CHAPTER 9 - THE ANALYTICAL PROCESS: AGROECOLOGICAL, REGIONAL, PRODUCTION SECTOR AND COMMODITY PERSPECTIVES

9.1. Introduction
9.2. The Congruence Approach
9.3. Initial Priority Setting
9.4. Modification of the Baseline
9.5. Modifiers Chosen
9.6. Data for Modifiers
9.7. Modifier Weights Chosen
9.8. Quantitative Impact of Each Modifier
9.9. Impact of Modifiers
9.10. Impacts of Changing Modifier Weights
9.11. Expected Productivity Gains
9.12. Importance of Particular Commodities for the Poor
9.13. Spillovers
9.14. Additional Inputs: The ACIAR Framework
9.15. Conclusion

9.1. Introduction

This chapter is the first of three describing the approach used by TAC to develop an analytical framework for the priority setting exercise. The approach began with agroecological zones, which were disaggregated by region before modifiers were applied to take into account concerns of efficiency, equity, sustainability, strength of national systems, self-reliance, and agroforestry. The results provide insights by agroecological zone, regional agroecological zone, region, production sector and commodity. The analysis is done in three parts - agriculture, forestry and fisheries. The next two chapters (Chapters 10 and 11) present TAC's analysis of needs and issues to be addressed by the CGIAR in institution building, and of public policy, public management and socioeconomic research issues.

The current chapter is structured as follows. Section 9.2 describes the nature of a congruence approach to priority setting. The approach requires the establishment of a baseline value for each production sector - agriculture, forestry and fisheries - in each agroecological zone. TAC selected a composite base made up of value of production, number of poor people and land area (agriculture and forestry only). This is described in Section 9.3. The rationale and mechanisms for modifying the baseline, and the modifiers chosen, are described in Sections 9.4 and 9.5. Section 9.6 provides the raw data for the modifiers chosen, while Section 9.7 discusses the weights to be attached to each modifier. Section 9.8 describes the quantitative impact on the baseline of each modifier. The results of the analysis using a uniform weight for the modifiers are presented in Section 9.9. Section 9.10 provides a sensitivity analysis of the impact of modifiers, first when all weights are changed and second when selected weights only are changed. Section 9.11 presents a discussion of inputs received from CGIAR Centres on the progress that can be expected from investment in research on particular commodities in particular agroecological zones, given critical mass. The chapter concludes (Section 9.12) with a brief review of another approach to quantitative priority setting, namely the framework developed by ACIAR.

9.2. The Congruence Approach

The congruence approach is one in which research resources are allocated according to the relative value of production by region or commodity. The approach is commonly used to assist in priority setting for resource allocation in agricultural research. It assumes that the opportunities for research to generate new knowledge to increase productivity are equal across commodities. It further assumes that the value of new knowledge produced by research is proportional to the value of output, ignoring the costs of inputs or the value added by processing.

A congruence approach can usefully be applied to the initial distribution of CGIAR priorities among agroecological zones, regions or regional agroecological zones.

However, care must be taken to restrict the analysis to parameters that measure extensity. Examples of such parameters are the value of production, the number of poor people or the area of agricultural land. Other parameters measure intensity. Examples are GDP per caput, or value of production per hectare. The congruence approach cannot be applied when intensity parameters are used because they cannot meaningfully be aggregated across regions.

9.3. Initial Priority Setting

9.3.1. An Overview
9.3.2. Baseline for Agriculture
9.3.3. Baseline for Forestry
9.3.4. Baseline for Fisheries

9.3.1. An Overview

9.3.1.1. Value of production
9.3.1.2. Poverty
9.3.1.3. Land use

TAC proposed to assign relative priorities by region and by agroecological zone initially on the basis of a weighted average of some important extensity parameters that reflect the three main concerns expressed by the CGIAR in its mission statement: the contribution of research to productivity, to the well-being of low-income people and to sustainability. To the extent that productivity is a major concern, relative priorities can be distributed in proportion to the value of production in each regional agroecological zone. If the well-being of low-income people is a major concern, priorities can be assigned in proportion to the number of poor people in each regional agroecological zone. To the extent that sustainability of land use is a major concern, priorities can be assigned in proportion to land in use (whether for agriculture, forestry, or both) in each regional agroecological zone.

The congruence approach, whether applied to value of production, number of poor people or area of land in use, should always emphasize efficiency: if research has to enhance production, it is better done where the value of production is large; if it has to alleviate poverty it is better done where the number of poor people is large; and if it has to serve sustainability, it is better done where there are large areas of land in use.

Such an initial assignment of priorities is based on broad demand considerations and does not reflect the many other important factors that have to be taken into account, such as need for research, potential for impact, capacity of national research systems to use the outputs of international research, or advantages of the research being undertaking by the CGIAR. Furthermore, the approach is based on a static concept (historical data) that reflects the past and does not allow for future changes or evolving trends.

To take these and other considerations into account, a standard procedure was developed for modifying the initial priorities by the use of intensity parameters. This procedure is discussed in Section 9.4.1. In the rest of the current section, the three extensity parameters that will determine the baseline for initial priorities are further considered.

9.3.1.1. Value of production

In Section 4.6, the value of production of the different production sectors and commodities in developing countries was discussed, both globally and by region. Crops account for 58% of the value of production of agriculture, forestry and fisheries, livestock for 18%, trees for 20% and fish for 4%. The value of each of these commodity groups by regional agroecological zone is presented in Table 9.1. The total value of production of three groups in developing countries amounts to about US$ 600 billion. In subsequent analysis, the values of crops and livestock have been aggregated into a common production value for agriculture. Crops and animal husbandry systems are interrelated to such an extent that the initial analysis is better applied to agriculture as a whole rather than to separate components.

Table 9.1. Annual gross value of production of crops, livestock, forestry and fisheries by region, AEZ and RAEZ (developing countries only, 1987-89)

	CROPS		LIVESTOCK		FORESTRY		FISHERIES1/
	US$'Mil.	SHARE %	US$'Mil.	SHARE %	US$'Mil.	SHARE %	US$'Mil.	SHARE %
SSA	34644.9	9.5	9717.9	8.0	19349.3	16.3	2070.6	6.1
1	7968.8	2.2	5077.8	4.2	5585.1	4.7	426.0	N/A
2	10384.6	2.9	1538.7	1.2	4842.7	4.1	369.3	N/A
3	11757.6	3.2	1181.9	1.0	8653.6	7.3	267.9	N/A
4	4533.9	1.2	1919.5	1.6	267.9	0.2	144.8	N/A
WANA 9	33108.2	9.1	12175.0	10.0	1825.7	1.5	1448.5	4.3
ASIA	221922.4	61.0	63937.9	52.6	72585.6	61.3	19772.5	58.8
1	21299.1	5.9	9409.4	7.8	5673.7	4.8	575.5	N/A
2	17503.9	4.8	4155.7	3.4	7925.4	6.7	409.3	N/A
3	44274.1	12.2	5739.2	4.7	34713.3	29.3	1702.5	N/A
5	46203.3	12.7	14607.2	12.0	4205.4	3.6	796.9	N/A
6	26594.4	7.3	5601.7	4.6	2525.4	2.1	481.7	N/A
7	51033.7	14.0	13241.7	10.9	4773.7	4.0	1432.1	N/A
8	15013.9	4.1	11183.0	9.2	12768.7	10.8	1185.9	N/A
LAC	73705.9	20.3	35593.2	29.3	24725.1	20.9	10349.6	30.8
1	5260.1	1.4	2774.2	2.3	862.2	0.7	34.5	N/A
2	16213.9	4.5	5478.7	4.5	5086.5	4.3	69.3	N/A
3	14255.8	3.9	5632.5	4.6	9134.8	7.7	79.1	N/A
4	7676.4	2.1	6345.4	5.2	3339.3	2.8	145.4	N/A
5	3337.6	0.9	2128.3	1.8	91.5	0.1	19.2	N/A
6	2059.6	0.6	1035.2	0.8	124.9	0.1	0.9	N/A
7	15877.3	4.4	5557.6	4.6	4236.4	3.6	70.3	N/A
8	6994.5	1.9	5590.9	4.6	653.1	0.6	6.3	N/A
9	2130.7	0.6	1050.4	0.9	1196.4	1.0	1.4	N/A
OVERALL AEZ	363381.4	100.0	121424.0	100.0	118485.7	100.0	33641.2	100.0
1	34528.0	9.5	17261.4	14.2	12121.0	10.2	1036.0	N/A
2	44102.4	12.1	11173.1	9.2	17854.6	15.1	847.9	N/A
3	70287.5	19.3	12553.6	10.3	52501.7	44.3	2049.5	N/A
4	12210.3	3.4	8264.9	6.8	3607.2	3.0	290.2	N/A
5	49440.9	13.6	16735.5	13.8	4296.9	3.6	816.1	N/A
6	28654.0	7.9	6636.9	5.5	2650.3	2.2	482.6	N/A
7	66911.0	18.4	18799.3	15.5	9010.1	7.6	1502.4	N/A
8	22008.4	6.1	16773.9	13.8	13421.8	11.3	1192.2	N/A
9	35238.9	9.7	13225.4	10.9	3022.1	2.6	1449.9	N/A

^1/Regional values of fisheries refer to inland and marine capture, while RAEZ and AEZ values only include the value of inland capture fisheries

9.3.1.2. Poverty

The second factor contributing to initial priority setting is an estimate of the number of poor people by region and agroecological zone. Regional data were obtained from a recent World Bank study, which estimated that the number of people living in absolute poverty, defined as having per caput incomes less than US$ 370 per year, amounted to 1,110 million, of whom 16% live in sub-Saharan Africa, 5% in West Asia-North Africa, 72% in Asia, and 7% in Latin America and the Caribbean (World Bank, 1990).

It proved more difficult to obtain reasonable estimates of the number of poor by agroecological zone. An analysis by IFPRI (Broca and Oram, 1991) provided some indications, but in general TAC considered that the database was too narrow and that available evidence did not allow meaningful conclusions to be drawn.

In addition, because of migration, any estimate of the number of poor people by agroecological zone would have to be treated with caution. For example, recent studies conducted by IRRI in several locations of Asia suggest that, because of migration, there are only marginal differences in wage rates between areas that have benefited from the green revolution and other areas. In Latin America, many resource-poor farmers of the high Andes have moved to lower, more fertile, areas in the valleys. In sub-Saharan Africa, migration is particularly important in the semi-arid zones of Southern and West Africa.

