Jodha 1986, Siddiq and Kundu

1993). Crop diversification, however, can also

be costly in terms of income gain forgone asfarm households include crops with lower but

more stable yields in their cropping pattern. In

addition, economies of size that may result from

specialization are also lost as production is

diversified.

Crop diversification is a feature of

traditional farming systems in Asia. The role of

crop diversification in risk reduction has been

analyzed extensively in the context of farming in

the semiarid tropics where farmers grow a range

of intercrops and mixed crops. Crop

diversification has been greater in the more risky

environments in the semiarid tropics of India

(Walker and Jodha 1986). In the rainfed rice

environments of eastern India, crop

diversification is greater in areas with a less

assured supply of irrigation (Pandey et al, this

volume). Crop diversification in flood-prone

areas in a village in eastern India declined after

dikes for protection from flood were constructed

(Ballabh and Pandey 1999).

Although diversification may reduce

instability, whether or not farmers are able to

diversify land use also depends on the

environmental conditions. Again taking the

example from Africa, low and unstable rainfall

and poor soils in the Sahel have constrained

opportunities for diversification, with the millet-

based cropping pattern being the dominant one.

In comparison, in the relatively favorable

environments of the northern Guinea zone, the

cropping pattern is more diversified (Matlon

1991). In addition, the more limited cropping

opportunities in the Sahel also mean that cropyields are likely to be highly correlated, thus

reducing the benefits from crop diversification.

In the humid environments of Asia, drainage

constraints in the submergence- prone bottom

land similarly limit opportunities for crop

diversification during the rainy season.

Varietal diversification. Growing several

varieties of a crop is a form of diversification

that can stabilize the total output of the crop if


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yields of different varieties are poorly correlated.

Varieties with different duration can reduce risk

by avoiding period-specific risk, For example,

short-duration varieties can escape terminal

drought that can severely affect the yield of a

longer duration variety. Similarly, varieties withdifferent degrees of tolerance for pests and

diseases also help reduce losses.

invariably grow several varieties for different

reasons, including possible risk reduction. In a

rainfed rice village in Orissa, more than 70% of

the farmers grow two to five varieties, with 20%

of the farmers growing six to eight varieties

(Kshirsagar et a1 1997). Similarly, in the rainfed

lowland of Lao PDR, 60% of the farmers grow

four or more rice varieties (Pandey and

Sanamongkhoun 1998). As with crop

diversification, other advantages of varietal

diversification are niche matching, staggering

labor demand, and generating a range of product

characteristics. These latter motives are not

directly related to risk management and may

condition the extent of varietal diversification

practiced by farmers in a given area.

Income diversification. Like crop

diversification that uses weak correlation among

activity returns to stabilize farm income,

diversification of income from farm to nonfarmsources is another way of stabilizing income. If

fluctuations in nonfarm incomes are independent

of fluctuations in farm output, income

diversification through one or more members of

the family working in the nonfarm sector can

stabilize total family income. The extent of

income diversification may depend on factors

such as rural education, transportation

infrastructure, access to institutional credit, and

availability of local resources for nonfarm

activities. These factors may constrainopportunities for income diversification even

when agricultural risk is high. In areas with

environmental conditions conducive to a strong

agricultural base, income-generating activities

that take advantage of agricultures forward and

backward linkages expand. On the other hand,

income diversification in agriculturally poor

areas tends to be outward-looking, with

households diversifying their income

Rainfed rice farmers in eastern India almost

geographically (Reardon et a1 1988, 1992).

on risk and efficiency implications of share

cropping exists (Newbery and Stiglitz 1979,

Otsuka et a1 1992). At their very basic, share

cropping arrangements that lead to sharing ofinput and output also lead to sharing of risk

between the landlord and the tenant. However,

the existence of share cropping depends on

many other factors in addition to risk benefits

(Otsuka et a1 1992).

Temporal adjustments. Crop growth is a

biological process that occurs over a period of

time. The economic output is obtained upon

maturity when the crop is finally harvested. The

crop is exposed to various factors during the

intervening period between planting and harvest.

