Decision support system for ex ante cost-benefit assessment of new agro-technology in the context of...

82 Int. J. Sustainable Society, Vol. 3, No. 1, 2011

Copyright © 2011 Inderscience Enterprises Ltd.

Decision support system for ex ante cost-benefit assessment of new agro-technology in the context of climate change

S.M. Fakhrul Islam Department of Agricultural Economics, Bangabandhu Sheikh Mujibur Rahman, Agricultural University, Salna, Gazipur, 1706, Bangladesh Fax: +880 2 9205333 E-mail: [email protected]

Abstract: Intrusion of saline water and soil salinity is a major threat of climate change to agriculture. Rice is the most important crop affected by salinity in the coastal area of Bangladesh. The objective of this paper is to develop tools for ex ante cost-benefit assessment, with an integration of knowledge of farmers, industry experts and scientists to develop new salinity and drought resistant (SDR) transgenic rice variety. Total incremental benefits of introducing SDR transgenic rice to replace traditional varieties are expected to be $948 ha 1

yielding a net benefit of $934 ha 1. The introduction of transgenic SDR rice in Bangladesh would have sizable social benefits. The total amount of non-discounted economic surplus from producing SDR transgenic rice over 10 years is projected at US$188 million with a producer surplus of $114 million. The internal rate of return (IRR) is 34% with a net present value (NPV) of $117 million.

Keywords: DSS; decision support system; climate change; technology development; ex ante cost-benefit assessment.

Reference to this paper should be made as follows: Islam, S.M.F. (2011) ‘Decision support system for ex ante cost-benefit assessment of new agro-technology in the context of climate change’, Int. J. Sustainable Society,Vol. 3, No. 1, pp.82–106.

Biographical notes: S.M. Fakhrul Islam is a Professor in the Department of Agricultural Economics at Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh. He received his PhD in Agricultural Economics with major in Natural Resource Economics from the University of Philippines at Los Banos and Master’s degree in Agricultural Economics from Bangladesh Agricultural University. His research interests include economics of information and its utilisation to improve firm performance, natural resource and environment, climate change, livelihood adaptations, food security and agrotechnology development. He presented his research at several international conferences, including IAAE, CIMMYT and FAO. He also published several books and book chapters.

DSS for ex ante cost-benefit assessment of new agro-technology 83

1 Introduction

The primary purpose of a decision support system (DSS) is to assist decision makers (DMs), individually or in groups, rather than an entire organisation. This assistance allows for custom design of a system in which DMs can use the system interactively to build and more importantly, to change analytical models of the decision problem. Interactive use allows immediate changes in assumed parameters with rapid feedback, encouraging the learning process that is impossible when the DM has to wait an extended period of time for output. Therefore, interaction and support for transferring the results of the analysis to the DMs in a communicable, manageable, easy understandable and quick way are a central feature of any effective DSS.

A DSS for trade-off assessment provides an organising principle and conceptual model for the design and organisation of a technology generating research project to quantify and assess competing objectives in agricultural production systems. Input from stakeholders (i.e. the general public, policy makers and research administrators) is used to identify the critical dimensions of social concern that is criteria for assessment of the sustainability of the system. Based on these criteria, hypotheses are formulated as trade-offs between possibly competing objectives, such as higher agricultural production, social benefits and improved environmental quality.

A number of challenges face researchers and policy makers in implementing a new type of research for technology generation. Firstly, despite the widespread acceptance of the goal of sustainable agricultural systems, a scientific consensus is lacking on how the economic, environmental and public health impacts of agricultural technologies can be quantified and assessed. Analysis of these complex, interrelated issues raises difficult theoretical and methodological problems for researchers. Environmental, agricultural and health characteristics of farmers, farmland and farming technologies vary over space and time.

Second is the problem of spatial variability. Trade-offs associated with agricultural production systems can be defined across several dimensions at a point in time, and it can also be defined in one or more dimensions overtime. In evaluating the long-term productivity of a production system, economic and environmental indicators can be used to quantify the productivity and other attributes of a system overtime. These indicators may include measures of phenomena such as economic returns, soil salinity and the organic content in the soil. Measuring trade-offs in these dimensions require site-specific data and models. Because the climate change and environmental impacts of different production systems are generally site-specific, one production system may not have the same impacts in all environmental dimensions at all sites. Thus, any attempt to rank production technology needs to account for spatial variability in economic and environmental outcomes.

Intrusion of saline water and soil salinity is a major threat of climate change to coastal agriculture. In Bangladesh, more than 30% of the net cultivated land is in the coastal area. Of the 2.85 million ha of coastal and offshore lands, about 0.83 million ha of arable lands are affected by varying degrees of soil salinity. Rice is the most important crop affected by salinity during the summer season in the coastal area of Bangladesh. The Bangladesh Rice Research Institute is conducting research on developing a salinity and drought resistant (SDR) transgenic rice variety to overcome the problem of soil salinity and drought in the context of climate change.

84 S.M.F. Islam

The objective of this study is to develop DSS tools, with integration of the farmers’ knowledge, scientific knowledge and historic knowledge in a way to develop a new agro-technology and formulate policy decisions by:

utilising location specific data that are available to individual farmers accommodating information from local experts (industry experts and scientists) to make the decisions work for them allowing the addition of new requirements and/or parameters on which to base decisions may change as defined by local experts and/or the scientific community developing an analytical framework and carry out spreadsheet analysis.