For the purposes of this report, the number of poor people by regional agroecological zone was therefore estimated on the basis of the regional estimates by the World Bank, disaggregated by regional agroecological zone on a pro rata basis by overall population, and adjusted for the value of GDP per caput. This estimate is to be treated with considerable caution, but was considered the most reliable available to TAC.

9.3.1.3. Land use

The third parameter used to determine initial priorities in agriculture and forestry was land use. Three categories of land use can be distinguished: cultivated land (including arable and permanent crop land), grazing land and forest land. The borders between these are not always clear because of shifting cultivation, agroforestry and fallowing. All three land-use categories have major sustainability problems (TAC/CGIAR, 1988). The weight attached to each of these categories in the land use parameter of the baseline would vary according to the production sectors. For the agriculture baseline, total area of usable land defined as arable land plus land with perennial crops plus grazing land plus forest and woodland, would be used. For the forestry baseline, only the area of forest and woodland would be incorporated in the land use component. For fisheries, land use would not be part of the baseline. Statistical information on the area of each land use category is presented in Annex 4.

9.3.2. Baseline for Agriculture

The next step in the analysis was to determine the weight to be attached to each component of the base. TAC did not wish to weight the value of production unduly because the data available were of widely varying quality. Value of production on a global basis is heavily influenced by the degree to which the commodity is traded (e.g. wheat versus yam) and by the price chosen to value output. Several commodities have no published data sources, while for others the international price reflects only a minor share of the market which is often distorted by subsidies and other government policies. International prices usually refer to high quality items. The prices used were the best available but varied from prices in the exporting country to wholesale prices in the importing country. The outcome of the analysis of value of production, therefore, has to be treated with considerable caution.

On the other hand, TAC could find no reason for giving undue weight to the other two parameters - the number of poor people and the land area. Since each is an indicator of efficiency, it was decided to weight all three equally. In terms of CGIAR goals, the highest pay off will be obtained by developing new technology where: (i) there is the highest level of production; (ii) it will benefit the largest number of poor people; and (iii) the land area available for more sustainable use is greatest.

Table 9.2 presents the results for agriculture across the 21 regional agroecological zones used in the analysis. Value of production across the regions is normalized to sum to 1000, as is the number of poor people and the total land area. These three sets of data are then averaged (equal weights) to determine a baseline value.

Table 9.3 presents the same data by region. In both tables it is clear that value of production and number of poor people favour Asia, whereas useable land shifts the emphasis more towards sub-Saharan Africa and Latin America.

Table 9.2: Baseline for priority setting by RAEZ in agriculture and its determinants (per thousand of total)

RAEZ	Weight = 0.33			Baseline
	VOP1/	Poor2/	Total usable land3/
AFRS 1	26.91	52.81	131.45	70.35
AFRS 2	24.60	35.77	52.62	37.65
AFRS 3	26.69	42.72	88.74	52.69
AFRS 4	13.24	30.70	20.91	21.61
WANA 9	93.41	54.00	75.06	74.18
ASIA 1	63.35	147.89	23.31	78.17
ASIA 2	44.68	58.27	21.52	41.49
ASIA 3	103.17	110.81	64.04	92.68
ASIA 5	125.44	142.70	32.52	100.24
ASIA 6	66.42	35.08	14.89	38.82
ASIA 7	132.59	112.05	40.31	95.02
ASIA 8	54.04	114.21	82.72	83.63
LAC 1	16.57	5.19	27.68	16.48
LAC 2	44.75	9.13	77.77	43.88
LAC 3	41.03	12.39	107.11	53.50
LAC 4	28.92	20.28	42.11	30.44
LAC 5	11.07	1.84	12.16	8.36
LAC 6	6.38	0.48	6.43	4.43
LAC 7	44.22	8.15	36.03	29.48
LAC 8	25.96	3.37	32.78	20.71
LAC 9	6.56	2.17	9.83	6.19
	1000.00	1000.00	1000.00	1000.00

^1/ VOP = Value of Production
^2/As estimated by the World Bank by region (World Bank, 1990), Subsequent distribution by AEZ pro rata with population adjusted with GDP/caput.
^3/Total usable land defined as arable land plus land with perennial crops plus grazing land plus forest and woodland

Table 9.3. Baseline for agriculture: distribution by region (%)

Weight =0.33				Baseline
Factor	VOP	No. of Poor	Usable Land
Region SSA	9.1	16.2	29.4	18.2
WANA	9.3	5.4	7.5	7.4
ASIA	59.0	72.1	27.9	53.0
LAC	22.6	6.3	35.2	21.4
WORLD	100.0	100.0	100.0	100.0

9.3.3. Baseline for Forestry

TAC debated thoroughly the appropriate weights for the baseline in forestry. Equal weighting, as in agriculture, was initially proposed, but three arguments eventually persuaded TAC to adopt different weights. First, in adopting the recommendations of Bellagio II the CGIAR has agreed that its focus in forestry and agroforestry should be limited to areas which largely exclude large-scale commercial forest production and utilization. Given that much of the global value of production comes from commercial log and timber production, TAC felt less weight should be given to value of production. Second, it is very difficult to value forest products used by the poor, such as fuelwood and charcoal. There was a concern that these and other products and services may be undervalued, again biasing research towards areas with commercial timber production and exports. Third, multiple use of the forest, and the opportunities for preserving it, are both much enhanced by the area of wooded land available.

TAC therefore decided that the weighting for the number of poor people should be the same, 0.33, that half the total weight, 0.5, should go to wooded area, and that the residual, 0.17, should be allocated to value of production. These weights are arbitrary but are based on TAC's best judgement. The results by regional agroecological zone are presented in Table 9.4 and by region in Table 9.5. Value of production and the number of poor people favour Asia, whereas wooded area emphasizes sub-Saharan Africa and Latin America and the Caribbean.

Table 9.4. Baseline for priority setting by RAEZ in forestry and its determinants (per thousand of total)

Weight	0.17	0.33	0.50	Baseline
RAEZ	VOP	Poor	Forest and Woodland
AFRS 1	46.14	52.81	101.00	75.77
AFRS 2	40.01	35.77	56.00	46.61
AFRS 3	71.49	42.72	139.00	95.75
AFRS 4	23.31	30.70	11.00	19.59
WANA 9	15.08	54.00	27.00	33.88
ASIA 1	46.87	147.89	14.00	63.77
ASIA 2	65.48	58.27	27.00	43.86
ASIA 3	286.79	110.81	102.00	136.32
ASIA 5	34.74	142.70	9.00	57.50
ASIA 6	20.86	35.08	7.00	18.62
ASIA 7	39.44	112.05	9.00	48.18
ASIA 8	105.49	114.21	57.00	84.12
LAC 1	7.12	5.19	39.00	22.42
LAC 2	42.02	9.13	118.00	69.16
LAC 3	75.47	12.39	178.00	105.92
LAC 4	27.59	20.28	46.00	34.38
LAC 5	0.76	1.84	10.00	5.74
LAC 6	1.03	0.48	2.00	1.33
LAC 7	35.00	8.15	23.00	20.14
LAC 8	5.40	3.37	13.00	8.53
LAC 9	9.88	2.17	12.00	8.40
	1000.00	1000.00	1000.00	1000.00

Table 9.5. Baseline for forestry: distribution by region (%)

Weight	0.17	0.33	0.50	Baseline
Factor	VOP	No. of Poor	Forest and Woodland
Region SSA	18.1	16.2	30.7	23.8
WANA	1.5	5.4	2.7	3.4
ASIA	60.0	72.1	22.5	45.2
LAC	20.4	6.3	44.1	27.6
WORLD	100.0	100.0	100.0	100.0

9.3.4. Baseline for Fisheries

TAC has far fewer data on fisheries than on forestry and agriculture. Further, land area is less relevant to most fisheries research issues, and the terrestrial agroecological zones identified for agriculture and forestry are much less applicable to fisheries. Thus the base for fisheries consisted of two factors only - value of production and number of poor people - each weighted equally (0.5). The results by region are presented in Table 9.6.

Having determined the baseline values for agriculture, forestry and fisheries, TAC then turned to the question of modifiers.

Table 9.6: Baseline for fisheries: distribution by region (%)

Factor	Weight = 0.5		Baseline
	VOP	No. of Poor
Region SSA	8.0	16.2	11.2
WANA	4.0	5.4	4.9
ASIA	56.0	72.1	65.4
LAC	32.0	6.3	18.5
WORLD	100.0	100.0	100.0

9.4. Modification of the Baseline

9.4.1. Standard Procedure
9.4.2. Selection of Modifiers

9.4.1. Standard Procedure

The initial allocation of priorities based on value of production, number of poor people and land area does not take into account several other important factors that also determine CGIAR priorities. A standardized approach was therefore developed for modifying the initial baseline by the use of intensity parameters. As an example, GDP per caput is used as a possible equity modifier for agriculture.

The CGIAR is particularly interested in improving the welfare of low-income people. Although the number of poor people by region and agroecological zone is one of the three elements that compose the baseline, there are good reasons for modifying this baseline with measures that reflect the intensity of poverty in a particular area. For reasons of equity, higher priority should be given to areas where income levels are generally low. In such areas, GDP per caput is usually also low.

Table 9.7. Example of a modifying step

		SSA	WANA	ASIA	LAC	TOTAL
1	Baseline relative priority (agr.)	182.3	74.18	530.06	213.47	1000.00
2	GDP/caput (US$)	294.0	1544.0	448.0	1847.0
3	Standardized max. at 1 (row2/max. value row2)	0.16	0.84	0.24	1.0
4	Take complement (1-row3)	0.84	0.16	0.76	0.0
5	Attach weight.5 (.5*row4)	0.42	0.08	0.38	0.0
6	Gross redistribution (row1*row5)	75.6	5.9	202.2	0.0	283.7
7	Baseline reduction (row1 *total row6/1000)	51.1	21.0	150.9	60.7	283.7
8	Net change to baseline (row6-row7)	+24.5	-15.1	+51.3	-60.7	0.0

Table 9.7 shows how the modifier GDP per caput affects the allocation of priorities by region. The modifier is weighted at 0.5. The initial baseline for agriculture is given in row 1. The values for GDP per caput by region are presented in row 2. In row 3, the range is then standardized by dividing the values in row 2 by the highest value of GDP per caput in row 2. Because in this particular case highest priority will be given to the region with the lowest GDP per caput, the order is reversed in row 4 by subtracting the values in row 3 from 1. This value is now adjusted for the weight of the modifier 5 (row 5). The values are only half those of row 4, because a weight of 0.5 was attached to this modifier. The baseline data of row 1 are multiplied with the numbers in row 5 to give row 6, which estimates the gross redistribution. The baseline reduction is estimated in row 7 and the net change to the baseline in row 8. The value obtained in row 8 indicates the difference and the effect of the modifier by region.