Some of these factors are known with a fair

degree of certainty, wheras others are highly

uncertain. These factors, together with

management interventions by farmers, determine

the ultimate economic value of the crop output.

Uncertainties are highest at planting time as

future values of uncertain events are known very

imprecisely. As uncertainties are resolved with

the passage of time, farmers can gain by making

decisions conditional on the occurrence of

uncertain events up to that time and the revised

expectation about the future occurrence ofuncertain events. Such a sequential decision-

making process imparts flexibility and allows

farmers to exploit favorable events for income

gains while reducing potential losses.

To assess the value of sequential decision

making, it may be useful to divide the cropping

season into early, middle, and late stages. The

early stage can be considered to include

preplanting and the period immediately after

planting. The major decisions to be made at this

stage are the crops, the variety, the timing of planting, and the method of establishment. The

middle stage is considered to be the period

between successful crop establishment and

flowering. Major decisions here are weeding,

fertilization, control of pests, and irrigation. The

final stage is the period after flowering until

harvest.

may determine the choice of crops. If rains are

Share cropping. A large volume of literature

The rainfall pattern during the early stage

7


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low or delayed during this period, farmers may

forgo rice completely and expand the area under

crops that require less water. Similarly, if too

much water is received, farmers may expand the

area under rice at the expense of other crops. In

eastern India, sown area of rice contracts inyears with low and unstable early-season rainfall

(Pandey et al, 2000a). If the crop fails to

establish itself because of too much or too little

rain, farmers may decide to replant. Farmers

similarly may engage in gap filling and thinning

to reduce risk (Singh et al 1995).

The choice of what rice variety to grow also

depends partly on the nature of rainfall during

this early period. Farm-level data from eastern

India indicate that, in years with late rains,

farmers expand the area under short-

durationvarieties as a mechanism for escaping terminal

drought. Expanding the proportionate area under

traditional varieties and resorting more to dry

seeding as opposed to transplanting are other

responses exhibited by farmers in eastern India.

Once the crop is successfully established,

farmers may adapt the level of input they use,

depending on their assessment of crop health. If

the crop potential appears to be low, farmers

may leave some fields unweeded and apply

lower than normal quantities of fertilizer.

Surplus resources may be used for other crops in

the same or the following season. Farmers even

replant the area with some other crops if they

anticipate the rice yield being too low and if the

season has not advanced too far (Singh et al

1995).

During the third stage, most of the

uncertainties would have been resolved and few

decisions would remain to be made. If rice fails

during this stage, farmers may go for salvage

operations to obtain at least the byproduct

(straw, in the case of rice). Another responseobserved in eastern India is to establish post

rainy-season crops early in the rice field if soil

moisture conditions are favorable.

The temporal adjustments described above

are farmers mechanisms for reducing losses in

poorer years and increasing gains in more

favorable years. Relative to committing all

resources at the beginning of the cropping

season or on the basis of a fixed calendar, the

8

average farm income will always be higher

when flexible methods are adopted. However,

opportunities for using flexibility may be

constrained by farmers ability to process the

necessary information about crop status and the

likely future occurrence of uncertain events. Inaddition, in poorer and harsher environments,

flexibility may be so circumscribed that it cannot

be relied upon as an effective risk-coping

mechanism.

Ex post coping mechanisms

How do farmers cope with losses that do occur

despite the various risk-reducing mechanisms

adopted? The shortfall in agricultural

production will reduce consumption if farmersare not able to meet the deficit through some

other means. Depleting food and cash savings,

earning more wage income, borrowing,

liquidating assets, reducing consumption,

relying on charity, and permanent migration are

some of the mechanisms used for coping with a

production shortfall. The economic burden and

the long-term productivity impacts of these

mechanisms differ.