2 Background

Trade-off assessment provides an organising principle and conceptual model for the design and organisation of a research projects to quantify and assess competing objectives in agricultural production systems (Crissman et al., 1998). Input from stakeholders (i.e. the general public, policy makers, scientists and research administrators) is used to identify the critical dimensions of social concern, that is, criteria for assessment of the productivity of the system. Based on these criteria, hypotheses are formulated as trade-offs between possibly competing objectives, such as higher agricultural production and improved environmental quality. Not all outcomes need to be trade-offs; win–win cases also can be accommodated. Once the key trade-offs are identified, researcher can proceed with project design and implementation and can identify the appropriate scientific disciplines to further design and implement the research needed to quantify these trade-offs. The next step, critical to quantifying trade-offs, is the identification of disciplinary models and data needed to quantify potential benefits and costs (Stoorvogel et al., 2001).

The most common approach for analysing the market level or aggregate welfare effects of agricultural research is the application of economic surplus analysis. Griliches (1958), Peterson (1967), and Schimitz and Seckler (1970) provide early examples of applying the economic surplus concept to ex post evaluation of agricultural research. There is a growing body of literature on use of economic surplus models for assessing impacts of agricultural research (e.g. Akino and Hayami, 1975; Alston et al., 1995; Karim, 2001; Miah and Hossain, 2001; Scobie and Posada, 1978; Siddique, 1985).

Three basic postulates used in this approach are: 1 that the competitive demand price for a given unit measures the value of that unit to

the demander 2 that the competitive supply price for a given unit measures the value of that unit to

the supplier 3 that when evaluating the net benefits accruing to each member of the relevant group

(e.g. nation) the benefits should be added without regard to the individual(s) to whom they accrue (Harberger, 1971).

When these assumption are valid, consumer benefits from consumption may be measured as the area beneath the ordinary demand curve and above the equilibrium price (as consumer surplus), and the area beneath the supply curve is a measure of total costs, so


changes in the net welfare of producers may be measured using the area above the supply curve and below the equilibrium price (as producer surplus). Considering possible trade-offs of technology replacement and underlying assumptions, the present study also used economic surplus analysis model for designing DSS tools for setting an ex ante costs-benefit assessment of new agro-technology in the context of climate change.

3 Methods

3.1 Agro-technology and trade-off analysis

A partial budget analysis technique was used for trade-off analysis for replacing traditional rice varieties by a SDR transgenic one. Based on farmers, scientists and industry experts’ opinions, possible changes in the use of inputs such as seed, fertiliser, irrigation, labour and yield were considered in a trade-off analysis. In addition, an economic surplus model was used for measuring potential benefits and costs of replacing a traditional variety with a transgenic one.

3.2 Economic surplus model for measuring benefit and costs

The basic approach of measuring economic surplus change due to introduction of a technology is illustrated in Figure 1. In this figure, the supply curve for a commodity under the original technology is denoted by S0 and the demand curve by D. The original price is P0 and the quantity supplied and demanded is Q0. Using above postulates, the consumer surplus from consumption of the commodity is equal to the triangular area ABP0 (the area beneath the demand curve less the cost of consumption). Similarly, the total producer surplus is equal to the triangular area P0BC (total revenue less the total costs of production as measured by the area under the supply function). The total surplus is equal to the sum of producer and consumer surplus, ABC, which is equal to the value of consumption (the area under the demand curve) minus the total cost of production (the area under the supply curve). Changes in producer, consumer and total economic surplus are measured as changes in these areas.

Introduction of a salinity and draught resistance (SDR) transgenic rice technology will shift the supply curve to S1, resulting in a new equilibrium price and quantity of P1and Q1. The change in consumer welfare (surplus, CS) from the supply shift is represented by the area P0BEP1 and the change in producer welfare (surplus, PS) is represented by the area P1EG minus the area P0BC. Consumers necessarily gain because they consume more goods at a lower price. In general, the net welfare effect on producers may be positive or negative depending on the supply and demand elasticities and the nature of the research-induced supply shift. This is so because there are two effects working in the opposite directions. The producers sell more goods, but they must sell at a lower price. Both costs and revenue are affected. Producer benefits are assured if costs fall and revenues rise. But under plausible conditions, in some cases (i.e. inelastic demand or a steeply sloped demand curve) revenue falls when supply increases. In addition, when supply shifts in a pivotal fashion against an inelastic demand, revenue falls faster than costs and producers losses are assured. The nature of the supply shift can clearly have important implications for the distribution of benefits. In Figure 1, the total or net welfare effect is equal to the sum of the changes in producer and consumer surplus, CBEG.

86 S.M.F. Islam

Figure 1 Illustration of economic surplus model

The working formula for estimating change in consumer surplus ( CS), producer surplus and total benefit from the research-induced supply shift for the closed economy case are as follows (Alston et al., 1995, p.211):

0 0CS (1 0.5 )P Q Z Zn (1)

0 0PS ( )(1 0.5 )P Q K Z Zn (2)

0 0TS CS PS (1 0.5 )P Q K Zn (3)

where K is the vertical shift of the supply function expressed as a proportion of the initial price, n is the absolute value of the elasticity of demand, is the elasticity of supply and Z = K /( + n) is the reduction in price.


3.2.1 Net present value

Net present value (NPV) of streams of research benefits and costs was calculated as:

1

NPV(1 )

nt t

tt

B Ct

where r is the discount rate, Bt is the calculated value for annual research benefits for tyears in the future, obtained by calculating total change in economic surplus for t years, Ct is the annual research costs expended t years in the future.