The relative priorities of Asia and sub-Saharan Africa, where GDP per caput is low, increase by 51.3 and by 24.5 respectively, while the priority rankings of West Asia-North Africa and Latin America and the Caribbean are reduced. It should be stressed that Table 9.7. is for illustrative purposes only and is simplified, as it only takes into consideration regional values. As will be discussed in Section 9.8, the analysis has been done for each modifier by agroecological zone, by region and by regional agroecological zone.

The values obtained through this procedure for each modifier are then aggregated for each agroecological zone, region, and regional agroecological zone and added to the initial distribution of relative priorities, keeping the overall total constant at 1,000. As a result, the modified baseline is also obtained on an overall total of 1,000. The order in which modifiers are applied does not influence their impact.

The effect of a modifier depends on the weight it has been assigned and on the spread or variability of its value across regional agroecological zones. One may attach a large weight to a certain modifier, but if its values do not differ much among regional agroecological zones, its effect on the distribution of priority will be small. For example, if the value of GDP per caput had been 100 in sub-Saharan Africa, 105 in Asia, 110 in West Asia-North Africa and 115 in Latin America and the Caribbean, the effect of this modifier would have been negligible. The greater the spread of values, the stronger the effect of the modifier. The variability within the data set therefore gives a certain implicit weight to the effect of a modifier.

Another issue is the direction in which intensity parameters are weighted. In the example of Table 9.7, greater weight was given to areas where GDP per caput was small. One could argue, however, that for reasons of efficiency, greater weight should be given to areas where GDP per caput is high. Such areas are likely to have strong national research systems, so that the CGIAR could limit its activities to the strategic germplasm research for which it has a strong comparative advantage. If this argument were accepted, row 4 in the table would then have to be deleted and the effect of the modifier altered accordingly.

The same data set can be used to estimate both extensity parameters and intensity parameters. A good example is the number of poor people. This was used as an important extensity parameter in calculating the baseline, but it can be logically transposed into an intensity parameter by expressing it as a percentage of the total population in the region. Using the data on the number of poor people in both parameters is not double counting, for it expresses two different concerns. Using the absolute number of poor as an extensity parameter would ensure that higher priority was given to areas with large numbers of poor. Using the proportion of poor people out of the total population in a region could ensure that higher priority is given direct to regions where poverty is particularly severe.

The proposed framework is not an optimizing procedure, but aims only at clarifying choices. By following this approach in its priority setting exercise, TAC makes it clear how priorities are arrived at, and the process remains transparent. TAC is then in a better position to engage in reasoned dialogues with other stakeholders in the process. A more detailed discussion of the use of this analytical framework is provided in De Wit, Gryseels and Van Kraalingen (1992).

9.4.2. Selection of Modifiers

TAC considered over 20 possible modifiers for agriculture and 10 for forestry that might be used to take into account: (1) the special nature of the CGIAR as an international organization; (2) alternative sources of research supply; (3) the strength of national research programmes; (4) the nature of self-reliance; (5) concerns for the efficiency of research; (6) equity issues; (7) sustainability; and (8) special issues.

In the end TAC retained 10 modifiers, 9 of which were used for agriculture, 6 for forestry and 1 for fisheries. The evaluation first attempted to determine whether or not the modifier was appropriate for the task. For example, a modifier for the share of urban population was proposed but rejected because it was not clear why urban dwellers should get more or less attention than other members of the population. Secondly, TAC discarded modifiers which duplicated others. For example, agricultural GDP per labourer as a measure of rural poverty is already captured by GDP per caput as a measure of overall poverty. Some modifiers were deemed appropriate but inadequate data were available to quantify them, particularly, for example, with respect to sustainability issues.

Since the baselines already represented considerations of efficiency, only one further efficiency modifier was chosen. This is the yield gap, or in other words the difference between potential yields and actual performance. Where that gap is narrow, it was judged that higher priority should be given because strategic research would be critical to raising the yield potential. Two modifiers - intensity of malnutrition and GDP per caput - reflect different concerns about equity. Three - "urgency", magnitude of deforestation, and soil degradation risk - address issues of sustainability. Two attempt to deal with issues of strength of national research systems - capacity of national system and country size. One attempts to address the issue of self-reliance and one the preservation of forest resources and the potential for agroforestry. Each of these modifiers is described in more detail in the next section.

In its selection of modifiers, TAC chose those which in its judgement best reflected the multifaceted character of the CGIAR's mission and goals. For example, sustainability is multifaceted and involves soil erosion, agricultural encroachment on forests, siltation of reservoirs, inappropriate deforestation and many other problems. TAC chose three modifiers to reflect these multiple dimensions, recognizing that the intensity of particular dimensions would vary across agroecological zones. The selection of more than one dimension lessens the regional distortions that might occur if one modifier only was relied on.

9.5. Modifiers Chosen

9.5.1. Efficiency Indicator
9.5.2. Equity Indicators
9.5.3. Sustainability Indicators
9.5.4. Strength of National Research System Indicators
9.5.5. Food Import Gap
9.5.6. Preservation of forest resources

9.5.1. Efficiency Indicator

9.5.1.1. Yield gap or scope for growth

9.5.1.1. Yield gap or scope for growth

In developing countries, the actual productivity of agricultural land is well below its sustainable potential. Potential crop productivity can be defined as the productivity of cropping systems with varieties that are optimally adapted to the prevailing agroecological conditions, free of insects, pests, diseases and weeds, and under optimal nutrient conditions. The larger the difference between potential and actual productivity, the greater the opportunity to obtain yield increases. When the difference approaches zero, efforts are needed to increase the stable biological yield ceilings of crops. On the basis of studies by FAO, an estimate was made of the ratio between actual and potential land productivity by region and by agroecological zone.

It was assumed that production potentials reflect maximum attainable productivity using current production technologies. Maximum attainable yields vary by crop and by agroecological zone, and the total current productivity potential for each zone is conditioned by the current crop mix. Estimates of production potentials of presently cultivated land in each agroecological zone (Table 9.8) were derived from the FAO Agriculture Towards 2000 information base, using current cropping patterns. Estimates of productivity potentials (C) vary from 22 million tonnes in AEZ 9 of Latin America and the Caribbean to 406 million tonnes in AEZ 7 of Asia. At the aggregate regional level, C varies from 229 million tonnes in West Asia-North Africa to 1,841 million tonnes in Asia.

Information on the current production of food crops (B1) and of cash crops has already been provided in Section 4.3 (Table 4.2). Setting current food production (B1) against production potentials (C) permits a quantification of how much additional food production is possible without further expansion in the cultivated area. The ratio (C-B1)/C therefore provides an estimate of the "yield gap" or scope for growth of food production on presently cultivated land. The yield gap is generally greater in sub-Saharan Africa (0.82) and in Latin America and the Caribbean than in Asia (0.60) and West Asia-North Africa.

Table 9.8. Potential productivity (C), scope for growth in food production (C-B1)/C, Production of Food and Cash Crops in 1990 (B2), annual growth in food demand over the period 1990-2010 (D) and the need for production growth (U) by RAEZ

RAEZ	C (106 GE)	(C-B1)/C	B2 (106t GE)	D (%)	U (%)
SSA	574.3	0.82	176.5	3.38	2.21
1	117.2	0.72	42.0	3.30	2.98
2	183.7	0.88	35.9	3.45	2.37
3	210.1	0.84	69.1	3.50	1.83
4	63.3	0.77	31.5	3.16	1.77
WANA	229.0	0.72	87.6	2.93	3.47
1	0.6	0.48	0.4	4.02	5.07
4	4.4	0.80	1.2	3.90	5.80
9	224.0	0.71	86.0	2.89	3.37
Asia	1 840.7	0.60	969.3	1.91	1.45
1	206.6	0.45	127.5	1.89	1.71
2	128.7	0.46	95.3	2.03	1.27
3	309.5	0.60	182.9	2.55	1.72
5	329.0	0.64	183.0	2.32	1.53
6	141.3	0.62	91.1	1.71	1.15
7	406.0	0.66	169.5	1.33	1.08
8	319.6	0.64	120.4	1.44	1.40
LAC	669.3	0.79	260.5	2.28	1.17
1	30.0	0.61	16.0	2.06	1.41
2	129.6	0.84	53.4	2.38	0.99
3	103.4	0.77	50.6	2.32	1.15
4	70.5	0.53	61.4	2.49	1.54
5	27.6	0.84	6.1	2.27	1.71
6	29.5	0.90	4.0	1.35	0.44
7	111.9	0.82	42.0	2.36	1.06
8	144.8	0.86	22.7	1.42	0.60
9	22.0	0.82	4.3	1.89	1.93
Overall	3313.3	0.68	1 493.9	2.23	1.63
1	354.4	0.55	185.9	2.28	2.00
2	442.0	0.74	184.5	2.46	1.40
3	519.6	0.70	279.1	2.78	1.66
4	133.8	0.64	91.2	2.70	1.62
5	356.6	0.66	189.1	2.32	1.53
6	170.8	0.67	95.1	1.70	1.12
7	517.9	0.69	211.5	1.46	1.08
8	464.4	0.71	143.0	1.44	1.27
9	246.0	0.72	90.3	2.85	3.41

GE - Grain equivalent
C -Potential productivity of presently cultivated land.
B1 - Present annual production of food crops.
B2 - Present annual production of food and cash crops.
D - Annual growth in demand for food over the period 1990-2010.
U - Increase in food demand as a percentage of 1990 food and cash crop production.

The CGIAR takes an interest in both situations. In sub-Saharan Africa there are many opportunities to obtain improvements in crop productivity through the application of technology resulting from applied and adaptive research. In Asia there is a much greater need for strategic research that aims at fundamental biotechnological breakthroughs that increase yield potentials.

Given the capacity of the CGIAR to conduct strategic research, TAC considers that, among regions and agroecological zones, higher priority should be given to those in which the scope for growth is low. This would particularly favour all the regional agroecological zones of Asia (except RAEZs 7 and 8), and RAEZs 1 and 4 of Latin America and the Caribbean. All the regional agroecological zones of sub-Saharan Africa and West Asia-North Africa would be assigned lower priority as a result. TAC considers that in the latter areas there is a particular need for greater efforts in institution building. The yield gap or scope for growth modifier is therefore particularly useful in providing guidelines on the type of activity that could be undertaken by the CGIAR.

