{"id":116,"date":"2018-02-08T13:06:53","date_gmt":"2018-02-08T13:06:53","guid":{"rendered":"http:\/\/www.rtb.cgiar.org\/rtb-bananaresearchpriorities\/?page_id=116"},"modified":"2019-02-13T06:25:21","modified_gmt":"2019-02-13T06:25:21","slug":"6-results","status":"publish","type":"page","link":"https:\/\/www.rtb.cgiar.org\/rtb-bananaresearchpriorities\/knowledge-toolkit\/6-results\/","title":{"rendered":"6. Results"},"content":{"rendered":"
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Photo Credit: S. Landersz\/Bioversity International<\/p><\/div>\n

After going through the key steps of the priority assessment, namely the identification of key constraints, description of research options, introduction of methodology used (Cost-Benefit Analysis and Economic Surplus Modelling), and account of data sources and elicitation process for parameter estimates, we can now look at the results of the Strategic Assessment of Banana Research Priorities.<\/p>\n

Before having a look at the results, we invite you to briefly take a look at the section on how to interpret and use the results in which we recap the explanatory power of the main outcome variables.\u00a0 As a next step, you can browse through the results from the Cost-Benefit Analysis using Economic Surplus Modelling and examine the number of beneficiaries, poverty reduction and regional distribution of impacts. Last but not least, results of the Sensitivity Analysis that was performed will be outlined.<\/p>\n<\/p><\/div> <\/div> <\/div><\/div><\/div><\/div><\/section>

6.1. How to interpret results<\/span><\/a><\/div>\n
6.2. Results from cost-benefit analysis using economic surplus model<\/span><\/a><\/div>\n
6.3. Number of beneficiaries, poverty reduction and regional distribution of impacts<\/span><\/a><\/div>\n
6.4. Sensitivity analysis<\/span><\/a><\/div>\n<\/div><\/div>
6.1. How to interpret results<\/h5>
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Before presenting the results of the banana research priority assessment, we would like to recap the main outcome variables and briefly explain how to interpret and use the results for each of them.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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\n\n\n\n\n\n\n\n\n\n
Outcome variable<\/th>\nBrief recap<\/th>\nInterpretation and use of results<\/th>\n<\/tr>\n<\/thead>\n
Adoption ceiling<\/b><\/td>\nThe adoption ceiling determines the maximum crop area on which the new technology will be adopted.<\/p>\n

Two scenarios were considered in the priority assessment exercise: the high adoption scenario (\u2018higher adoption\u2019) comprises the adoption ceiling estimated by the resource persons; for the more conservative adoption scenario (\u2018lower adoption\u2019) the expert estimate was reduced by 50% while all other parameters were held constant.<\/td>\n

The estimated maximum adoption area translates into the maximum number of farmers adopting the technology and thus into the likely number of beneficiaries of the new technology. This indicator is therefore important when making funding decisions as it shows how many people could potentially be reached by each banana research investment. Also, the adoption is the factor with which all yield\/cost effects are multiplied. This means that it largely determines the total benefits.<\/p>\n

Including the second – more conservative – adoption scenario helps to correct for potential overestimation of benefits. We can probe if a research investment would still be sound if adoption was much lower than anticipated (as notoriously happens).<\/td>\n<\/tr>\n