3.2.2 Internal rate of return

The internal rate of return (IRR) to research investment was calculated as the discount rate at which the NPV would be zero, that is

1

0(1 IRR)

nt t

tt

B C

3.2.3 Sensitivity analysis

Sensitivity analysis was conducted under alternative scenarios of increase in yield, input cost, supply elasticity and base yield, cost and supply elasticity.

Cost and return information, secondary data and adoption information were combined in an economic surplus model to project total economic benefits of developing SDR transgenic rice variety and their distribution by region within the country and to consumers, producers and seed sector. The costs of the research and product development (including meeting regulatory hurdles) were included along with the benefits in a benefit cost analysis of the public investment. Excel spreadsheets were used for estimating the economic surplus models.

4 Data

4.1 Collected data and sources

Considering the intensity of rice cultivation in the salinity affected coastal region of Bangladesh and vulnerability to climate change, Noakhali and Sathkhira districts were selected for field surveys for the present study. Data were collected from the farmers, scientists and industry experts. Three sets of questionnaires were used (Appendix). Primary data were collected from 60 farmers, 30 from each region. Data were also collected from five scientists and eight industry experts from the Department of Agricultural Extension (DAE), the Bangladesh Agricultural Development Corporation (BADC) and seed companies.

Data collected from farmers included various input costs such as seed, fertilisers, pesticides and labour. Data were also collected on crop varieties, sources of seeds, crop management practices, input and output prices, crop losses due to salinity problem and crop yields. From scientists, data were collected on potential regions for transgenic SDR

88 S.M.F. Islam

rice, expected yield changes if transgenic SDR rice were grown, changes in variable costs for transgenic SDR rice, time lags and costs for technology development and regulatory requirements. Data were collected from industry experts on preferred varieties, sources of seed, extent of crop losses due to salinity, expected extent and time path of adoption of transgenic SDR rice, time lags and costs for technology development and regulatory requirements. Secondary data on crop acreage and production were collected from district offices of DAE and various issues of Year Book of Agricultural Statistics of Bangladesh.

5 Results

5.1 Analysis with the economic surplus model

The main assumptions for the economic surplus and rate of return analysis were based on field surveys of farmers, scientists and industry experts. The summary of assumed changes in the rice budget due to SDR rice is presented in Tables 1 and 2.

Yield of transgenic SDR rice is expected to increase by 20–40%. Irrigation cost is assumed to decrease by a remarkable 25%, with slight increases in seed (10%), fertiliser (10%) and harvest labour costs. The probability of success is assumed to be 70–90%. Table 1 Summary of changes (trade-offs) in transgenic SDR rice budget (based on farmer,

scientists and industry expert opinion)

Particular Changes

Variable cost Seed cost To increase by 4–10% Fertiliser cost To increase by 5–10% Irrigation cost To decrease by 25% Pesticide cost No change Labour cost To increase by 2–6% Fertiliser labour cost To increase by 3% Harvest labour cost To increase by 4% Other labour cost No change Other costs No change Yield and returns Yield (kg) To increase by 20–40% Price per kg No change Yield loss 10–75% Variety BR-4, 6, 28, 29, 30, 40, 41 Source BADC and local market Probability of success 70–90%


Table 2 Assumptions used in economic surplus model

Parameter Description and value

Year Annual benefits are projected for 15 years after research commences, 2005–2019 (t = 1, 2, , 15).

Supply elasticity The supply elasticity, , is set at 0.13 for rice. Demand elasticity The demand elasticity, , is set at 0.20 for rice. Proportionate yield change

The estimate of yield increase, E(Y), is 20%.

Proportionate change in input cost per hectare

The proportionate change in input cost per hectare is 14%.

Probability of research success

The technology has not been released yet, and the relevant probability of research success is set at 0.90.

Adoption rate For the purpose of simulation, the assigned maximum adoption rates are 30%, 60% and 70%. Time required to reach the peak: 3 years.

Price Wholesale prices for the period of 2000–2005 are averaged, giving a mean value of $183.98 per ton.

Quantity The pre-research quantity is constant, equal to the base quantity 323 thousand ton in 2005.

Research cost The estimated annual research cost for SDR rice is 9,538.46 thousand dollar.

5.2 Ex ante analysis of cost and profitability of transgenic SDR rice

The farmer survey revealed that salinity and drought continues to be a major problem in rice production in Noakhali and Sathkhira causing annual losses in yield of 10–75%. Present crop regimes also require irrigation for rice production throughout the growing season. If an SDR transgenic rice is cultivated in Noakhali and Sathkhira, it would reduce irrigation costs per ha by $3.37 and $12.19, respectively (Table 3). On average, the per ha irrigation cost reduction would be $7.8 or 25% of total irrigation cost. The commercialisation of SDR transgenic rice would reduce labour cost in the salinity regions by 4%. The total variable cost saving in Noakhali for SDR transgenic rice would be 0.37% and in Sathkhira it would be 4.75%, respectively.

The yield of SDR transgenic rice is expected to increase by 460 kg ha 1 in Noakhali and by 603 kg ha 1 in Sathkhira. The minimum expected yield increase of transgenic SDR rice over non-transgenic SDR rice is 20%. With this yield increase, the gross margin of SDR transgenic rice in Noahkhali would increase by 102% ($68 ha 1) and by 45%($133 ha 1) in Sathkhira (Table 4). However, in all these regions the extent of increase of gross margin of SDR transgenic rice over conventional non-transgenic rice is about 49%.The commercialisation of a specific transgenic SDR rice variety could potentially offer a significant increase in gross margin and profitability to the producers.