9.5.2. Equity Indicators

9.5.2.1. Malnutrition
9.5.2.2. GDP per caput

9.5.2.1. Malnutrition

The CGIAR mission statement stresses that the System's activities should enhance nutrition and well-being in developing countries, especially among low-income people. In determining the baseline values, the absolute number of poor in each region and agroecological zone was therefore explicitly considered.

TAC considers that, in addition, higher priority should be assigned to areas where poverty and malnutrition are particularly severe. Malnutrition is reflected in the number of children under five that are underweight, defined as two standard deviations below desirable weight for age. According to UNICEF, about 36% of children under five in developing countries excluding China, or 150 million children, are malnourished by this criterion. About 39% of children are stunted, as measured by height for age, while 8.4% or 35 million children are wasted, as measured by desirable weight for height (Carlson and Wardlaw, 1990).

Thus, in the developing world, more than one child in three is suffering from malnutrition. In the case of wasting, which indicates acute malnutrition, one child in 12 is affected. Of the 150 million children that are malnourished (excluding China), 75% live in Asia, 19% in sub-Saharan Africa and 6% in Latin America and the Caribbean. Data for West Asia-North Africa and data on the intensity of this measure (number of children malnourished as a proportion of total number of children in each region) are not available.

Pending the availability of a more appropriate data set, TAC considered that the number of malnourished people in proportion to total population for each region would serve as an adequate proxy. This proportion has been estimated at 35% in sub-Saharan Africa, 22% in Asia, 9% in West Asia-North Africa and 14% in Latin America and the Caribbean (FAO, 1991b).

9.5.2.2. GDP per caput

GDP per caput is generally accepted as an indicator of the income status of a country. To allow its use as a modifier, an analysis was made of GDP per caput by regional agroecological zones. For reasons of equity, TAC considers that higher priority should be given to areas where GDP per caput is low. This would favour areas with generally low income levels.

There was a second reason why TAC considered that GDP per caput would be an appropriate modifier. The CGIAR is only one component of the global agricultural research system. Many other institutes and agencies conduct research, in both the public and the private sectors. In assigning priorities to geographic areas, the existence of these other suppliers of research should be taken into account, since the CGIAR should conduct only those activities that it can undertake more effectively than any other agency. It is difficult, however, to quantify the extent of alternative sources of supply. The data are incomplete, relate to certain countries and commodities only, and particularly to the public sector.

In view of the lack of a quantitative indicator of alternative sources of research supply, TAC considered that an appropriate alternative would be an indicator that would reflect the ability of an area to finance its own research services. GDP per caput is a suitable indicator for this purpose. Where it is low, the area has limited capacity to finance its own research services. Such areas should receive higher priority.

The use of GDP per caput as a modifier favours all the regional agroecological zones of sub-Saharan Africa and Asia, except RAEZ 6 in Asia. On average, GDP per caput amounts to US$ 294 in sub-Saharan Africa, US$ 448 in Asia, US$ 1544 in West Asia-North Africa and US$ 1847 in Latin America and the Caribbean.

9.5.3. Sustainability Indicators

9.5.3.1. Urgency of need for production growth
9.5.3.2. Deforestation
9.5.3.3. Soil degradation risk

9.5.3.1. Urgency of need for production growth

An important factor determining priorities among different geographic areas is the pressure on agricultural production to meet future demand. As already discussed, the sustainability of agricultural production is at risk in many developing countries as a result of population growth, increased and changing demand for food, and the depletion of natural resources.

In Section 4.3, an estimate was made of the increases in agricultural output required between 1990 and 2010 to achieve food self-reliance for each regional agroecological zone. Information was also presented on present (year 1990) and future (year 2010) demand for food, as estimated on the basis of population size and demand per caput. Given the production of food and cash crops in 1990 (B2 as shown in Table 9.8), an estimate can now be made of the annual growth in food demand over the period 1990-2010 (see D in Table 9.8). Growth in demand varies from 3.38% in sub-Saharan Africa to 1.91% in Asia.

Increases in food demand in grain equivalent as percentages of food and cash crop production in 1990 can now also be estimated (see U in Table 9.8.). This parameter reflects the urgency of the need for production growth. The value of U is calculated by using the value of D to estimate the actual demand for food in 1990 and stating this as a percentage of the present annual production of food and cash crops (B2). The value of U averages 1.63% when all developing countries are combined, but varies from 3.47% in West Asia-North Africa, to 2.21% in sub-Saharan Africa, to 1.45% in Asia and to 1.17% in Latin America and the Caribbean. Urgency also varies considerably within each region. In sub-Saharan Africa, for example, it is 2.98% in the semi-arid tropics but only 1.83% in the humid tropics. In Latin America and the Caribbean, it ranges from 0.6% in AEZ 8 to 1.93% in AEZ 9. The higher the value the more urgent the need for growth in production, the greater the pressure on marginal and fragile land, and the higher the priority of the area.

When taken together, the parameters U (urgency) and (C-B1)/C (yield gap or scope for growth) reveal the different nature of the production challenge in different agroecological zones. An urgent need for production increases in an area with relatively small scope for growth (e.g. in AEZ 9 of West Asia-North Africa and AEZ 1 of sub-Saharan Africa) will probably lead to a food crisis and to heavy pressure on natural resources. For areas where high demand for growth is combined with relatively high scope for growth, as in AEZs 6 and 8 of Latin America and the Caribbean, the prospects are more favourable.

9.5.3.2. Deforestation

Research has an important role to play in combatting deforestation. Often, it is not practical to preserve forest on good agricultural land because cultivation would provide a higher return, but trees in farmland play important roles in sustaining agricultural production. In many cases, deforestation can be slowed down by improving productivity and resource management in adjacent agricultural areas. However, this approach is unlikely to halt deforestation altogether.

The higher the rate of deforestation, the higher the priority that should be assigned to a particular area. Deforestation rates vary considerably, from 1.7% in sub-Saharan Africa, through 1.4% in Latin America and the Caribbean, to 1% in West Asia-North Africa and 0.9% in Asia.

However, the absolute area deforested is much larger in Latin America and the Caribbean than elsewhere. TAC considers that, rather than rate of deforestation, a more appropriate indicator of CGIAR priorities would consist of the share of each region's deforested area in the total area deforested in developing countries annually. It has been estimated that a total of 16.8 million ha are deforested in developing countries every year, 45% of this in Latin America and the Caribbean, 38% in sub-Saharan Africa, 15% in Asia and 2% in West Asia-North Africa. The modifier is estimated as the total area deforested in each region each year divided by the priority baseline.

9.5.3.3. Soil degradation risk

The sustainability of agricultural production is a key issue in considering CGIAR priorities. Soil degradation is a major threat to sustainability in several areas of the developing world. A case could be made that the higher the degree of degradation of arable land in a given area, the higher the priority that should be assigned to that area.

Distilling a single quantitative indicator of the state of soil resources in different regional agroecological zones was difficult. Soil constraints data could not be used because they concern total rather than arable land area. Furthermore, it is necessary to select particular constraints because the sum of all constraints is not useful in discriminating amongst different regional agroecological zones. The latter problem is also presented by the data on human-induced land degradation, which are, in any case, only partly quantitative.

It was therefore decided to use estimates of the effects of water and wind erosion on the productivity of rainfed land; these are based on the FAO population supporting capacity study (FAO, 1982). This model uses climatic (rainfall and wind erosion) indices, soil, terrain, texture and vegetation/land use factors under situations where no conservation measures are applied. It overcomes some of the problems mentioned above.

Over the four developing regions as a whole, the area of potential rainfed cropland is reduced in the long run by 24.7% if the full rate of soil erosion remains unchecked. At the regional level, the highest risk of degradation exists in Asia (35.6% decrease in cropland), followed by West Asia-North Africa (20.1%) and sub-Saharan Africa (16.5%). Latin America and the Caribbean are less at risk from soil erosion (11.4%).

Within the tropics, the humid zones carry the highest risk of soil erosion, followed by the subhumid zones. Cool tropical zones and the warm semi-arid tropics carry similar levels of risk. In the subtropics with summer rainfall, the warm humid zone and the cool zone in Asia are at high risk. In Latin America and the Caribbean, all the subtropical zones are at low risk.

9.5.4. Strength of National Research System Indicators

9.5.4.1. Capacity of national research systems
9.5.4.2. Small countries

9.5.4.1. Capacity of national research systems

The effectiveness of the CGIAR depends on the ability of national research systems to identify a priority research agenda, use the products of international research, and conduct collaborative research. The CGIAR mission to help resource-poor farmers implies a need to build capacity in national research systems, so that they can do an effective job in bringing new technology to these ultimate clients. However, the equity consideration implied by the objective of strengthening weak national systems may compromise short-term economic efficiency by diverting resources away from servicing strong national research systems which are already effective partners.

The CGIAR works with both strong and weak national research systems. The strength of a national system to some extent determines the kind of collaborative activity it undertakes with CGIAR Centres. Traditionally, research activities with weak national systems have involved a higher level of collaborative applied and adaptive research -partly as a means of transferring technology, partly as a form of capacity building and technical assistance. Work with stronger national systems tends to be more strategic in nature. However, although conducted in collaboration with stronger systems, strategic research nevertheless produces results that eventually reach smaller, weaker systems.

TAC has considered both quantitative and qualitative information to incorporate the status of national research systems by region and agroecological zone into its priority setting. Some of this information is contained in a background paper from ISNAR (Pardey and Roseboom, 1991).

It proved difficult to select a single indicator for the strength of national research systems, but one good proxy is the number of scientists by regional agroecological zone. This was estimated also by Pardey and Roseboom (1991), and of the total number of 76,174 scientists in developing countries, 6% were located in sub-Saharan Africa, 72% in Asia, 12% in Latin America, and 10% in West Asia-North Africa.

The ratio of number of scientists by RAEZ represents the density of scientists in each area. The strengthening of national research systems is an important mission of the CGIAR. TAC therefore considers that greater weight should be given to areas with lower densities.

9.5.4.2. Small countries

Large countries such as India and China have strong national research systems. However, many small countries lack the resources and the capacity to set up a comprehensive research system of their own. To provide effective research services, they rely especially on networks and on collaboration with other national and international research institutions. The smaller the country, the greater the difficulty in achieving a critical mass of resources and scientists for a given research activity.