Net Present Value (NPV)<\/b><\/td>\nThe Net Present Value gives us today\u2019s value of net benefits generated by each research option. It is computed by deducting the discounted future costs from the discounted future benefits.<\/td>\nThe minimum requirement for a sound banana research investment is that the expected Net Present Value is positive meaning that the discounted benefits exceed the discounted costs. If several research options that we assessed yield a positive Net Present Value, the option with the highest Net Present Value is usually chosen. Yet, since the Research and Development costs vary substantially across research options ($2.8 million\u2013$47.7 million) and only a limited amount of money might be available for investment, the Net Present Value should not be used alone to rank the research options.<\/td>\n<\/tr>\n
Internal Rate of Return (IRR)<\/b><\/td>\nThe Internal Rate of Return is the discount rate at which the research investment breaks even. Technically, it is the discount rate at which the NPV equals zero meaning that the present value of costs of our research would exactly equal the present value of the research benefits.<\/td>\nBasically, the Internal Rate of Return is the interest rate earned on the research investment and can be compared to other interest rates earned by alternative use of the funds. Hence, the research project is considered a good investment if the Internal Rate of Return exceeds these other rates. In our case, the research investment is a sound investment if the Internal Rate of Return exceeds 10% (which is the discount rate applied in our Cost-Benefit Analysis model). In general, the higher the Internal Rate of Return of a banana research investment, the more desirable it is to undertake this project.<\/td>\n<\/tr>\n
Number of beneficiaries<\/b><\/td>\nThe number of beneficiaries represents the estimated number of households and persons who will benefit from the research option. The numbers are based on the adoption ceiling in each of the countries, the production area within those countries as well as the assumption of average banana production area of a farmer and the average family size.<\/td>\nSimilar to the Net Present Value results, this information should be interpreted with respect to the different magnitude of the investments required\/assumed across research options.<\/td>\n<\/tr>\n
Poverty reduction<\/b><\/td>\nThe poverty reduction results show how many people can be lifted out of poverty due to adopting a new technology resulting from the respective banana research investment.<\/td>\nSince the ultimate impact and goal of public agricultural research investments is to benefit the poor by eradicating poverty and hunger, the results of the poverty reduction model are an important additional indicator complementing the results from the Cost-Benefit Analysis. In this more equity focused consideration, we adjust for the specific region where benefits occur by including national poverty indicators and so-called region-specific \u2018poverty elasticities\u2019. As a consequence, the ranking of research options can be different from that resulting from the other indicators and generally research options with a larger share of expected adoption in Sub-Saharan Africa, where poverty rates are much higher than in Latin America & Caribbean and Asia, will rank higher based on the expected poverty reduction.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div> <\/div> <\/div><\/div><\/div>
6.2. Results from cost-benefit analysis using economic surplus model<\/h5>
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For the estimation of benefits resulting from technology adoption we used a 25-year horizon and the computation of the Net Present Value (NPV) is based on a discount rate of 10%. \u00a0To correct for potential overestimation of benefits, we ran the model for a second, more conservative adoption scenario for which the adoption ceiling estimated by the resource persons was reduced by 50% while all other parameters were held constant.<\/p>\n

The scenario with the original adoption ceiling estimates is referred to as \u201chigher adoption\u201d and the more conservative (50% adoption) scenario as \u201clower adoption.”<\/p>\n

The results of the economic surplus modelling and cost-benefit analysis are displayed in the following table:<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Technology<\/b><\/td>\n\n