90 S.M.F. Islam

Table 3 Ex ante analysis of economic performance of transgenic SDR rice over non-transgenic SDR rice in Noakhali

Cost item

Noakhali Sathkhira

Cost ($ ha 1)

Change %

Cost ($ ha 1)

Change %

Non-transgenic SDR rice

Transgenic SDR rice

Non-transgenic SDR rice

Transgenic SDR rice

Labour 149.90 146.78 4.62 4.1 152.50 149.41 4.62 4.0 Power 33.28 33.28 0.00 0 38.77 38.77 0.00 0 Seed 25.95 28.54 2.59 10.0 15.65 17.22 1.56 10.0 Fertiliser 49.04 53.94 4.90 10.0 29.48 32.42 2.95 10.0 Pesticides 6.36 6.36 0.00 0.0 10.97 7.68 42.52 387.6 Irrigation 13.47 10.10 3.37 25.0 48.78 36.58 12.19 25.0 Total variable cost

277.99 279.00 1.02 0.37 296.15 282.08 14.06 4.75

Yield (kg ha 1) 2,299.14 2,759 459.8 0.2 3,016.13 3,619 603.226 0.2

Total return ($ ha 1)

344.16 413.00 68.83 20 593.95 712.73 118.79 20

Gross margin ($ ha 1)

66.18 133.99 67.82 102 297.80 430.65 132.85 45

Table 4 Ex ante analysis of economic performance of SDR rice over non-SDR rice in two regions

Cost item

Cost ($ ha 1)

Change % Non-transgenic SDR

rice Transgenic SDR

rice

Labour 151.20 148.10 4.62 4.10 Mechanical/draft power 36.02 36.02 0.00 0.00 Seed 20.80 22.88 2.08 10.00 Fertiliser 39.26 43.18 3.93 10.00 Pesticides 8.45 8.45 0.00 0.00 Irrigation 31.12 23.32 7.80 25.00 Total variable cost 286.86 281.95 4.91 1.71

Yield (kg ha 1) 26,110.00 31,332.00 5,222.00 0.20

Total return ($ ha 1) 2,201.08 3,133.20 932.12 42.35

Gross margin ($ha) 1,914.22 2,851.25 937.03 49.00

A partial budget analysis of replacing traditional varieties by a SDR transgenic variety is presented in Table 5. The introduction of SDR rice would reduce irrigation cost by $7.80 ha 1 and irrigation labour cost by $3.09 ha 1. The total incremental benefit is expected to be $947.89 ha 1 against an incremental cost of $13.86 ha 1. Thus, yielding a net benefit of $934.03 ha 1.


Table 5 Partial budget of SDR rice replacing traditional varieties

Particular Based on 2005 crop budget ($ ha 1)

Incremental benefits Reduced cost Irrigation cost 7.80 Irrigation labour 3.09 Added return Increased revenue 937.00 Total incremental benefits 947.89 Incremental costs Added cost Seed cost 2.08 Fertiliser 3.93 Harvest labour 7.85 Reduced return –Total incremental cost 13.86 Net incremental benefits 934.03 Incremental benefit-cost ratio 59.00

5.3 Spreadsheet analysis

Spreadsheets were used for estimating changes in consumer surplus, producer surplus, total net benefits and rates of return. Research expenditures were assumed to begin in 2005 and to continue until 2009. Thus, there is a five years of research, technology development and regulatory lag before benefits occur. Excel spreadsheets were prepared for analysing distribution of benefits and the social return of developing and extending SDR transgenic rice for the coastal region of Bangladesh. The results are presented in Spreadsheets 1 and 2.

92 S.M.F. Islam

Spreadsheet 1 Estimation of supply shifter K

A B C D E F G H I J K

1 Year e n Yield

change

Gross proportion cost change

Proportion of input

cost change per ha

Proportion input cost

change per ton

Net cost change

Probability of success

Adopt rate K

2 2005 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.0 0.000000

3 2006 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.0 0.000000

4 2007 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.0 0.000000

5 2008 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.0 0.000000

6 2009 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.0 0.000000

7 2010 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.0 0.000000

8 2011 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.3 0.383885

9 2012 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.6 0.767769

10 2013 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

11 2014 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

12 2015 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

13 2016 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

14 2017 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

15 2018 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

16 2019 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

17 2020 0.13 0.2 0.2 1.538462 0.14 0.116667 1.421795 0.9 0.7 0.895731

Note: Following working formulae were used for developing Spreadsheet 1: Gross proportion of cost change, E2 = +B2/D2 Proportion input cost change per ton, G2 = +F2/(1 + D2) Net cost change, H2 = +(E2 – G2) Supply shifter, K2 = +(H2*I2*J2).


Spreadsheet 2 Ex ante analysis of consumer and producer surplus, net benefits and social rate of returns from producing transgenic SDR rice in Bangladesh, 2010–2020

L M N O P Q R S T U L

1 Year Z Price Quantity

Change in economicsurplus ( CTS)

(thousand $)

Change in consumer surplus ( CS)

(thousand $)

Change in producer surplus ( PS)

(thousand $)

Researchcost

(thousand $)

Net benefit

(thousand $)NPV

(thousand $) IRR (%)