TAC considers that higher priority should be given to areas that consist predominantly of small countries. The modifier used was the average size of countries within a regional agroecological zone, related to the baseline value of priorities. This indicator particularly favours Central America and the Caribbean, West Africa, and West Asia-North Africa.

9.5.5. Food Import Gap

The CGIAR has included the notion of self-reliance in its mission statement to replace the previous implicit goal of self-sufficiency. TAC wished to introduce a modifier to reflect this new consideration. A comprehensive measure would require a complex analysis of each country's comparative advantages across sectors to determine whether it had a resource base sufficient to feed its population either by domestic production or by exports to pay for imported food. This could not be done. However, TAC was aware of a recent IFPRI study (Ezekiel, 1989) which projected food aid needs by country and region for the year 2000. The study used standard methods to make linear projections of potential production. It projected aggregate demand based on population and income growth rates weighted by the income elasticity of demand. Agricultural (food) exports were projected to increase at the rate of production growth while commercial imports were projected to grow at the same rate as GNP. Thus, the difference between production plus imports and demand minus exports was identified as the gap that would need to be filled by food aid. The gap indicates the potential magnitude of import needs after taking into account potential agricultural exports.

TAC used these estimates by region as an indicator of the difficulty the region would have in feeding itself. In the analysis, regions with larger food aid gaps were given greater weights.

9.5.6. Preservation of forest resources

As already discussed in Section 9.5.3.2, deforestation can often only be slowed down by improving productivity and resource management in adjacent agricultural areas. The encroachment on forests by agriculture not only has unfavourable environmental consequences, but also causes fuelwood scarcity. In such areas, high priority should be given to agroforestry. TAC considered wooded area per caput to be an appropriate indicator of the pressure on forest resources. Greater weight should be given to areas where the wooded area per caput is low. Wooded area per caput amounts to 2.15 ha in Latin America and the Caribbean, 1.33 ha in sub-Saharan Africa, 0.19 ha in West Asia-North Africa and 0.18 ha in Asia. This indicator particularly favours the Asia and West Asia-North Africa regions, as well as the cool tropics of sub-Saharan Africa.

9.6. Data for Modifiers

Table 9.9 presents an overview of the values of the data used to estimate the modifiers chosen, by region and agroecological zone. Data on malnutrition and deforestation were available only at the regional level.

TAC acknowledges that the quality of the data set could be improved. It was particularly difficult to disaggregate data available on a country basis so that they would fit into an agroecological zone framework. However, TAC considered that, as priority setting is a continuing activity, well informed "guestimates" could be used when more reliable data were not available. The Committee will seek to improve the quality of the data set over time.

9.7. Modifier Weights Chosen

Having selected the modifiers listed above it remained for TAC to decide what weights should be attached to each.

In the debate on weights, TAC had three major concerns. First, different weights among modifiers could re-introduce undesirable distortions. Second, the level of a weight directly impacts on the baseline in a particular agroecological zone in proportion to the inter-regional differences in the value of the basic indicator. Thus, there are already implicit differences in the impact that each modifier will have on the base. And third, unless compelling reasons could be found to weight modifiers differently, equal weights would be the least distorting option.

At the end of the debate it was TAC's collective judgement that weights should be equal across modifiers. The remaining question was, at what level should those weights be fixed? The initial approach was to assume a weight of 1 as it seemed the most neutral. But clearly, any weight, including zero, is arbitrary in the absence of objective explanatory variables. TAC decided that weights in excess of 1 would give undue importance to modifiers that strongly discriminated among regional agroecological zones. In its analysis, the Committee examined the impact of three levels of weights - 0.25, 0.5, and 1.0. Given the linearity of the analytical process these were sufficient to determine the trend in the impact of each modifier.

Table 9.9. Value of Modifiers by Region and Agroecological Zone

	SSA	1	2	3	4	WANA
1. Yield gap or scope for growth	0.82	0.72	0.88	0.84	0.77	0.72
2. Malnutrition (% population malnourished)	35					9
3. GDP/caput (US Dollars)	294	291	255	379	185	1544
4. Production growth needed to meet demand (% p.a.)	2.21	2.98	2.37	1.83	1.77	3.47
5. Deforestation ('000 ha)	6400					300.0
6. Soil degradation hazard (% rainfed cropland)	16.5	10.8	15.2	28.8	10.6	20.1
7. Capacity of NARS (no. of scientists)	4917	1974	1150	1101	612	7836
8. Size of countries (no. of countries)		26	16	15	8	21
9. Food import gap by 2000 (MMT)	25.95					19.07
10. Wooded area/caput (ha)	1.33	1.32	1.14	1.98	0.31	0.19

	ASIA	1	2	3	5	6	7	8
1. Yield gap or scope for growth	0.60	0.45	0.46	0.60	0.64	0.62	0.66	0.64
2. Malnutrition (% population malnourished)	22
3. GDP/caput (US Dollars)	448	298	424	490	304	1043	504	368
4. Production growth needed to meet demand (% p.a.)	1.45	1.71	1.27	1.72	1.53	1.15	1.08	1.40
5. Deforestation ('000 ha)	2500
6. Soil degradation hazard (% rainfed cropland)	35.6	29.2	31.1	63.0	17.9	17.9	46.0	46.2
7. Capacity of NARS (no. of scientists)	54558	4436	2630	6095	9884	4772	14416	12325
8. Size of countries (no. of countries)		2	4	17	3	4	2	7
9. Food import gap by 2000 (MMT)	2.55
10. Wooded area/caput (ha)	0.18	0.07	0.26	0.47	0.05	0.07	0.04	0.30

	LAC	1	2	3	4	5	6	7	8	9
1. Yield gap or scope for growth	0.79	0.61	0.84	0.77	0.53	0.84	0.90	0.82	0.86	0.82
2. Malnutrition (% population malnourished)	14
3. GDP/caput (US Dollars)	1847	1887	2061	1758	1504	2029	2458	2109	2422	1750
4. Production growth needed to meet demand (% p.a.)	1.17	1.41	0.99	1.15	1.54	1.71	0.44	1.06	0.60	1.93
5. Deforestation ('000 ha)	7600
6. Soil degradation hazard (% rainfed cropland)	11.4	12.0	17.1	26.0	10.4	9.1	12.1	4.9	5.0	7.3
7. Capacity of NARS (no. of scientists)	8861	636	1664	1702	1367	392	169	1831	2813	289
8. Size of countries (no. of countries)		9	14	21	9	2	1	3	2	2
9. Food import gap by 2000 (MMT)	6.3
10. Wooded area/caput (ha)	2.15	2.62	2.48	5.10	0.77	1.68	0.93	0.99	1.04	1.76

In the tables that follow in Section 9.8 the impact of each modifier by agroecological zone, region and regional agroecological zone is explored using a uniform weight of 0.5. In Section 9.10 the sensitivity of the results to different levels of uniform weights and different weights among modifiers are presented. This is done in the interests of transparency, and to allow other stakeholders in the System to present arguments for proceeding differently in subsequent rounds of the analysis.

9.8. Quantitative Impact of Each Modifier

9.8.1. Agriculture
9.8.2. Forestry
9.8.3. Fisheries

9.8.1. Agriculture

In Table 9.10, the actual impact of each modifier when weighted at 0.5 is displayed by region, by agroecological zone and by regional agroecological zone. Looking down a column shows two things: (i) whether the modifier had a positive or negative impact on the distribution of priority; and (ii) by how much.

For example, modifier 1 (yield gap) has a relatively small negative impact on all four agroecological zones of sub-Saharan Africa (AFRS 1-4) and a large positive impact on Asia 1. Looking across a row shows how a regional agroecological zone is impacted by a modifier and by how much. For example, looking across WANA 9 we see that yield gap (modifier 1) subtracts 1.9 from the West Asia-North Africa baseline, malnutrition (2) subtracts 13.46, GDP per caput (3) subtracts 11.0, but that urgency (4) adds 18.9 to the base, and so on across the row. The net effect of all of the modifiers is to increase the West Asia-North Africa base by 6.34, despite the fact that six of the nine modifiers subtract from it. Clearly the largest impact on the West Asia-North Africa base comes from modifier 9 (food import gap).

The table also allows the reader to compute what would happen to a regional agroecological, regional or agroecological base if one or more modifiers were removed. If you wish to change the direction in which a modifier is used, simply invert all the signs (for example, if in your opinion greater weight rather than less should be given to areas where the density of scientists is high and national programmes are strong). The impact of alternative weights can also easily be considered by adjusting the impact value proportionally. For example, the impact of a modifier weighted at 1.0 can be computed by doubling the value of impact of the modifier at 0.5.

Table 9.11 displays the quantitative impacts (plus or minus) of each of the modifiers on each of the regional agroecological zones, regions and agroecological zones. Several things are clear. First, the various modifiers impact differently on each agroecological zone and region: no agroecological zone or region is favoured or disfavoured by all modifiers. That is, looking across any agroecological zone or region one does not find a consistent pattern of all pluses or all minuses. Second, the net effect of all modifiers is positive for all tropical agroecological zones (AEZs 1-4) and negative for all subtropical agroecological zones (AEZs 5-9) except AEZ 9, which is found mostly in West Asia-North Africa. Third, it follows from the agroecological impacts that the sub-Saharan Africa base is increased by the net effect of all modifiers because this region contains only tropical agroecological zones. The West Asia-North Africa base is also increased by the application of all the modifiers.