Adoption area<\/b><\/p>\n<\/td>\n

\n

All benefits<\/b><\/p>\n<\/td>\n<\/tr>\n

\n

Lower
\nAdoption<\/b><\/p>\n<\/td>\n

\n

Higher
\nAdoption<\/b><\/p>\n<\/td>\n

\n

Lower
\nAdoption<\/b><\/p>\n<\/td>\n

\n

Higher
\nAdoption<\/b><\/p>\n<\/td>\n<\/tr>\n

\n

(‘000 ha)<\/b><\/p>\n<\/td>\n

\n

(‘000 ha)<\/b><\/p>\n<\/td>\n

\n

NPV
\n($’000)<\/b><\/p>\n<\/td>\n

\n

IRR
\n(%)<\/b><\/p>\n<\/td>\n

\n

NPV
\n($’000)<\/b><\/p>\n<\/td>\n

\n

IRR
\n(%)<\/b><\/p>\n<\/td>\n<\/tr>\n

Recovery from BBTV<\/td>\n\n

404<\/p>\n<\/td>\n

\n

807<\/p>\n<\/td>\n

\n

1,340,032<\/p>\n<\/td>\n

\n

63<\/p>\n<\/td>\n

\n

2,740,802<\/p>\n<\/td>\n

\n

79<\/p>\n<\/td>\n<\/tr>\n

BXW management: GM-resistant varieties<\/td>\n\n

436<\/p>\n<\/td>\n

\n

872<\/p>\n<\/td>\n

\n

105,619<\/p>\n<\/td>\n

\n

38<\/p>\n<\/td>\n

\n

216,028<\/p>\n<\/td>\n

\n

46<\/p>\n<\/td>\n<\/tr>\n

BXW management: cultural practices<\/td>\n\n

643<\/p>\n<\/td>\n

\n

1,287<\/p>\n<\/td>\n

\n

1,980,437<\/p>\n<\/td>\n

\n

76<\/p>\n<\/td>\n

\n

4,083,161<\/p>\n<\/td>\n

\n

95<\/p>\n<\/td>\n<\/tr>\n

Cropping system intensification*<\/td>\n\n

627<\/p>\n<\/td>\n

1,253<\/td>\n\n

547,506<\/p>\n<\/td>\n

\n

43<\/p>\n<\/td>\n

\n

1,127,387<\/p>\n<\/td>\n

\n

54<\/p>\n<\/td>\n<\/tr>\n

Resistant EAHB (NEW)<\/td>\n\n

592<\/p>\n<\/td>\n

\n

1,185<\/p>\n<\/td>\n

\n

98,516<\/p>\n<\/td>\n

\n

23<\/p>\n<\/td>\n

\n

214,366<\/p>\n<\/td>\n

\n

28<\/p>\n<\/td>\n<\/tr>\n

Resistant EAHB (RELEASE)<\/td>\n\n

397<\/p>\n<\/td>\n

\n

795<\/p>\n<\/td>\n

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300,974<\/p>\n<\/td>\n

\n

51<\/p>\n<\/td>\n

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612,477<\/p>\n<\/td>\n

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61<\/p>\n<\/td>\n<\/tr>\n

Resistant plantain (NEW)<\/td>\n\n

524<\/p>\n<\/td>\n

1,049<\/td>\n\n

295,359<\/p>\n<\/td>\n

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29<\/p>\n<\/td>\n

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618,668<\/p>\n<\/td>\n

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34<\/p>\n<\/td>\n<\/tr>\n

Resistant plantain (RELEASE)<\/td>\n\n

449<\/p>\n<\/td>\n

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898<\/p>\n<\/td>\n

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1,110,961<\/p>\n<\/td>\n

\n

64<\/p>\n<\/td>\n

\n

2,264,126<\/p>\n<\/td>\n

\n

75<\/p>\n<\/td>\n<\/tr>\n

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Foc A: Quarantine – Scenario 1**<\/p>\n<\/td>\n

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441<\/p>\n<\/td>\n

–<\/td>\n300,739<\/td>\n14<\/td>\n–<\/td>\n–<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 2***<\/p>\n<\/td>\n

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396<\/p>\n<\/td>\n

–<\/td>\n193,661<\/td>\n13<\/td>\n–<\/td>\n–<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 3****<\/p>\n<\/td>\n

330<\/td>\n–<\/td>\n69,627<\/td>\n11<\/td>\n–<\/td>\n–<\/td>\n<\/tr>\n
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Foc B: Integrated Management<\/p>\n<\/td>\n

172<\/td>\n344<\/td>\n505,714<\/td>\n30<\/td>\n1,052,200<\/td>\n36<\/td>\n<\/tr>\n
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Foc C: Resistant cultivars<\/p>\n<\/td>\n

307<\/td>\n614<\/td>\n186,519<\/td>\n20<\/td>\n424,864<\/td>\n25<\/td>\n<\/tr>\n
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Foc D: GM resistant cultivars<\/p>\n<\/td>\n

63<\/td>\n127<\/td>\n137,024<\/td>\n28<\/td>\n286,030<\/td>\n34<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

BBTV = Banana Bunchy Top virus; BXW = Banana Xanthomonas wilt;\u00a0<\/i>Foc =\u00a0Fusarium oxysporum f. sp. cubense<\/i><\/p>\n