2 2005 0.000 184 323 0.0 0.00 0.00 9,538.462 9,538.5 117,430.8846 33.78

3 2006 0.000 184 323 0.0 0.00 0.00 9,538.462 9,538.5 4,003.9907

4 2007 0.000 184 323 0.0 0.00 0.00 9,538.462 9,538.5 6,594.1696

5 2008 0.000 184 323 0.0 0.00 0.00 9,538.462 9,538.5

6 2009 0.000 184 323 0.0 0.00 0.00 9,538.462 9,538.5

7 2010 0.000 184 323 0.0 0.00 0.00 9,538.462 9,538.5

8 2011 0.151 184 323 23,158.1 91,228.96 14,035.22 23,158.1

9 2012 0.302 184 323 47,006.2 18,517.61 28,488.63 47,006.2

10 2013 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

11 2014 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

12 2015 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

13 2016 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

14 2017 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

15 2018 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

16 2019 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

17 2020 0.353 184 323 55,108.9 21,709.58 33,399.35 55,108.9

Total 511,035.83 201,317.15 309,718.69 511,035.83

Discounted total (at 10%) 188,063 74,085.56 113,978

Note: Following working formulae were used for developing Spreadsheet 2: Change in total economic surplus, P2 = +(K2*N2*O2*(1+0.5*M2*C2)) Change in consumer surplus, Q2 = +(N2*K2*M2*(1+(0.5*O2*C2)) Change in producer surplus, R2 = + =P2 Q2NPV at 10% interest rate, U2 = NPV(0.1,S2:S17) NPV at 30% interest rate, U3 = NPV(0.3,S2:S17) NPV at 40% interest rate, U4 = NPV(0.4,S2:S17) IRR, T2 = (0.3+0.1*(4003.99/(4003.99+6594.17)))*100.

5.4 Distribution of benefits and social return

The per unit (ton) cost reduction (or supply shifter K) is estimated for a period of ten years based on the projected yield and cost changes. Its initial value in 2010 is 0.37 and it gradually increases to 0.87 in 2013 and there after remains constant. This shift implies that rice production in the salinity area of Bangladesh would increase by 37% in the first year of adoption of transgenic SDR rice. In the later period, it is expected to increase by 87% per annum.

94 S.M.F. Islam

The results of economic surplus analysis model are presented in Spreadsheet 2. The introduction of transgenic SDR rice in Bangladesh would have a high social return on investment. Spreadsheet 2 shows the estimated social return and its distribution between producer and consumers for the period 2110–2020. The total amount of discounted economic surplus from producing SDR transgenic rice over 10 years is projected at US$188.06 million with a producer surplus of $113.9 million and a consumer surplus of $74.08 million. The annual distribution of producer surplus ranged from $14.04 to $33.39 million, while annual consumer surplus ranged from $9.1 to $21.7 million. The rate of social return or high IRR is estimated at 33.8% and a NPV of $117.4 million.

5.5 Sensitivity analysis

Sensitivity analysis was conducted under alternative scenarios of

1 a 30% increase in yield

2 a 20% increase in input cost

3 a 50% increase in supply elasticity

4 a 50% increase in base yield, cost and supply elasticity.

The IRR, NPV, total economic surplus and distribution of benefits between producers and consumers are presented in Tables 6–9. Under the first alternative scenario of a 30%increase in yield, compared to the base situation, it was observed that both the IRR and NPV sharply increased from 33.8% to 42% and $117.4 to $208.7 million, respectively. Our base situation is based on the possibility of a 20% yield increase for transgenic SDR rice over traditional non-transgenic varieties. The base scenario was based on a minimum yield potentiality of SDR rice as expected by the farmer, scientists and industry experts. However, with a higher yield assumption, the social rate of return, NPV and benefits of both producers and consumers increase sharply (Table 6).

In case of a 20% increase in input costs, it was observed that the transgenic technology would still remain viable with a 32.9% IRR and a NPV of $111.7 million. In case of 50% increase in the supply elasticity, it was observed that compared to the base scenario, the IRR sharply decreased from 33.8% to 26.1% and NPV from $117.4 to $59.4 million. But in this case, consumer surplus would decrease by 20.4% and producer surplus would increase by 79.6%. In case of a 50% increase in base yield, input cost and supply elasticity, it was observed that IRR, NPV, net benefit, producer and consumer surplus each changed slightly. Thus, the transgenic SDR rice technology would remain viable over the exiting non-transgenic one.


Table 6 Sensitivity analysis of consumer surplus, net benefits and social rate of return from producing transgenic SDR rice in Bangladesh, 2010–2020: assuming 30% increase in yield

Year TS (thousand $) CS (thousand $) PS (thousand $) Net benefit (thousand $) 2005 0.00 0.00 0.00 9,538.46 2006 0.00 0.00 0.00 9,538.46 2007 0.00 0.00 0.00 9,538.46 2008 0.00 0.00 0.00 9,538.46 2009 0.00 0.00 0.00 9,538.46 2010 0.00 0.00 0.00 9,538.46 2011 36,125.67 14,231.32 21,894.34 36,125.67 2012 73,903.36 29,113.44 44,789.92 73,903.36 2013 86,863.04 34,218.77 52,644.27 86,863.04 2014 86,863.04 34,218.77 52,644.27 86,863.04 2015 86,863.04 34,218.77 52,644.27 86,863.04 2016 86,863.04 34,218.77 52,644.27 86,863.04 2017 86,863.04 34,218.77 52,644.27 86,863.04 2018 86,863.04 34,218.77 52,644.27 86,863.04 2019 86,863.04 34,218.77 52,644.27 86,863.04 2020 86,863.04 34,218.77 52,644.27 86,863.04 Total 804,933.36 317,094.96 487,838.40 804,933.36 Discounted total (at 10%)

296,118.00 116,652.40 179,465.00 NPV = 208,751.44 IRR = 42%

Table 7 Sensitivity analysis of consumer surplus, net benefits and social rate of return from producing transgenic SDR rice in Bangladesh, 2010–2020: assuming 20% increase in input cost