Table 9.10. Quantitative impact of agricultural modifiers at weight = 0.5

BASELINE RELATIVE PRIORITY	NUMBER	1	2	3	4	5	6	7	8	9	NUMBER
	NAME MODIFIER	YIELD GAP	MALNUTRITION	GDP/CAPUT	URGENCY	DEFORESTATION	SOIL DEGRADATION	STRENGTH OF NARS	AV. SIZE OF COUNTRY	FOOD IMPORT GAP	NAME MODIFIER
	WEIGHT POS./NEG.	0.5 NEG.	0.5 POS.	0.5 NEG.	0.5 POS.	0.5 POS.	0.5 POS.	0.5 NEG.	0.5 NEG.	0.5 POS.	WEIGHT POS./NEG.
70.3	AFRS1	-1.8	13.37	7.5	13.0	18.5	-9.5	11.1	7.9	6.0	AFRS1
37.7	AFRS2	-4.3	7.15	4.3	3.6	9.9	-3.8	5.7	4.6	3.2	AFRS2
52.7	AFRS3	-4.8	10.01	4.7	1.0	13.8	0.4	9.3	6.7	4.5	AFRS3
21.6	AFRS4	-1.1	4.11	2.8	0.2	5.7	-2.9	3.1	2.3	1.8	AFRS4
74.2	WANA9	-1.9	-13.46	-11.0	18.9	-13.5	-4.5	-7.1	8.0	30.9	WANA9
78.2	ASIA1	9.8	0.34	8.3	0.1	-13.1	0.9	5.0	-16.1	-5.6	ASIA1
41.5	ASIA2	5.0	0.18	3.3	-2.6	-6.9	1.1	1.8	2.4	-3.0	ASIA2
92.7	ASIA3	3.8	0.40	6.2	0.2	-15.5	25.9	3.3	9.8	-6.6	ASIA3
100.2	ASIA5	1.9	0.43	10.5	-2.5	-16.8	-7.9	-7.3	-18.8	-7.2	ASIA5
38.8	ASIA6	1.2	0.17	-1.8	-3.1	-6.5	-3.0	-5.9	4.2	-2.8	ASIA6
95.0	ASIA7	0.8	0.41	6.0	-8.5	-15.9	13.7	-23.5	-34.1	-6.8	ASIA7
83.6	ASIA8	1.6	0.36	7.6	-3.6	-14.0	12.2	-19.5	1.0	-6.0	ASIA8
16.5	LAC1	0.6	-1.81	-3.6	-0.7	4.2	-2.1	2.1	2.2	-0.7	LAC1
43.9	LAC2	-4.0	-4.83	-11.1	-4.5	11.2	-3.7	6.3	5.7	-1.7	LAC2
53.5	LAC3	-2.8	-5.88	-10.2	-4.3	13.6	-0.8	7.9	7.2	-2.1	LAC3
30.4	LAC4	2.4	-3.35	-4.3	-0.7	7.7	-4.2	1.8	3.4	-1.2	LAC4
8.4	LAC5	-0.8	-0.92	-2.1	0.0	2.1	-1.2	0.8	0.9	-0.3	LAC5
4.4	LAC6	-0.6	-0.49	-1.5	-0.8	1.1	-0.6	0.6	0.4	-0.2	LAC6
29.5	LAC7	-2.4	-3.24	-7.7	-2.7	7.5	-5.4	1.4	1.4	-1.2	LAC7
20.7	LAC8	-2.1	-2.28	-6.8	-3.3	5.3	-3.7	2.6	0.2	-0.8	LAC8
6.2	LAC9	-0.5	-0.68	-1.2	0.2	1.6	-1.0	0.6	0.8	-0.2	LAC9
1000.0	SUM	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	SUM
182.3	SSA	-12.0	34.6	19.3	17.8	47.9	-15.8	29.2	21.5	15.5	SSA
74.2	WANA	-1.9	-13.5	-11.0	18.9	-13.5	-4.5	-7.1	8.0	30.9	WANA
530.1	ASIA	24.1	2.3	40.1	-20.0	-88.7	42.9	-46.2	-51.6	-38.0	ASIA
213.5	LAC	-10.2	-23.5	-48.5	-16.8	54.3	-22.7	24.1	22.2	-8.4	LAC
165.0	AEZ1	8.6	11.9	12.2	12.4	9.6	-10.7	18.1	-6.0	-0.3	AEZ1
123.0	AEZ2	-3.4	2.5	-3.5	-3.4	14.1	-6.4	13.8	12.7	-1.5	AEZ2
198.9	AEZ3	-3.8	4.5	0.6	-3.0	11.9	25.6	20.6	23.7	-4.3	AEZ3
52.0	AEZ4	1.3	0.8	-1.5	-0.5	13.4	-7.1	4.9	5.7	0.6	AEZ4
108.6	AEZ5	1.2	-0.5	8.4	-2.5	-14.6	-9.1	-6.4	-17.9	-7.5	AEZ5
43.3	AEZ6	0.6	-0.3	-3.3	-3.9	-5.4	-3.6	-5.4	4.6	-3.0	AEZ6
124.5	AEZ7	-1.6	-2.8	-1.7	-11.2	-8.4	8.4	-22.2	-32.7	-8.0	AEZ7
104.3	AEZ8	-0.5	-1.9	0.9	-6.9	-8.7	8.5	-17.0	1.1	-6.8	AEZ8
80.4	AEZ9	-2.4	-14.1	-12.1	19.1	-11.9	-5.5	-6.5	8.8	30.7	AEZ9

Table 9.11. Relative impacts of agricultural modifiers by agroecological zone and region (weight 0.5)

AEZ/Region	MODIFIER									Net Effect of all Modifiers
	(1) Yield Gap	(2) Malnutrition	(3) GDP/Caput	(4) Urgency	(5) Deforestation	(6) Soil Degradation	(7) Cap. of NARS	(8) Small Country	(9) Import Gap
AEZ
AEZ 1	+	+	+	+	+	-	+	-		+
AEZ 2	-	+	-	-	+	-	+	+	-	+
AEZ 3	+	+	+	-	+	+	+	+	-	+
AEZ 4	+	+	-	-	+	-	+	+	+	+
AEZ 5	+	-	+	-	-	-	-	-	-	-
AEZ 6	-	-	-	-	-	-	-	+	-	-
AEZ 7	-	-	-	-	-	+	-	-	-	-
AEZ 8	-	-	+	-	-	+	-	+	-	-
AEZ 9	-	-	-	+	-	-	-	+	+	+
Region
SSA	-	+	+	+	+	-	+	+	+	+
WANA	-	-	-	+	-	-	-	+	+	+
ASIA	+	+	+	-	-	+	-	-	-	-
LAC	-	-	-	-	+	-	+	+	-	-

9.8.2. Forestry

The same results for forestry are displayed in Tables 9.12 and 9.13. For the forestry analysis six modifiers were used (five are the same as for agriculture). Again, it is clear from Table 9.12 that no agroecological zone or region is consistently discriminated against or favoured by all modifiers. The net effect of the forestry modifiers is to increase the base in all tropical agroecological zones (AEZs 1-4) and deduce it in all subtropical ones (AEZs 5-9). This again favours sub-Saharan Africa.

Table 9.12. Quantitative impact of forestry modifiers (w = 0.5)

WEIGHT = 0.5
DIRECTION		-1	1	-1	1	1	-1
GROSS REDISTRIBUTION		125	191	333	255	263	247
BASELINE RELATIVE PRIORITY	NAME MODIFIER	GDP/ CAPUT	DEFORESTATION	SOIL DEGR. HAZARD	WOOD LAND/ CAPUT	MAL NUTRITION	AV. SIZE COUNTRY IN RAEZ
70.5	AFRS1	8.4	13.6	-11.1	-0.4	13.6	4.6
37.7	AFRS2	5.5	8.4	-5.2	0.6	8.4	3.0
52.8	AFRS3	8.9	17.2	-0.4	-6.7	17.2	6.5
19.0	AFRS4	2.6	3.5	-2.9	1.8	3.5	1.2
74.4	WANA9	-4.9	-4.5	-2.5	4.0	-6.5	1.2
78.3	ASIA1	7.0	-12.8	0.0	6.3	-0.4	-10.2
41.6	ASIA2	3.7	-8.8	0.6	3.4	-0.3	1.3
92.9	ASIA3	9.7	-27.3	36.5	16.2	-0.9	8.0
100.6	ASIA5	6.2	-11.5	-5.2	6.7	-0.4	-13.0
39.0	ASIA6	-0.8	-3.7	-1.7	2.2	-0.1	0.7
95.3	ASIA7	3.3	-9.6	6.4	4.6	-0.3	-20.5
83.8	ASIA8	8.0	-16.8	11.3	10.4	-0.5	0.9
16.5	LAC1	-4.8	4.2	-3.1	-3.0	-2.7	1.6
44.0	LAC2	-17.2	13.1	-6.7	-8.2	-8.3	4.7
53.6	LAC3	-19.8	20.1	-2.8	-39.8	-12.8	7.6
30.5	LAC4	-4.7	6.5	-5.2	1.7	-4.1	2.1
8.4	LAC5	-1.4	1.1	-0.9	-0.2	-0.7	0.3
4.4	LAC6	-0.4	0.3	-0.2	0.0	-0.2	0.0
29.6	LAC7	-5.2	3.8	-3.9	0.5	-2.4	0.0
20.8	LAC8	-2.7	1.6	-1.6	0.2	-1.0	-0.6
6.2	LAC9	-1.6	1.6	-1.5	-0.4	-1.0	0.6
1000.0	SUM	0.0	0.0	0.0	0.0	0.0	0.0
180.0	SSA	25.5	42.7	-19.6	-4.7	42.6	15.4
74.4	WANA	-4.9	-4.5	-2.5	4.0	-6.5	1.2
531.6	ASIA	37.2	-90.5	47.9	49.9	-2.9	-32.8
214.1	LAC	-57.8	52.3	-25.8	-49.2	-33.3	16.2
165.3	AEZ1	10.7	5.1	-14.2	2.9	10.5	-4.0
123.3	AEZ2	-8.0	12.7	-11.3	-4.2	-0.3	9.1
199.3	AEZ3	-1.2	10.0	33.3	-30.3	3.5	22.2
49.5	AEZ4	-2.1	10.0	-8.1	3.5	-0.6	3.3
108.9	AEZ5	4.9	-10.4	-6.1	6.5	-1.1	-12.7
43.4	AEZ6	-1.2	-3.5	-1.9	2.3	-0.3	0.7
124.9	AEZ7	-1.9	-5.8	2.5	5.1	-2.7	-20.5
104.6	AEZ8	5.3	-15.2	9.6	10.6	-1.6	0.2
80.6	AEZ9	-6.4	-2.9	-3.9	3.6	-7.5	1.8

Table 9.13. Relative impacts of forestry modifiers by agroecological zone and region

AEZ/Region	MODIFIER						Net Effect of all Modifiers
	(1) GDP per caput	(2) Deforestation	(3) Soil Deg. Risk	(4) Woodland/Caput	(5) Malnutrition	(6) Small country
AEZ
AEZ 1	+	+	-	+	+	-	+
AEZ 2	-	+	-	-	+	+	+
AEZ 3	-	+	+	-	+	+	+
AEZ 4	-	+	-	+	+	+	+
AEZ 5	+	-	-	+	-	-	-
AEZ 6	-	-	-	+	-	+	-
AEZ 7	-	-	+	+	-	-	-
AEZ 8	+	-	+	+	-	-	-
AEZ 9	-	-	-	+	-	+	-
Region
SSA	+	+	-	-	+	+	+
WANA	-	-	-	+	-	+	-
ASIA	+	-	+	+	-	-	-
LAC	-	+	-	-	-	+	-

9.8.3. Fisheries

The baseline value for fisheries was modified by only one variable, the malnutrition modifier, and only on a regional basis. The results are presented in Table 9.14. The malnutrition modifier increases the base for sub-Saharan Africa and reduces it in each of the other regions. This modifier had the same impact on each of the regions for agriculture and forestry as well.