If farmers are able to save during better-

than-normal years and use the savings to meet

consumption deficits during drought years, they

may be able to maintain their consumption level

over time despite short-term fluctuations in

agricultural output. Savings in agricultural

societies may take various forms. They could be

held in the form of food grains, cash, and

jewelry. They could also be held in the form of

productive assets such as bullocks, farm

implements, and land. Even if own savings are

not enough to meet the consumption deficit,

village-level institutions may permit sharing of

risk across individuals such that individualconsumption fluctuates much less than

individual production.

developing countries indicates that consumption

smoothing is a common practice among farmers

(Townsend 1994, 1995). Based on data from the

International Crops Research Institute for the

Semi-Arid Tropics (ICRISAT), crop inventory

and cash reserves play major roles in smoothing

Empirical evidence from several studies in


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consumption in the semiarid tropics of India

(Lim and Townsend 1994, Paxson and

Chaudhuri 1994). The importance of these two

mechanisms varies by farm size, with large

farmers relying more on crop inventory and

small farmers relying more on currency. The useof credit was another important mechanism.

Results for Thailand were also similar

(Townsend 1995).

. The effectiveness of these mechanisms

depends on the seventy of risk such as drought

and crop output in the preceding year. Problems

are less severe in a year with mild drought that

follows a good year, and these mechanisms may

be adequate to meet the shortfall. These internal

reserves, however, may be grossly inadequate

when drought years are consecutive or if droughtis severe. In such situations, farmers may be

forced to reduce consumption, with small

farmers and landless labor suffering the most.

Based on farm-level data from arid and

semiarid areas of India, the decline in cereal

consumption in a drought year relative to a

normal year varied between 12% and 22%

(Jodha 1978). In addition, there were drastic cuts

in the expenditure on protective food such as

milk, sugar, vegetables, fruits, meat, and others.

Pandey et al (2000b) made similar observationsfor eastern India. Such shortfalls in consumption

point to the inadequacy of consumption-

smoothing mechanisms, especially among small

farmers.

Livestock, in addition to being useful for

agricultural production, are also an important

store of wealth in rural societies. They serve an

important role in consumption smoothing.

During drought years, livestock are sold and

proceeds are used to meet a consumption

shortfall. Disposal of livestock can also help

reduce carrying costs, which tend to be high,

especially during drought years (Kinsey et al

1998). In the Sahel zone of Africa, where poor

environmental conditions constrain the efficacy

of ex ante mechanisms, manipulation of

livestock inventory is an important ex post

mechanism (Matlon 1991). Farmers in India

similarly use the livestock inventory to reduce

consumption shortfall (Jodha 1978).

A problem with the use of livestock for

consumption smoothing is that this coping

mechanism, while helping farmers to survive

during drought years, can reduce the long-term

production potential. Where livestock are simply

a store of wealth, this will not create a problem.Disposing of livestock in this case would be

similar to withdrawing cash from the bank. In

fact, disposal of small animals such as goats and

sheep, which tend to be good stores of value, is

generally the initial response to income

shortfalls. However, livestock are also the major

source of draft power needed for several farm

operations such as tillage, pumping irrigation

water, threshing rice, and hauling farm inputs

and outputs. Faced with the prospect of a severe

shortage in consumption in a severe or prolonged drought, farmers may sell productive

livestock such as cattle, buffaloes, and horses.

Once these productive livestock assets are

depleted, it takes a long time for them to be

replenished. Thus, even after the drought is over

and rainfall returns to normal, it may take

several years for farmers to rebuild their stock of

livestock. A typical feature of the livestock

depletion-replenishment cycle is that livestock

are sold when their prices are falling due to

excess supply during drought years (Jodha1978). Increased demand during the

replenishment phase pushes the prices up,

making it more difficult for farmers to reacquire

the livestock. If several drought years occur in a

row, the livestock asset may be depleted so

severely that several years of normal conditions

would be needed for full replenishment of the

livestock. The effect of drought can thus linger

on for several years until productive assets are

fully replaced. As the mortality of livestock is

higher in drought years due to poor nutrition, the

asset base can be depleted dramatically during a

run of drought years. Thus, this coping

mechanism could be costly in terms of future

production potential forgone. The impact is

likely to be greater for small farmers than for

large farmers as small farmers often need a

longer time to replenish the depleted stock.