Remarks<\/b>:\u00a0<\/i>Lower adoption scenario: analysis with 50% lower adoption ceiling; NPV calculated using a real interest rate of 10%; *Benefits from reduced yield variability and improved status of (on-farm) natural resources (e.g., soil fertility) have not been included in this assessment, which thus likely shows an underestimation or lower boundary of the effect;
\n**Scenario 1: Doubling of arrival time and 50 percent reduced increase of loss rate (12.50%) once Foc reaches the country as compared to a scenario without intervention;
\n***Scenario 2: Arrival time as in Scenario 1 minus 5 years; 50 percent reduced increase of loss rate (12.50%) once Foc reaches the country;
\n****Scenario 3: Arrival time as in Scenario 1 minus 5 years; 25 percent reduced increase of loss rate (18.75%) once Foc reaches the country;<\/i><\/p>\n

Source:<\/b>\u00a0Results of Strategic Assessment of Banana Research Priorities
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In a nutshell, all assessed research options yield sizeable positive Internal Rates of Return (i.e., returns on the investment well above a standard 10% interest rate). Internal Rates of Returns are positive and above 10%, even under the (50%) lower adoption scenario. There is, however, considerable variation in the return on investment between research options, with \u201cBXW management: cultural practices\u201d yielding an estimated 76% and the \u201cFoc A: Quarantine \u2013 Scenario 3\u201d an estimated 11%.<\/p>\n

Estimated Net Present Values are positive throughout, confirming profitable investments. Since Research and Development costs (i.e., the level of investment) vary substantially across research options, the two indicators Internal Rate of Return and Net Present Value produce somewhat different rankings of the research options in terms of their profitability.<\/p>\n

The table also displays the estimated area on which the new technology will be adopted under both the lower and higher adoption scenarios. As per definition of the scenarios, the adoption ceiling reached under the lower adoption scenario is half of the higher adoption scenario area. The estimated adoption area is an additional indicator to be considered when making funding decisions as it translates into the likely number of beneficiaries of the new technology.<\/p>\n

Download the PDF\u00a0Here<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div> <\/div> <\/div><\/div><\/div>

6.3. Number of beneficiaries, poverty reduction and regional distribution of impacts<\/h5>
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The following table shows the estimated number of households and persons who will benefit from each of the research options. These figures are determined by the adoption ceiling in each of the countries, the number of countries included, and the production area within those countries.<\/p>\n

Similar to the Net Present Value results, this information should be interpreted with respect to the different magnitude of the investments required\/assumed across research options.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Technology<\/b><\/td>\nNumber of beneficiaries<\/b><\/td>\nPoverty reduction<\/b><\/td>\n<\/tr>\n
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Lower
\nAdoption<\/b><\/p>\n<\/td>\n

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Higher
\nAdoption<\/b><\/p>\n<\/td>\n

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Lower Adoption<\/b><\/p>\n<\/td>\n

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Higher Adoption<\/b><\/p>\n<\/td>\n<\/tr>\n

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Households
\n[‘000]<\/b><\/p>\n<\/td>\n

\n

Persons [‘000]<\/b><\/p>\n<\/td>\n

\n

Households [‘000]<\/b><\/p>\n<\/td>\n

\n

Persons [‘000]<\/b><\/p>\n<\/td>\n

\n

Persons
\n[‘000]<\/b><\/p>\n<\/td>\n

\n

Persons
\n[‘000]<\/b><\/p>\n<\/td>\n<\/tr>\n

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Recovery from BBTV<\/p>\n<\/td>\n

2,018<\/td>\n9,674<\/td>\n4,036<\/td>\n19,348<\/td>\n638<\/td>\n1,285<\/td>\n<\/tr>\n
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BXW management: GM-resistant varieties<\/p>\n<\/td>\n

2,173<\/td>\n10,745<\/td>\n4,346<\/td>\n21,489<\/td>\n155<\/td>\n311<\/td>\n<\/tr>\n
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BXW management: cultural practices<\/p>\n<\/td>\n

3,217<\/td>\n15,665<\/td>\n6,434<\/td>\n31,329<\/td>\n1,611<\/td>\n3,287<\/td>\n<\/tr>\n
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Cropping system intensification<\/p>\n<\/td>\n

1,397<\/td>\n6,428<\/td>\n2,794<\/td>\n12,856<\/td>\n342<\/td>\n686<\/td>\n<\/tr>\n
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Resistant EAHB (NEW)<\/p>\n<\/td>\n