Year TS (thousand $) CS (thousand $) PS (thousand $) Net benefit (thousand $) 2005 0.00 0.00 0.00 9,538.46 2006 0.00 0.00 0.00 9,538.46 2007 0.00 0.00 0.00 9,538.46 2008 0.00 0.00 0.00 9,538.46 2009 0.00 0.00 0.00 9,538.46 2010 0.00 0.00 0.00 9,538.46 2011 22,332.02 8,797.46 13,534.56 22,332.02 2012 45,306.35 17,847.96 27,458.39 45,306.35 2013 53,107.20 20,921.02 32,186.18 53,107.20 2014 53,107.20 20,921.02 32,186.18 53,107.20 2015 53,107.20 20,921.02 32,186.18 53,107.20 2016 53,107.20 20,921.02 32,186.18 53,107.20 2017 53,107.20 20,921.02 32,186.18 53,107.20 2018 53,107.20 20,921.02 32,186.18 53,107.20 2019 53,107.20 20,921.02 32,186.18 53,107.20 2020 53,107.20 20,921.02 32,186.18 53,107.20 Total 492,495.95 194,013.56 298,482.39 492,495.95 Discounted total (at 10%)

181,245.00 71,399.42 109,845.00 NPV = 111,667.70 IRR = 32.93%

96 S.M.F. Islam

Table 8 Sensitivity analysis of consumer surplus, net benefits and social rate of return from producing transgenic SDR rice in Bangladesh, 2010–2020: assuming 50% increase in supply elasticity

Year TS (thousand $) CS (thousand $) PS (thousand $) Net benefit (thousand $)

2005 0.00 0.00 0.00 9,538.46 2006 0.00 0.00 0.00 9,538.46 2007 0.00 0.00 0.00 9,538.46 2008 0.00 0.00 0.00 9,538.46 2009 0.00 0.00 0.00 9,538.46 2010 0.00 0.00 0.00 9,538.46 2011 14,761.47 7,287.31 7,474.16 14,761.47 2012 29,876.36 14,749.09 15,127.27 29,876.36 2013 34,993.19 17,275.12 17,718.07 34,993.19 2014 34,993.19 17,275.12 17,718.07 34,993.19 2015 34,993.19 17,275.12 17,718.07 34,993.19 2016 34,993.19 17,275.12 17,718.07 34,993.19 2017 34,993.19 17,275.12 17,718.07 34,993.19 2018 34,993.19 17,275.12 17,718.07 34,993.19 2019 34,993.19 17,275.12 17,718.07 34,993.19 2020 34,993.19 17,275.12 17,718.07 34,993.19 Total 324,583.31 160,237.33 164,345.98 324,583.31 Discounted total (at 10%) 119,463.00 58,975.53 60,488.00 NPV = 59,444.91

IRR = 26.14%

Table 9 Sensitivity analysis of consumer surplus, net benefits and social rate of return from producing transgenic SDR rice in Bangladesh, 2010–2020: assuming a 50% increase in the base yield, input cost and supply elasticity

Year TS (thousand $) CS (thousand $) PS (thousand $) Net benefit (thousand $) 2005 0.00 0.00 0.00 9,538.46 2006 0.00 0.00 0.00 9,538.46 2007 0.00 0.00 0.00 9,538.46 2008 0.00 0.00 0.00 9,538.46 2009 0.00 0.00 0.00 9,538.46 2010 0.00 0.00 0.00 9,538.46 2011 22,578.49 8,894.56 13,683.93 22,578.49 2012 46,127.67 18,171.51 27,956.16 46,127.67 2013 54,193.10 21,348.80 32,844.31 54,193.10 2014 54,193.10 21,348.80 32,844.31 54,193.10 2015 54,193.10 21,348.80 32,844.31 54,193.10 2016 54,193.10 21,348.80 32,844.31 54,193.10 2017 54,193.10 21,348.80 32,844.31 54,193.10 2018 54,193.10 21,348.80 32,844.31 54,193.10 2019 54,193.10 21,348.80 32,844.31 54,193.10 2020 54,193.10 21,348.80 32,844.31 54,193.10 Total 502,250.98 197,856.45 304,394.54 445,020.22 Discounted total (at 10%) 184,778.00 72,791.38 111,987.00 NPV = 114,643.59

IRR = 33.36%


5.6 Environmental impact

From our field surveys of farmers, scientists and industry experts, and also from farmers’ focus group discussions, it is clear that the growers are aware about soil salinity problems of rice cultivation and most of them are risk averse, often trying to avoid risk of crop loss by irrigating. On the basis of our survey results, it was estimated that irrigation resulted in an expenditure of US$12.4 million in salinity rice areas of four districts of Khulna, Noakhali, Barishal and Patuakhali, indicating there is a scope for cost savings in terms of reduction of irrigation for rice cultivation and for protecting the environment with conservation of ground water as well as solving soil salinity problems by means of adopting a SDR transgenic rice crop.

6 Conclusion

Soil salinity is a major, and the most persistent, threat to coastal agriculture. In Bangladesh, more than 30% of the net cultivated land is in the coastal area. Of the 2.85 million ha of coastal and offshore lands, about 0.83 million ha of arable land are affected by varying degrees of soil salinity. In saline soils, crop growth is hampered by salt accumulation in the crop root zone. In the coastal region of Bangladesh, considerable amounts of salt accumulate on the soil surface by evaporation, particularly in the presence of a shallow saline water table during the dry period from February to May. Rice is the most important crop affected by salinity during the summer season in the coastal area of Bangladesh. Therefore, an attempt was taken to develop DSS tools for ex ante assessment of costs and benefits of SDR transgenic rice technology in the context of salinity intrusion problem due to the climate change.