As with the depletion of livestock, severe

droughts can lead to excessive exploitation of

common property resources (CPR) that are a

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critical component of village livelihood systems

(Jodha 1986). The CPR are resources owned in

common by village residents. These include

community forests, pasture/waste land, ponds,

river banks and river beds, and groundwater. The

poorer segments of the rural population areespecially dependent on CPR, even in normal

times, to generate food, fiber, and income.

During drought periods, these resources become

even more important. For example, the reduced

supply of fodder during drought years increases

the reliance on forest and community grazing

areas for sustaining the livestock. Similarly,

additional incomes are generated by selling

timber, fuelwood, and other forest products.

Collection of edible forest products such as

fruits, nuts, and bamboo shoots also increases asfarmers attempt to meet the shortfall in

production. If these CPR are depleted

excessively during drought years, the

productivity of agriculture and livelihood of the

poor can be adversely affected for many years

even after the meteorological drought ends.

Short-term or permanent migration to earn

income from cities or far-away places is another

coping mechanism. Migration to nearby places

is likely to be less effective due to covariate

movements in income within a small geographic

area. Prospects for earning income within the

locality affected by drought are limited due to a

reduction in demand for labor in the agricultural

as well as nonagricultural sector. Employment in

far-away places or in sectors unlikely to be

affected by drought will have a stabilizing effect

as such income is less covariate with income in

drought-affected areas. In addition to seasonal

migration during drought periods, diversification

of earning with some family members working

permanently in cities helps smooth consumption.

A variant of this coping mechanism is the

marital relationship with families in far-away

places. Income transfers through this mechanism

have helped farmers in the semiarid tropics of

India to stabilize consumption during drought

years (Rosenzweig and Stark 1989). Similarly,

diversification of income from the farm to

nonfarm sector is a way of exploiting the low

covariance for income and consumption

stabilization. For example, the proportion of

10

income derived from nonfarm employment

outside the region has been higher in the riskier

Sahel zone than in the less risky Sudan zone of

Africa (Matlon 1991).

in smoothing consumption. Credit permits borrowing against future income potential to

meet a current consumption shortfall. In a

perfectly competitive market, the opportunity

cost of credit is equal to the interest on savings.

Hence, long-run consumption will not depend on

whether savings are used or credit is taken to

meet a shortfall in consumption in poor years. In

reality, credit markets are imperfect, with the

effective interest rate on credit being higher than

the interest on savings. Risk aversion among

lenders, the high transaction cost of serving alarge number of small farmers, and information

asymmetry between borrowers and lenders are

the major reasons for capital market failure in

developing countries (Binswanger and

Rosenzweig 1986). As a result, the use of credit

for consumption smoothing in developing

countries is limited, more so among small

farmers who are considered as high-risk

borrowers by formal credit institutions.

Despite a poorly developed formal market

for credit, the available evidence on the extent of

consumption smoothing indicates the presence

of informal institutional arrangements for risk

sharing in rural areas. These may be village-

level rice banks, local money lenders, mutual

self-help groups, interlocked credit and labor

markets, and social and family networks.

Income transfers (in cash or in kind) through

these informal arrangements can provide very

effective insurance, especially if the risk affects

only a few households (Jodha 1978, Ben-Porath

1980, Platteau 1991, Fafchamps 1992,

Townsend 1995). The provision of suchinsurance is believed to be one of the critical

functions of the family as an institution

(Rosenzweig 1988). Although very effective in

insuring poor households against a consumption

shortfall caused by life-cycle events such as

death or illness in the family, these mechanisms

are less effective in dealing with covariate risks

that affect everybody within the community.

Historical records of mass migration, starvation,

Credit can potentially play an important role


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and death attest to the failure of these informal

mechanisms when droughts are severe and

widespread. These informal arrangements that

characterize traditional rural societies also seem

to weaken considerably in the face of

commercialization and greater exposure to the

outside world (Jodha 1978).