934<\/td>\n4,326<\/td>\n1,869<\/td>\n8,652<\/td>\n953<\/td>\n1,935<\/td>\n<\/tr>\n
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Resistant EAHB (RELEASE)<\/p>\n<\/td>\n

634<\/td>\n2,937<\/td>\n1,267<\/td>\n5,874<\/td>\n389<\/td>\n782<\/td>\n<\/tr>\n
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Resistant plantain (NEW)<\/p>\n<\/td>\n

1,979<\/td>\n8,820<\/td>\n3,957<\/td>\n17,641<\/td>\n390<\/td>\n800<\/td>\n<\/tr>\n
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Resistant plantain (RELEASE)<\/p>\n<\/td>\n

1,696<\/td>\n7,566<\/td>\n3,393<\/td>\n15,133<\/td>\n247<\/td>\n50<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 1*<\/p>\n<\/td>\n

2,014<\/td>\n9,772<\/td>\n–<\/td>\n–<\/td>\n836<\/td>\n–<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 2**<\/p>\n<\/td>\n

1,810<\/td>\n8,799<\/td>\n–<\/td>\n–<\/td>\n738<\/td>\n–<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 3***<\/p>\n<\/td>\n

1,489<\/td>\n7,200<\/td>\n–<\/td>\n–<\/td>\n639<\/td>\n–<\/td>\n<\/tr>\n
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Foc B: Integrated Management<\/p>\n<\/td>\n

817<\/td>\n3,938<\/td>\n1,634<\/td>\n7,875<\/td>\n79<\/td>\n157<\/td>\n<\/tr>\n
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Foc C: Resistant cultivars<\/p>\n<\/td>\n

1,475<\/td>\n7,204<\/td>\n2,950<\/td>\n14,408<\/td>\n430<\/td>\n865<\/td>\n<\/tr>\n
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Foc D: GM resistant cultivars<\/p>\n<\/td>\n

306<\/td>\n1,371<\/td>\n612<\/td>\n2,743<\/td>\n44<\/td>\n89<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

BBTV = Banana Bunchy Top virus; BXW = Banana Xanthomonas wilt;\u00a0<\/i>Foc =\u00a0Fusarium oxysporum f. sp. cubense
\nRemarks<\/b>: Lower adoption scenario: analysis with 50% lower adoption ceiling; *Scenario 1: Doubling of arrival time and 50 percent reduced increase of loss rate (12.50%) once Foc reaches the country as compared to a scenario without intervention; **Scenario 2: Arrival time as in Scenario 1 minus 5 years; 50 percent reduced increase of loss rate (12.50%) once Foc reaches the country; ***Scenario 3: Arrival time as in Scenario 1 minus 5 years; 25 percent reduced increase of loss rate (18.75%) once Foc reaches the country;
\nSource:<\/b>\u00a0Results of Strategic Assessment of Banana Research Priorities<\/i><\/p>\n<\/div>\n

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The last two columns in the table show the results of the calculation of the estimated poverty reduction effects of the different research options. These results show a different \u201cranking\u201d of research options. The expected number of poor persons lifted out of poverty is partly determined by the magnitude of the Net Present Value, which is an input used for the calculation.<\/p>\n

The model also adjusts for the specific region where benefits will occur by including national poverty indicators and region-specific elasticities. As a consequence, research options that have a high share of adoption predicted within Sub-Saharan Africa (e.g., breeding for resistant EAHB) rank higher using this performance indicator and those with larger share of adoption in Latin America and the Caribbean (e.g., breeding for resistant plantain varieties) rank lower.<\/p>\n

The following table displays information about the regional distribution of the adoption area for the different research options. We note that these numbers are determined by the choice of countries to be included and, although resource persons have compiled the lists of countries to be included based on the severity\/presence of the constraint or the suitability of the new technology, there may be scope to broaden the target region(s) and\/or adapt the innovations in question to other areas. Also, the regional distribution of benefits is not only driven by the adoption area, but also by other parameters used in the model, such as productivity and cost effects, crop prices, and likely success rate.<\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Technology<\/b><\/td>\n\n