The trade-off analysis carried out in terms of partial budgeting showed that the introduction of SDR transgenic rice replacing traditional variety would reduce irrigation cost by $7.80 ha 1 and irrigation labour cost by $3.09 ha 1. The total incremental benefit is expected to be $947.89 ha 1 against an incremental cost of $13.86 ha 1, thus yielding a net benefit of $934.03 ha 1. The introduction of transgenic SDR rice in Bangladesh would have sizable social benefits. The total amount of non-discounted economic surplus from producing SDR transgenic rice over 10 years is projected at US$188.06 million with a producer surplus of $113.9 million and a consumer surplus of $74.08 million. The annual distribution of producer surplus ranged from $14.04 to $33.39 million, while annual consumer surplus ranged from $9.1 to $21.7 million. The rate of social return or IRR is estimated at 33.8% and a NPV of $117.4 million. The results of sensitivity analysis implied that transgenic SDR rice technology would remain viable over the existing non-transgenic variety under alternative scenarios.

Future research directions

The DSS tools developed in this study using economic surplus model and spreadsheet-based analysis can be used for assessing ex ante costs and benefits of investment on research for agro-technology development in the context of climate change. The spreadsheet-based analytical technique presented in this study could be useful for the future research.

98 S.M.F. Islam

References Akino, M. and Hayami, Y. (1975) ‘Efficiency and equity in public research: rice breeding in

Japan’s economic development’, American Journal of Agricultural Economics, Vol. 57, No. 1, pp.1–6.

Alston, J.M., Norton, G.W. and Pardey, P.G. (1995) Science under Scarcity: Principles and Practice for Agricultural Research Evaluation and Priority Setting. Ithaca: Cornell University Press.

Crissman, C.C., Antle, J.M. and Capalbo, S.M. (Eds.) (1998) Economic, Environmental, and Health Tradeoffs in Agriculture: Pesticides and the Sustainability of Andean Potato Production. Boston: Kluwer Academic Publishers, p.281.

Griliches, Z. (1958) ‘Research costs and social return: hybrid corn and related innovations’, Journal of Political Economy, Vol. 66, No. 5, pp.419–431.

Harberger, A.C. (1971) ‘Three basic postulates for applied welfare economics: an introspective essay’, Journal of Economic Literature, Vol. 9, No. 3, pp.785–797.

Karim, M.R. (2001) ‘Return from agricultural research and extension in wheat in Bangladesh’, Paper presented in the Training Workshop on Agricultural Research Impact Evaluation Methods (Phase II), Organized at Bangladesh Agricultural Research Institute, Farm Gate, Dhaka, April 2001.

Miah, M. and Hossain, I. (2001) ‘Return to varietal research and extension of brinjal in Bangladesh’, A paper presented in the workshop on, Impact of Investment in Agricultural Research. Organized at Bangladesh Agricultural Research Institute, Farm Gate, Dhaka, April 2001.

Peterson, W.L. (1967) ‘Return to poultry research in the United States’, Journal of Farm Economics, Vol. 49, No. 3, pp.656–670.

Schimitz, A. and Seckler, D. (1970) ‘Mechanized agriculture and social welfare: the case of tomato harvester’, American Journal of Agricultural Economics, Vol. 52, No. 4, pp.569–777.

Scobie, G.M. and Posada, R.T. (1978) ‘The impact of technical change on income distribution: the case of rice in Columbia’, American Journal of Agricultural Economics, Vol. 60, No. 1, pp.85–92.

Siddique, M.R. (1985) ‘Social return to rice research in Bangladesh’, Unpublished MS (Economics) Thesis, Faculty of Economic Studies, University of New England, Australia.

Stoorvogel, J.J., Antle, J.M., Crissman, C.C. and Bowen, W. (2001) The Tradeoff Analysis Model Version 3.1: A Policy Decision Support System for Agriculture, Wageningen, The Netherlands: Laboratory of Soil Science and Geology, Wageningen University, ISBN: 90-6754-660-7.


Appendix

Decision support system for ex ante costs-benefit assessment of new agro-technology in the context of climate change

Farmer questionnaire

Commodity: salinity and draught resistance (SDR) rice

Respondent Interviewer

Name: _________________________ Name: ________________________

Location: _______________________ Date: _________________________

Type of land tenure: ______________

Education: ______ Age ______

Years in farming: _____

Years in salinity rice cultivation _____

1. What are your current crop management practices for the following:

a) Land preparation? Land area _______decimal, variety cultivated _______

Number of plough_________ by Bullock/power tiller, Labour used _____ (man days) wage ____ Tk/md, Power tiller cost ______

b) Crop establishment?