Publicly sponsored relief programs are used

to deal with the failure of these ex post

consumption-smoothing mechanisms in the face

of large covariate risk. To the extent that food

insecurity is due to the lack of exchange

entitlements, these relief programs are designed

to transfer income to farmers in affected areas to

reduce consumption deficits and prevent

excessive asset depletion. The relief programs

generally take the form of income transfer/employment generation although direct food

distribution may also be a component when

drought is severe. Several authors (Corbett 1988,

Hay 1988, Dev 1996) have discussed the

strengths and weaknesses of various types of

relief programs.

Methods for risk analysis

One of the most widely applied models for

studying decision making under uncertainty isthe expected utility model (Anderson et a1

1977). Under risky situations, decision makers

are assumed to select options that maximize the

expected utility of probabilistic consequences.

To implement the model, it is essential to know

the decision makers attitudes toward risk and

the probability of various outcomes resulting

from an action.

Attitudes toward risk are captured in the

utility function that transforms monetary gains

and losses into utility. Risk analysis consists of

combining the subjective probability of

outcomes and the associated utility to identify

risk-efficient decisions.

Different methods are available for

estimating the utility function and the implied

risk aversion coefficient (Binswanger 1980,

Binswanger and Sillers 1983,Antle 1983). Two

popular specifications used in applied work are

the utility function with constant partial risk

aversion coefficient and the utility function with

constant absolute risk aversion coefficient.

Estimates of both types of risk aversion

coefficients have been derived for several

farming systems.

derived in at least two ways: using historical

data and using a predictive simulation model.

Both approaches have advantages and

disadvantages. While the use of historical data is

based on the assumption that the future will be

similar to the past, the use of a predictive

simulation model requires that the model

adequately mimic the real production system.

The use of a simulation model to predict the

consequences of changes in technical parameters

is becoming more popular (Muchow and

Bellamy 1991, Lansigan et a1 1997). Usingstochastic weather input to drive a suitably

validated simulation model, the probability

distribution of yield for a specific technical

intervention can be obtained. The distribution of

yield can then be transformed into the

distribution of economic variable (for example,

profit), which is then used for economic risk

analysis. Anderson and Hazell (I 994, Lansigan

et a1 (1997) have discussed the advantages and

disadvantages of using simulation models to

identify risk-efficient technologies.Once the utility function and probability

distribution of income are obtained, several

approaches could be used to identify risk-

efficient decisions. A popular integrative

approach is the use of whole-farm planning

models. Variants ranging from simple stochastic

budgets to discrete stochastic programming

models are available (Hardaker et a1 1991).

Other approaches include stochastic dominance

analysis, nonoptimizing simulation models, and

variants thereof.

The required probability distributions can be

Objective function specification

The consequences of an action must be assessed

in relation to the objective function or what

farmers would like to achieve from their fanning

activities. While the objective function may

include aspects such as quality of life, childrens

education, and level of leisure, analysts often

focus on economic criteria such as farm income

11


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and consumption. If the objective of a farmer is

to maintain a given level of consumption, the

utility function should be defined in terms of

consumption. However, as income and

consumption are highly correlated, income is

often used as a proxy for consumption. Farmers

derive income not only from rice but also from

several activities that include growing other

crops and nonfarm employment. Income from

rice is often a smaller component of their total

income, especially in the more marginal

environments. Even if rice production is low,

farmers may be able to maintain their

consumption level by obtaining additional

income from other sources. Thus, it is not

enough to evaluate rice technologies in terms of

how much additional income they can generatefrom rice. Income from all sources should be

considered simultaneously.

Risk benefit and rice research

What opportunities exist for rice research to

reduce fluctuations in income and consumption

of farmers? What is the size of the economic

benefit if rice yield and production could be

stabilized? Answers to these questions are

critical for designing suitable technological and policy interventions to reduce &he cost of risk.

define what we mean by cost of risk and

develop a device to measure it quantitatively.