Adoption area after 25 years (higher adoption scenario)<\/b><\/p>\n<\/td>\n<\/tr>\n

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Africa<\/b><\/p>\n<\/td>\n

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Latin America & Carribean<\/b><\/p>\n<\/td>\n

\n

Asia\/Pacific<\/b><\/p>\n<\/td>\n

\n

ALL<\/b><\/p>\n<\/td>\n<\/tr>\n

\n

[‘000 ha]<\/b><\/p>\n<\/td>\n

\n

Share [%]<\/b><\/p>\n<\/td>\n

\n

[‘000 ha]<\/b><\/p>\n<\/td>\n

\n

Share [%]<\/b><\/p>\n<\/td>\n

\n

[‘000 ha]<\/b><\/p>\n<\/td>\n

\n

Share [%]<\/b><\/p>\n<\/td>\n

\n

[‘000 ha]<\/b><\/p>\n<\/td>\n<\/tr>\n

\n

Recovery from BBTV<\/p>\n<\/td>\n

706<\/td>\n87<\/td>\n–<\/td>\n–<\/td>\n101<\/td>\n13<\/td>\n807<\/td>\n<\/tr>\n
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BXW management: GM-resistant varieties<\/p>\n<\/td>\n

872<\/td>\n100<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n872<\/td>\n<\/tr>\n
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BXW management: cultural practices<\/p>\n<\/td>\n

1,287<\/td>\n100<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n1,287<\/td>\n<\/tr>\n
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Cropping system intensification<\/p>\n<\/td>\n

1,051<\/td>\n84<\/td>\n69<\/td>\n5<\/td>\n134<\/td>\n11<\/td>\n1,253<\/td>\n<\/tr>\n
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Resistant EAHB (NEW)<\/p>\n<\/td>\n

1,185<\/td>\n100<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n1,185<\/td>\n<\/tr>\n
\n

Resistant EAHB (RELEASE)<\/p>\n<\/td>\n

795<\/td>\n100<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n–<\/td>\n795<\/td>\n<\/tr>\n
\n

Resistant plantain (NEW)<\/p>\n<\/td>\n

646<\/td>\n62<\/td>\n371<\/td>\n35<\/td>\n31<\/td>\n3<\/td>\n1,049<\/td>\n<\/tr>\n
\n

Resistant plantain (RELEASE)<\/p>\n<\/td>\n

548<\/td>\n61<\/td>\n315<\/td>\n35<\/td>\n35<\/td>\n4<\/td>\n898<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 1*<\/p>\n<\/td>\n

203<\/td>\n46<\/td>\n44<\/td>\n10<\/td>\n194<\/td>\n44<\/td>\n441<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 2**<\/p>\n<\/td>\n

181<\/td>\n46<\/td>\n39<\/td>\n10<\/td>\n175<\/td>\n44<\/td>\n396<\/td>\n<\/tr>\n
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Foc A: Quarantine – Scenario 3***<\/p>\n<\/td>\n

\n

157<\/p>\n<\/td>\n

47<\/td>\n39<\/td>\n12<\/td>\n135<\/td>\n41<\/td>\n330<\/td>\n<\/tr>\n
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Foc B: Integrated Management<\/p>\n<\/td>\n

6<\/td>\n2<\/td>\n21<\/td>\n6<\/td>\n317<\/td>\n92<\/td>\n344<\/td>\n<\/tr>\n
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Foc C: Resistant cultivars<\/p>\n<\/td>\n

217<\/td>\n35<\/td>\n23<\/td>\n4<\/td>\n373<\/td>\n61<\/td>\n614<\/td>\n<\/tr>\n
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Foc D: GM resistant cultivars<\/p>\n<\/td>\n

18<\/td>\n14<\/td>\n3<\/td>\n2<\/td>\n106<\/td>\n83<\/td>\n127<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

BBTV = Banana Bunchy Top virus; BXW = Banana Xanthomonas wilt;\u00a0<\/i>Foc =\u00a0Fusarium oxysporum f. sp. cubense<\/i><\/p>\n