A. Planting:

Seed rate ________kg/bigha Price of seed ______Tk/kg, Source of seed: Purchased from_____, or Own _____

Planting time _________, Labour used Hired: Male ___ md, Female ____ md, Own: Male _____ md, Female __ md, Wage_____ Tk/md

100 S.M.F. Islam

B. Application of manure: Quantity __________ton/bigha,

Labour used: Own ____md, Hired ____ md

C. Application of fertiliser: kg/bigha

Quantity: Urea ____, TSP ____, MP ___, Zinc sulphate ____ Other (specify) ___

Price: (Tk/kg) Urea ___, TSP ____, MP ___, Zinc sulphate ____ Other (specify) __

D. Mulching: No. ___, Labour used: Own ____md, Hired ___ md, Wage ___Tk/md

E. Weeding: No. ___, Labour used: Own ____md, Hired ____ md, Wage ___Tk/md

c) Water management? Irrigation No. ____, Source ____ Labour used: Own ____md, Hired ____ md, Wage ___Tk/md, Fuel/Rent cost of pump ___Tk/bigha

d) Pest management information:

Name of pest/disease: __________________________________________

Control method

Frequency (per cropping

season) Quantity/bigha Price Brand name

Chemicals Insecticides Fungicides Biological Botanical Others (specify)

Labour used: Own ____manday, Hired ____ manday, Wage ___Tk/manday

e) Harvesting: Time: _____, Labour used: Own: Male ____ manday, Female: ____ manday, ired: Male ____ manday, Female ____ manday, Wage ___Tk/manday

2. What was your yield per bigha for this crop last year? Ton/bigha_____________ and your average over the last 5 years? Ton/bigha_________, Output Price Tk/kg _________

3. What was your average annual crop loss (%) due to the target pest last year? ___________ and over the last 5 years? ____________________

4. What are the preferred varieties in your area? ____________________


5. What is/are your source/s of seeds/planting materials? _________________

6. What are your market outlets? ( )

a. traders ___________

b. direct selling ____________

c. contract growing _____________

7. What are the major problems of rice cultivation in your area:

a. Pest/disease ___________________________________________________

b. Seed problem____________________________________________

c. Labour scarcity____________________________________________

d. Fertiliser problem__________________________________________

e. Irrigation problem_____________________________________________

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Industry Expert Questionnaire

Commodity: salinity and draught resistant (SDR) rice


Name: _________________________ Name: _________________________

Occupation: _____________________ Date: __________________________

Institutional affiliation (if any): ______________________

Years of experience on the crop: _______

1. The target pest problem of rice that is required to be addressed by biotechnology is salinity and draught problem.

2. What was the average annual crop loss (%) due to the target pest last year? ________ and in the last 5 years? __________

3. What are the preferred varieties in your area? _______________________________

4. What are the main sources of seed?____________________________________

5. How many years will it take to complete the technology development and to meet the various regulatory requirements?

Year Time lag (years)

Minimum Most likely Maximum Technology development Regulatory: Limited field trial Multi-location field trial Food safety assessment Apply for commercialisation

6. What are the chances (%) that the product will pass the regulatory requirements and be commercialised? _____________

7. What is the maximum percentage of crop area expected to be covered by the biotech crop? __________________How many years will it take to reach that maximum once the crop is commercially released? _____________________________

8. What are the expected costs involved in developing the technology and meeting the regulatory requirements?


Year

Cost (lac taka)

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Technology development

Regulatory Limited field trial Multi-location field trial

Food safety assessment

Apply for commercialisation

9. Do you expect an increase (decrease) in area devoted to the commodity over the next 10 years? If so, by what percent per year?

Region Increase or decrease Percent change per year

10. What are the constraints of adoption of genetically modified crop in Bangladesh?

a. ___________________________________________________

b. ___________________________________________________

c. ___________________________________________________

d. ___________________________________________________

11. What are the probable solutions that you would like to suggest?

a. ___________________________________________________

b. ___________________________________________________

c. ___________________________________________________

d. ___________________________________________________

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Scientist Questionnaire

Crop: salinity and drought resistant (SDR) rice


Name: _________________________ Name: _________________________

Position: _______________________ Date: __________________________

Institute _________________________

Specialty: ________________________

Education: ______________________

Years of experience on the crop: _____

1. The target burning problem of the crop is salinity and draught problem

2. a) If a genetically modified crop is developed and adopted to solve above problem (for those farmers who adopt it in the region) what will be the expected yield change? ______________%

b) What will be most potential regions for the genetically modified crop _______________

3. What percent of total variable costs is currently represented by each variable input? What is your estimate of the percent change in cost (per hectare) (if any) for each of the inputs if the genetically modified crop is adopted?

Region:

Most likely cost change (per ha)

Variable input Current cost share (%) Decrease no change increase Percent change

Variable ($/ha) Hired labour Fertiliser Pesticides Seeds Marketing Other

4. What is the probability (percent chance) of biotech research developing a solution with a commercially acceptable level of effectiveness against the targeted problem? _________________


5. How many years will it take to complete the technology development and to meet the various regulatory requirements?

Year

Time lag (years)

Minimum Most likely Maximum

Technology development Regulatory: Limited field trial Multi-location field trial Food safety assessment Apply for commercialisation

6. What are the expected costs involved in developing the technology and meeting the regulatory requirements?

Year Cost (lac taka)

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Technology development

Regulatory Limited field trial Multi-location field trial

Food safety assessment

Apply for commercialisation

7. Which variety do you intend to put this technology? (check your answer(s))

Options for variety choice Remarks

Variety type Hybrid Saved seeds/OP Variety source Public Private Variety use Fresh Processed Target market Domestic Export

8. What are the expected unintended environmental effects? (check if a concern)

a. gene flow ______________________________

b. reduced biodiversity ______________________

c. harms non-target organisms ________________

d. others (specify) __________________________

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9. What are the constraints of adoption of genetically modified crop in Bangladesh?

a. ___________________________________________________

b. ___________________________________________________

c. ___________________________________________________

d. ___________________________________________________

10. What are the probable solutions that you would like to suggest?

a. ___________________________________________________

b. ___________________________________________________

c. ___________________________________________________

d. ___________________________________________________

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