For this, we use the expected utility model of

decision making. The model postulates that,

under risky situations, decision makers evaluate

decisions in terms of expected utility of income

and choose the action that maximizes the

expected utility. For risk-neutral decision

makers, the decision that maximizes the

expected utility is also the decision that

maximizes the expected income gain. A risk-

averse decision maker, on the other hand, would

be willing to sacrifice some income to avoid

taking risk. The cost of risk is the amount of

income sacrificed to protect or insure against

risk. Using the expected utility theory, the cost

of risk can be approximated as (Pandey et al

1999).

Before proceeding further, it is essential to

P= 0.5R [a2 Cr2 + 2 a (1 - a) g Cr Cy]

12

where P is the cost of risk (or risk deduction)

expressed as a proportion of mean income, R is

the coefficient of relative risk aversion, Cr is the

coefficient of variation (CV) of rice income, a is

the share of rice income in total income, Cy is

the CV of nonrice income, and g is the

correlation coefficient between rice and nonrice

income. The proportional risk premium

measured in this equation provides an estimate

of the cost of risk currently borne by farmers

relative to the situation in which the variability

of rice income is completely eliminated. As

there will always be some variability of rice

income that cannot be eliminated, the estimate

obtained from this equation can be considered an

upper bound value.

This indicates several ways through whichthe economic cost of risk can be reduced:

lowering the CV of income from rice, lowering

the ratio of rice income to nonrice income, and

reducing the correlation of rice income with

nonrice income. Stabilization of rice yield

through breeding and better crop management

can be an important research intervention. The

lowering of the share of rice to nonrice income

implies crop and income diversification. The

scope of technical intervention to achieve this

may be somewhat limited in the case of rainfedrice, as waterlogged conditions of the fields limit

other cropping options during the rainy season.

Farmers have other cropping alternatives only

during the postrainy season, provided moisture

is nonlimiting. Development of shorter duration

varieties in areas where the success of a

postrainy- season crop depends on how early it

is established can facilitate diversification of

crop income. Other options for encouraging crop

and income diversification are related to policy

interventions such as the development of road,

transport, and marketing infrastructure. These

policy interventions can also help reduce the

correlation between rice and nonrice income by

broadening the income base of rural households.

Technology and yield risk

Technical research can basically be classified

into two types: plant improvement and crop

management. Two risk-related issues involve


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plant improvement research. The first is the

issue of the extent to which improved varieties

are more or less risky than traditional varieties.

Plant breeders use stability analysis and other

forms of genotype x environment (G x E)analysis to assess the stability and adaptability of

alternative varieties versus the traditional check.

The analysis of G x E interactions has been a

topic of interest among plant breeders and

powerful tools and methods have been

developed (Cooper and Hammer 1996).

However, most of these analyses use some

statistical notion of stability for discriminating

among cultivars. Such analysis could be usefully

complemented by decision analytical tools such

as stochastic dominance analysis to explicitly

account for farmers risk aversion (Binswanger

and Barah 1980, Witcombe 1989).

The second is the issue of the extent to

which a combination of several varieties reduces

risk. Ample evidence shows that farmers grow

several varieties of rice simultaneously in

rainfed areas (Kshirsagar and Pandey 1995,

Pandey and Sanamongkhoun 1998). Although

there may be several reasons for doing so, risk

reduction through varietal diversification

appears to be an important one (Smale et al

1994). The strategy of varietal diversification

could potentially be used to reduce the overall

risk, even if modem varieties are less stable than

their traditional counterparts.

Crop management research, on the other

hand, is concerned with altering yield risk by

manipulating agronomic practices. Agronomic

manipulation can reduce the yield risk

associated with stress conditions such as

drought, flood, and pests. For example,

improved nutrient management may help reduce

risk by making plants more tolerant of stresssuch as drought as well as by helping them to

recover faster when the stress is relieved (Wade

et al 1999). Similarly, options may exist for

reducing risk by manipulating timing, placement

and quantities of inputs.