Remarks<\/b>: Lower adoption scenario: analysis with 50% lower adoption ceiling; *Scenario 1: Doubling of arrival time and 50 percent reduced increase of loss rate (12.50%) once Foc reaches the country as compared to a scenario without intervention; **Scenario 2: Arrival time as in Scenario 1 minus 5 years; 50 percent reduced increase of loss rate (12.50%) once Foc reaches the country; ***Scenario 3: Arrival time as in Scenario 1 minus 5 years; 25 percent reduced increase of loss rate (18.75%) once Foc reaches the country; Given that quarantine and surveillance measures are executed at the national level, we assumed that all farmers \u201cadopt\u201d or benefit from the technology once the country implements the Foc A quarantine schemes. The Foc A figures in this table represent only the area where Foc would spread and could be contained;<\/i><\/p>\n

Source:<\/b>\u00a0Results of Strategic Assessment of Banana Research Priorities<\/i><\/p>\n<\/div>\n

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Download the PDF\u00a0Here<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div> <\/div> <\/div><\/div><\/div>

6.4. Sensitivity analysis<\/h5>
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Photo Credit: R. Swennen KU Leuven\/Bioversity International<\/p><\/div>\n

All ex ante assessments are trying to predict future outcomes of (hypothetical) investments and the results are based on (expert) estimates of the costs and effects. It is thus very reasonable to ask how robust the results of the priority assessment are considering the large uncertainty involved in estimating parameters and making assumptions on future events. In most cases, the experts are too optimistic with regard to the future adoption and the effect of a new technology. To address this problem, it is good practise to conduct a sensitivity analysis which helps explore how sensitive the results of the assessment are to changes in the estimates of key variables.<\/p>\n

For the sensitivity analysis we have focused on those parameters which we have elicited from the resource persons (i.e. experts) rather than model inherent parameters (such as elasticities or discount rates) or those parameters populated based on (inter)national statistics (e.g. banana production area, yield or\u00a0 farm-gate prices). In order to keep this section (and the number of scenarios) manageable, we focused on the most crucial parameters which at the same time seem most prone to overly optimistic assumptions.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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  • The key parameter driving the assessment is the\u00a0adoption area<\/b>\u00a0of the new technology. In the results section you have seen that we have already included a much more conservative \u201clow adoption\u201d scenario which assumes a 50% lower adoption ceiling. For the purpose of testing the robustness of our results, we have gone even further and tested if investments would still be profitable if adoption was only 25% of what experts predicted. Even under this extremely conservative scenario, all assessed research options reach positive Net Present Values and the Internal Rates of Return are well above the 10% benchmark level. Since a reduced adoption ceiling affects all research options in the same way, the ranking of the research options is not changed.<\/li>\n
  • Furthermore, everything always takes more time than initially anticipated, thus we considered a\u00a0later start of benefits<\/b>. Delays in adoption are common and could be caused by e.g. a longer research period than anticipated, and\/or delays in the subsequent out-scaling and dissemination efforts which ensure the technology is available to the farmers. We assumed that adoption would start 2 years later than originally planned, while keeping the adoption ceiling and pace at the same level. While this reduces Net Present Values and Internal Rates of Return for all research options (and considerably so for some), all research options would still be ranked as economically viable investments.<\/li>\n
  • Only when combining the two scenarios outlined above, i.e. computing the results for much reduced adoption AND a delay, the Net Present Value and Internal Rate of Return indicators slip for some research options.<\/li>\n<\/ul>\n

    In summary, our sensitivity analysis shows that the results of the priority assessment are robust even under rather extreme scenarios such as much lower (only 25%) adoption or delays. If you are interested in the detailed results of the sensitivity analysis for (a) specific research option(s), please have a look at the sensitivity analysis section of our report.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div> <\/div> <\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"Photo Credit: S. Landersz\/Bioversity International After going through the key steps of the priority assessment, namely the identification of key constraints, description of research options, introduction of methodology used (Cost-Benefit Analysis and Economic Surplus Modelling), and account of data sources and elicitation process for parameter estimates, we can now look at the results of the...","protected":false},"author":12,"featured_media":118,"parent":56,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-116","page","type-page","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\n6. 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