Table 9.14. Impact of modifier on fisheries base (weight 0.5)

Region	Base	Modified Base
SSA	11.2	13.9
WANA	4.9	4.2
ASIA	65.4	64.4
LAC	18.5	17.5

9.9. Impact of Modifiers

9.9.1. Agriculture
9.9.2. Forestry
9.9.3. Fisheries

9.9.1. Agriculture

9.9.1.1. Priorities by region, agroecological zone and regional agroecological zone
9.9.1.2. Priorities by commodity and region

9.9.1.1. Priorities by region, agroecological zone and regional agroecological zone

The effect of all the nine modifiers (all weighted at 0.5) on the priority analysis for agriculture is shown in Table 9.15. The most striking cumulative effect of the modifiers is the shift in priority from the subtropical to the tropical agroecological zones. The only subtropical agroecological zone whose priority rating increased significantly was the cool subtropics with winter rainfall (AEZ 9), which predominates in West Asia-North Africa.

One of the main consequences of this shift is to boost the priority for sub-Saharan Africa by more than 80% relative to the baseline. Asia, on the other hand, declines by almost 30% relative to the baseline. The other regional changes are relatively minor - an increase of 9% in West Asia-North Africa and a decrease of 14% in Latin America and the Caribbean.

On a regional basis, the analysis results in a final ranking of Asia at 395, sub-Saharan Africa at 340.24, Latin America and the Caribbean at 184.10 and West Asia-North Africa at 80.66. On an agroecological basis, the warm humid tropics (AEZ 3) receive the highest ranking at 274.64, the semi-arid tropics (AEZ 1) rank second with 220.78, while the subhumid tropics (AEZ 2) score third with 148. According to the analysis, the lowest priority zone appears to be AEZ 6, the warm subhumid subtropics with summer rainfall.

9.9.1.2. Priorities by commodity and region

Setting priorities by region and agroecological zone using the baseline and modifiers chosen by TAC would have considerable consequences for priorities among commodities. To quantify these, a method was developed for adjusting the value of production of each commodity in each regional agroecological zone by the ratio between the final priority ranking with modifiers weighted at 0.5 and the initial ranking based on value of production. The ratio is calculated by dividing the final value by regional agroecological zone as given in Table 9.15 by the value of production by regional agroecological zone as presented in Table 9.2. If modifier weights change, the ratio will have to be adjusted also. The ratio ranges from a high of 5.07 in RAEZ 1 of sub-Saharan Africa (AFRS1) to a low of 0.20 in RAEZ 7 (ASIA7) of Asia.

Next, the value of production of each commodity in each regional agroecological zone is multiplied by the ratio obtained for that zone. This means that a crop with a high production value grown mainly in an area that is accorded low priority may end up with lower priority than a crop with a low production value grown mainly in an area that is accorded high priority. Commodities produced in RAEZ 1 of sub-Saharan Africa would increase almost fivefold in their value of production. Those produced in RAEZ 7 of Asia will reduce by more than four-fifths. These adjusted values of production of a commodity can then be aggregated by region and by agroecological zone. The results are shown in Tables 9.16 and 9.17, which show the unadjusted and adjusted values respectively, globally and by region.

Table 9.15. Outcome of agriculture analysis (w = 0.5)

RAEZ/AEZ/REGION	BASELINE	FINAL
RAEZ
AFRS1	70.35	136.41
AFRS2	37.65	68.01
AFRS3	52.69	98.31
AFRS4	21.61	37.51
WANA9	74.18	80.66
ASIA1	78.17	67.66
ASIA2	41.49	42.82
ASIA3	92.68	120.15
ASIA5	100.24	52.75
ASIA6	38.82	21.20
ASIA7	95.02	27.17
ASIA8	83.63	63.26
LAC1	16.48	16.71
LAC2	43.88	37.17
LACS	53.50	56.18
LAC4	30.44	32.12
LAC5	8.36	6.86
LAC6	4.43	2.47
LAC7	29.48	17.15
LACS	20.71	9.68
LAC9	6.19	5.75
Total	1000.00	1000.00
AEZ
AEZ1	165.00	220.78
AEZ2	123.03	148.00
AEZ3	198.87	275.64
AEZ4	52.05	69.63
AEZ5	108.60	59.62
AEZ6	43.26	23.67
AEZ7	124.50	44.31
AEZ8	104.34	72.94
AEZ9	80.36	86.41
Total	1000.00	1000.00
Region
AFRS	182.30	340.24
WANA	74.18	80.66
ASIA	530.06	395.00
LAC	213.47	184.10
Total	1000.00	1000.00

The last column of Table 9.16, labelled "Global VOP", shows the percentage share of each commodity in the global value of production of 35 major agricultural commodities. The total is standardized at 100. Thus rice represents 17.8% of the global value of production, wheat 6.4%, etc. The first part of the table illustrates the regional distribution of this value of production by commodity. Since regional price differences were not used in calculating the total value of production, this regional distribution reflects production only. For example, 2% of rice is produced in sub-Saharan Africa, 1% in West Asia-North Africa, 93% in Asia and 4% in Latin America and the Caribbean. Barley is produced predominantly (66%) in West Asia-North Africa, cassava (45%) in sub-Saharan Africa, and soybean (65%) in Latin America and the Caribbean.

The unadjusted baseline data are dominated by the value of production of the staple cereal crops (rice, wheat and maize), and by the large differences between regions in the production not only of cereals but of many other commodities. Banana and plantain, beef and buffalo meat, and milk are the other CGIAR commodities with a significant (>4%) share in the value of production. The most significant aspect of the regional distribution is the dominance (>80%) of Asia in the production of rice, sweet potato, chickpea, coconut and cabbage, which are all either CGIAR commodities already or else under consideration. Rubber and pigmeat are also of particular importance in Asia. The bulk share of sweet potato and pigmeat is produced in China.

Table 9.17 presents the outcome of the weighting process. The first column repeats the basic share of each commodity in unadjusted value of production. The second column represents the adjusted share in value of production of each commodity (with modifiers weighted at 0.5). Commodities that are mostly produced in Asia and in the subtropics generally reduce in importance, while commodities produced in the tropics and in sub-Saharan Africa generally rank higher. The right hand side of the table shows the distribution of this adjusted value of production by commodity and by region.

The weighting process generally shifts the ranking of commodities in favour of sub-Saharan Africa and away from Asia, as might be expected from the analysis above (Section 9.9.1.1). Overall, rice shifts from 17.8 to 13.2%, and on a regional basis rice in sub-Saharan Africa increases from 2% to 9%. Similar regional shifts can be seen in the cases of wheat, maize, millet and sorghum, although the overall ranking of maize does not change and those of millet and sorghum increase. In the case of wheat, the priority for West Asia-North Africa increases from 9% to 26%. Other significant regional shifts include large improvements in the ranking in sub-Saharan Africa of cassava, sweet potato, bananas and plantain, phaseolus beans, broad beans, groundnuts, beef and buffalo meat, sheep and goat meat, and milk.

9.9.2. Forestry

The results of the analysis for forestry are summarized in Table 9.18. The impact of the six modifiers on the allocation of priority by regional agroecological zone is quite variable. For example, the warm humid tropics (AEZ 3) increase in importance in sub-Saharan Africa and Asia but decrease in Latin America and the Caribbean. The impact on the allocation to global agroecological zones is little changed. The situation is different for the regions, however. The allocation to sub-Saharan Africa increases significantly (up 102), while the allocation to Asia increases slightly, and that to Latin America and the Caribbean decreases (up 9 and down 102 respectively). The analysis suggests that Asia should receive a ranking of 461.2, sub-Saharan Africa of 339.6, Latin America and the Caribbean of 178.4, and West Asia-North Africa of 20.7. The most important zone appears to be the warm humid tropics (AEZ 3), which receives 375.5, while the warm subhumid subtropics receive only 16.08.

Table 9.16. Value of production by agricultural commodity and its regional distribution not adjusted for RAEZ priorities (% of total)

COMMODITY	VOP	AFRICA	WANA	ASIA	LAT. AM	SUM
Rice	17.8	1.8	1.1	93.0	4.2	100.0
Wheat	6.4	0.8	19.0	70.0	10.2	100.0
Maize	4.1	10.3	4.0	57.5	28.2	100.0
Barley	0.6	4.7	65.9	23.0	6.4	100.0
Sorghum	0.8	32.4	2.7	40.3	24.6	100.0
Millet	0.7	41.3	0.8	57.6	0.3	100.0
Cassava	2.0	45.0	0.0	34.6	20.4	100.0
Potato	2.8	3.1	15.2	65.1	16.5	100.0
Sweet Potato	2.9	5.0	0.1	93.1	1.9	100.0
Yam	0.6	96.6	0.0	0.8	2.6	100.0
Banana & Plantain	2.1	34.5	0.8	29.2	35.6	100.0
Chickpea	0.5	2.7	14.5	80.3	2.5	100.0
Cowpea	0.2	95.5	0.4	1.9	2.2	100.0
Pigeonpea	0.2	6.1	0.0	92.4	1.5	100.0
Broad Bean	0.4	8.9	22.5	64.0	4.5	100.0
Lentil	0.2	1.2	47.9	47.8	3.1	100.0
Beans	1.1	23.9	7.8	20.2	48.1	100.0
Soybean	2.5	0.5	0.9	33.3	65.3	100.0
Groundnut	2.6	21.8	0.9	73.5	3.9	100.0
Coconut	1.1	4.9	0.0	87.9	7.1	100.0
Tomato	1.2	4.7	49.5	23.0	22.8	100.0
Onion	0.8	2.8	23.4	58.9	14.9	100.0
Cabbage	0.4	0.7	9.0	85.1	5.2	100.0
Orange	3.5	1.6	15.3	20.7	62.3	100.0
Lemon & Lime	0.7	2.8	29.3	20.6	47.2	100.0
Pineapple	0.5	11.1	0.0	63.7	25.2	100.0
Grape	2.5	0.2	53.7	9.1	37.1	100.0
Apple	1.1	0.1	29.5	50.7	19.7	100.0
Sugar	2.7	6.9	6.5	40.1	46.5	100.0
Coffee	2.7	20.4	0.1	17.0	62.5	100.0
Tea	0.8	12.3	8.7	76.6	2.4	100.0
Cocoa	0.8	57.7	0.0	14.6	27.6	100.0
Tobacco	2.6	5.8	6.1	73.4	14.8	100.0
Rubber	1.1	6.1	0.0	92.8	1.1	100.0
Cotton	2.8	8.9	11.4	65.1	14.7	100.0
Jute	0.2	0.1	0.2	99.0	0.7	100.0
Hemp	0.0	0.0	3.9	93.1	2.9	100.0
Sisal	0.0	24.5	0.4	4.2	70.9	100.0
Palm Oil	0.7	16.7	0.0	77.7	5.6	100.0
Beef & Buffalo Meat	5.0	13.0	8.6	21.3	57.2	100.0
Sheep & Goat Meat	1.7	17.9	29.8	44.0	8.3	100.0
Pigmeat	4.8	1.2	0.1	87.7	10.9	100.0
Poultry Meat	1.9	6.5	14.0	43.7	35.8	100.0
Milk	8.9	8.5	11.1	52.2	28.3	100.0
Eggs	2.8	4.3	11.6	61.5	22.5	100.0
Sum	100.0	9.1	9.3	59.0	22.5	100.0
Grain crops	30.4
Starchy crops	10.5
Leguminous crops	78
Vegetables & Fruits	10.7
Other Crops	15.5
Livestock	25.0