The study of the quantitative effects of input

management on risk remains a major field of

inquiry by agricultural economists, among

others. Production function specifications that

permit estimation of marginal risk effects have

been developed (Just and Pope 1979, Antle

1983). Such production functions have been

applied to derive the optimal allocation of

several inputs under risky situations. Although

attempts have been made to quantify marginal

risk effects of several inputs such as fertilizers,

irrigation, and pesticides in a range of

environments using such a framework, the

empirical analyses have often produced

somewhat inconsistent results (Roumasset et al

1989, Pandey 1989).

An important area of research in the context

of crop management technology is the effect of

uncertainty on input use in a dynamic context.

Instead of committing all inputs at the beginning

of the crop season, inputs are used sequentially,

with farmers revising the level of input use

depending on crop conditions and their

expectations regarding stochastic variables such

as prices and weather. Possibilities for such

dynamic adjustments of input use provide

flexibility for efficient risk management. In

addition, reliable forecasts of stochastic

variables such as weather can improve the

efficiency of resource allocation by reducing the

level of uncertainty (Byerlee and Anderson

1982, Abedullah and Pandey 2000).

Data needs

Farmers are concerned about risk that manifests

itself in the form of unpredictable fluctuations in

yield over time. To analyze risk and risk-coping

mechanisms, temporal data are hence required.

The generation of temporal data, however, is

expensive and time-consuming. Plant breeders

have partially got around this constraint in their

selection program by including several testing

locations to capture different environmentalconditions. Fortunately, this approach has

worked well in the past. However, this kind of

spatial substitute for temporal data is less useful

when analyzing farmers coping mechanisms

and in studying how risk influences resource

allocation over time through its effect on assets

of farm households. Spatial data are not of much

help in studying these dynamic elements. The

only extensive panel data that have been used

widely to study risk and many other aspects of

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the village economy is the village-level study

database generated by ICRISAT (Walker and

Ryan 1990). A similar kind of database covering

major cropping systems would certainly be very

useful for studying responses to risk in other

rainfed environments. As indicated in the paper

by Pandey et a1 (2000a), some progress is being

made in this direction.

Strategies for developing anddisseminating risk-reducingtechnologies

What are the implications of the above

discussion for developing and disseminating

risk-reducing technologies? Are the strategies

and institutional mechanisms likely to bedifferent from those now in place? Space

limitations preclude me from going into much

in-depth discussion on this topic, which by itself,

is very broad. Nevertheless, some comments on

this important topic are in order. Based on the

above discussion, it can be deduced that the

following features of technology help reduce

risk:

less input demanding;

lower degree of prior commitments of

inputs; technologies that improve flexibility of

decision making;

technologies that use information about

conditioning factors;

technologies that help stabilize area, not just

yield, as area variability can be an important

source of production variability; and

technologies that raise income by improving

the productivity of other components of the

farming systems (e.g., those that facilitate

crop diversification and intensification).

These considerations suggest the following

strategies for technology development,

adaptation, and dissemination for reducing risk:

Emphasis on technologies that reduce yield

losses in unfavorable years rather than those

that increase yield in favorable years only

(downside risk). However, trade-off between

yield gain and instability may be inevitable.

More emphasis on developing durable

14

resistance to pests/diseases and abiotic

stresses. Molecular techniques may have a

major role to play, especially when dealing

with polygenic traits.

complementary options in which each

component can also stand on its own.

Although productivity improvement can be

high when several components are

combined in the form of a package, such

packages also tend to increase risk. By

allowing farmers to pick and choose from a

complementary basket of options, such an

approach makes sequential adoption of the

most profitable (and least risky) components

possible.

More adaptive research and decentralized

regional testing for specific adaptation.

Specific technologies for each region

developed through adaptive research will

improve the suitability of such technologies

to their target domain and reduce risk. T

Risk Analysis and Management in Rainfed Rice Systems

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