Table 9.17. Value of production by agricultural commodity adjusted for priorities by RAEZ (% of total)

WEIGHT 0.5 AND BASELINE PRIORITY
COMMODITY	VOP	ADJUSTED	AFRICA	WANA	ASIA	LAT. AM	SUM
Rice	17.8	13.2	9.0	1.2	84.8	5.0	100.0
Wheat	6.4	4.0	4.3	26.4	60.3	8.9	100.0
Maize	4.1	4.2	36.1	3.3	39.0	21.5	100.0
Barley	0.6	0.6	13.4	63.7	17.9	5.1	100.0
Sorghum	0.8	1.5	72.6	1.3	15.4	10.7	100.0
Millet	0.7	1.5	80.8	0.3	18.9	0.1	100.0
Cassava	2.0	4.5	74.7	0.0	16.3	9.0	100.0
Potato	2.8	2.1	12.3	17.9	51.9	17.9	100.0
Sweet Potato	2.9	1.4	34.2	0.1	62.7	2.9	100.0
Yam	0.6	1.9	98.7	0.0	0.3	1.0	100.0
Banana & Plantain	2.1	3.6	62.0	0.4	16.2	21.4	100.0
Chickpea	0.5	0.4	9.2	14.4	73.7	2.8	100.0
Cowpea	0.2	0.9	98.8	0.1	0.5	0.6	100.0
Pigeonpea	0.2	0.2	20.6	0.0	77.9	1.5	100.0
Broad Bean	0.4	0.4	31.2	21.9	42.5	4.5	100.0
Lentil	0.2	0.2	3.8	46.5	46.9	2.7	100.0
Beans	1.1	1.6	54.4	4.9	12.0	28.8	100.0
Soybean	2.5	1.5	3.5	1.2	23.9	71.3	100.0
Groundnut	2.6	3.7	62.5	0.5	35.2	1.8	100.0
Coconut	1.1	1.4	15.1	0.0	79.4	5.4	100.0
Tomato	1.2	1.2	19.2	44.9	15.7	20.2	100.0
Onion	0.8	0.7	13.2	23.3	49.2	14.3	100.0
Cabbage	0.4	0.3	3.3	11.1	77.6	8.1	100.0
Orange	3.5	3.0	7.1	15.9	15.7	61.3	100.0
Lemon & Lime	0.7	0.6	12.9	27.3	19.4	40.4	100.0
Pineapple	0.5	0.7	30.9	0.0	49.7	19.4	100.0
Grape	2.5	1.9	1.0	62.7	7.0	29.4	100.0
Apple	1.1	0.7	0.4	41.1	37.3	21.2	100.0
Sugar	2.7	2.9	27.2	5.2	27.9	39.6	100.0
Coffee	2.7	3.8	44.1	0.1	13.0	42.8	100.0
Tea	0.8	0.9	31.1	7.3	60.4	1.3	100.0
Cocoa	0.8	2.0	81.4	0.0	6.6	12.0	100.0
Tobacco	2.6	1.9	30.2	7.3	45.9	16.6	100.0
Rubber	1.1	1.3	18.2	0.0	80.9	0.9	100.0
Cotton	2.8	2.6	40.6	10.7	34.9	13.9	100.0
Jute	0.2	0.2	0.7	0.2	98.5	0.7	100.0
Hemp	0.0	0.0	0.0	7.4	87.0	5.6	100.0
Sisal	0.0	0.1	62.9	0.2	1.0	35.9	100.0
Palm Oil	0.7	1.1	36.8	0.0	58.5	4.8	100.0
Beef & Buffalo Meat	5.0	5.9	42.5	6.3	14.6	36.6	100.0
Sheep & Goat Meat	1.7	2.3	53.5	19.0	23.0	4.5	100.0
Pigmeat	4.8	3.3	6.6	0.1	78.7	14.6	100.0
Poultry Meat	1.9	2.0	23.7	11.9	32.5	31.8	100.0
Milk	8.9	9.7	33.5	8.9	36.2	21.4	100.0
Eggs	2.8	2.4	18.5	11.5	46.2	23.8	100.0
Sum	100.0	100.0	33.665	8.10681	39.7228	18.5053	100.0
Grain crops	30.4	25.0
Starchy crops	10.5	13.6
Leguminous crops	7.8	8.9
Vegetables & Fruits	10.7	8.9
Other Crops	15.53	18.07
Livestock	25.05	25.5

Table 9.18. Outcome of forestry analysis (w = 0.5)

RAEZ/AEZ/REGION	BASELINE	FINAL
RAEZ
AFRS1	75.77	104.51
AFRS2	46.61	67.27
AFRS3	95.75	138.52
AFRS4	19.59	29.34
WANA9	33.88	20.74
ASIA1	63.77	53.67
ASIA2	43.86	43.84
ASIA3	136.32	178.62
ASIA5	57.50	40.45
ASIA6	18.62	15.25
ASIA7	48.18	31.98
ASIA8	84.12	97.42
LAC1	22.42	14.71
LAC2	69.16	46.54
LACS	105.92	58.40
LAC4	34.38	30.69
LAC5	5.74	3.84
LAC6	1.33	0.84
LAC7	20.14	13.00
LAC8	8.53	4.28
LAC9	8.40	6.10
Total	1000.00	1000.00
AEZ
AEZ1	161.97	172.89
AEZ2	159.62	157.65
AEZ3	337.99	375.54
AEZ4	53.98	60.03
AEZ5	63.23	44.29
AEZ6	19.96	16.08
AEZ7	68.32	44.98
AEZ8	92.65	101.70
AEZ9	42.28	26.84
Total	1000.00	1000.00
Region
AFRS	237.72	339.64
WANA	33.88	20.74
ASIA	452.38	461.22
LAC	276.02	178.40
Total	1000.00	1000.00

9.9.3. Fisheries

The impact of the malnutrition modifier on the priority allocation across regions for fisheries is shown in Table 9.19. The modifier increases the ranking of sub-Saharan Africa but decreases that of the other three regions. The analysis suggests an allocation to sub-Saharan Africa of 138.8, to West Asia-North Africa of 42.43, to Asia of 643.83 and to Latin America and the Caribbean of 174.9.

Table 9.19. Outcome of analysis on fisheries with weight = 0.5

	INITIAL	FINAL
SSA	111.77	138.81
WANA	48.53	42.43
ASIA	654.37	643.83
LAC	185.32	174.92
TOTAL	1000.00	1000.00

9.10. Impacts of Changing Modifier Weights

9.10.1. All modifiers increased and decreased equally: agriculture, forestry and fisheries
9.10.2. Sensitivity to Changing One Weight
9.10.3. Sensitivity of adjusted commodity values, and their regional distribution, to modifier weights: agriculture
9.10.4. TAC's Conclusions Regarding Weights

9.10.1. All modifiers increased and decreased equally: agriculture, forestry and fisheries

So far, TAC has reported the values of the baseline adjusted by all modifiers weighted at 0.5. TAC also conducted analyses using weights of 0.25 and 1.00. Because the model is linear, three observations are sufficient to enable the reader to extrapolate to further weighting levels.

The results for the regional distribution of priority for agriculture, forestry and fisheries are presented in Table 9.20. The modifiers have significant impacts on regional distribution. For example, agriculture in sub-Saharan Africa has a base priority of 18.0. Weighting all modifiers at 0.25 increased that to 25.8. Weighting at 0.5 increased it still further to 33.7; and a weight of 1 increased it to 47.4. Choosing a weight of 1 nearly doubles the African value over its level at a weighting of 0.25. The agricultural modifiers favour both sub-Saharan Africa and West Asia-North Africa, so their share rises as the weights are increased. In contrast the share of Asia is almost cut in half when the modifiers are weighted at 1.

In forestry, the modifiers favour sub-Saharan Africa at the expense of all other regions. In this region the importance of forestry rises from 28.9% at a weighting of 0.25, to 44.1% at 1. All other regions decline, but more slowly than for agriculture. This results from using only six modifiers instead of nine.

The analysis of fisheries used only one modifier. It favours sub-Saharan Africa, but not heavily. The result is that the redistribution across regions when weights are adjusted is much less pronounced.

Clearly, the choice of the level of weight to be attached to modifiers changes the priority ranking considerably.

Table 9.20. Impact of changing all modifier weights equally: regional distribution for agriculture, forestry and fisheries

Region/Production Sector	Baseline (%)	0.25 (%)	0.50 (%)	1.00 (%)
SSA
Agriculture	18.2	25.8	34.0	47.4
Forestry	23.8	28.9	34.0	44.1
Fisheries	11.2	12.5	13.9	16.6
WANA
Agriculture	7.4	7.8	8.1	8.4
Forestry	3.4	2.7	2.1	0.8
Fisheries	4.8	4.5	4.2	3.6
ASIA
Agriculture	53.2	46.4	39.5	29.1
Forestry	45.2	45.7	46.1	47.0
Fisheries	65.4	64.9	64.4	63.3
LAC
Agriculture	21.4	20.0	18.4	15.1
Forestry	27.6	22.7	17.8	8.1
Fisheries	18.5	18.0	17.5	16.4
Total	100	100	100	100