ANNEX I - TERMS OF REFERENCE
ANNEX II - METHODOLOGY APPLIED FOR THE DATABASE
ANNEX III - CONCEPTUAL FRAMEWORK FOR ANALYZING RESEARCH INVESTMENTS IN POVERTY ALLEVIATION IN A MARGINAL LANDS CONTEXT
ANNEX IV - CURRENT CGIAR ACTIVITIES
ANNEX V - COMPOSITION OF THE STUDY TEAM
ANNEX VI - LIST OF ACRONYMS AND ABBREVIATIONS
The terms of reference for the study are to:
(1) Examine concepts and definitions for use by the CGIAR.(2) Examine potentials for research gains from inherently marginal lands (regardless of production potentials relative to other lands) in terms of: (a) the per hectare gains possible on the individual marginal land types (by ecosystem or region); and (b) the total area of individual marginal land types on which gains could be applied; (c) the policy gains (e.g., related to incentives) that might be derived from research. Indicate how such gains would be distributed among people/groups. Indicate how such gains would be distributed among people/groups, with a focus on how the poor would gain, both now and in the future.
(3) Development an assessment of how the marginal lands issue relates to: (a) common property issues and research (since a large proportion of marginal lands is in common property status); (b) links between marginal lands research and development of off-farm employment (looking at the marginal lands issue in a holistic context of how expanded off-farm employment could relieve pressures on the farm population that has to depend on the land for survival and income); and (c) development of new, more sustainable farm technologies. In each case provide a judgement on the role of CGIAR research in dealing with the issue.
(4) Make suggestions on future CGIAR priorities and strategies for research work on marginal lands, including whether the current level of effort is adequate in relation to that devoted to other land types.
As part of the study a database was created to specify marginal and non-marginal lands and their characteristics including: land use and area, soil classes, moisture regime, total and rural population and rural poverty. The scope of the database was to indicate orders of magnitude of different land types in the developing regions, i.e. the extent of favourable and less favourable lands for agricultural purposes, the extent of forest and woodlands and of dry areas and the numbers of rural people and rural poor living on them.
1. Primary Data
The primary data used on land area and population was supplied by FAO/Land and Water Division. The countries land area by soil classes information was derived from the digitized FAO Soil Map of the World at a scale of 1:5 million, overlaid with agroclimatic maps. This data was georeferenced to cells of 5 square minutes, an area of about 30 km2 at the Equator. For every country the output was a file with:
· soil/terrain - 11 classes characterized as follows:1 - sloping terrain: steeply dissected with slopes in excess of 30%;2 - shallow soils: with depth limitations within 50 cm of the surface caused by the presence of coherent and hard rock or hard-pans;
3 - poorly drained soils: waterlogged and/or flooded for a significant part of the year;
4 - coarse textured soils: coarse in texture or having gravel, stones, boulders, rock outcrops in surface layers or at the surface;
5 - heavy cracking clays: with high clay content and deep cracks occurring at some point in time in most years (unless irrigated);
6 - infertile soils: with deficiencies in major, secondary and minor plant nutrients when cultivated;
7 - saline/sodic soils: with high salt content/exchangeable sodium saturation;
8 - acid sulphate soils: soils in which sulphidic materials have accumulated;
9 - organic soils/peat soils: composed to more than 50% of organic material;
10 - no problem soils: no constraints to sustained agricultural production;
11 - "miscellaneous" lands (mainly water bodies).
· moisture regime - 16 reference length of growing period (LGP) classes in 30 day intervals of "rainfed" moisture availability and temperatures that permit plant growth;
The resolution for the files extracted was at 0.1%, i.e., percentage values of the total surface or population of a country below a tenth of percent per cell were not recorded during the extraction. This fact can lead to a limited loss of land areas in the output files, especially for large countries.
A feature of the soil/land types constraints information is that they are mutually exclusive, i.e., these are used in a sieving approach with the sequence of soil characteristics above. In this way the first land type, sloping, would have all the soils with more than 30% slope in the data irrespective of their further soil characteristics, including therefore also sandy and/or infertile soils in the sloping class. The sequence is somewhat arbitrary but geared towards agricultural purposes, i.e., the most limiting factor - or the most vulnerable aspect with regard to erosion potential - steepness, is excluded first. The sequence of the soil classes therefore influences the results for soil/terrain types 1 to 9, whereas the type 10 "no constraint" can be seen as the output of the sieving and their values are not affected by the order of sieving. With this method the surface area information is additive to 100%.
The countries' population data was estimated by a study team of Santa Barbara University in 1989. It was extracted in the same set-up as the terrain/soil data above, and a country's cell with land area information has a corresponding cell with population information in a second file. The sub-national level population data used by the Santa Barbara University was spread to the corresponding georeferenced land assuming an homogeneous population density across the administrative area.
Country values on percentages rural population and rural poor people were taken from data based on UNDP Human Development Report 1994.
2. Calculation of land type and population values
Country summary files were created which contain the information for land areas and population by soil characteristics and LGPs. The approach followed to derive from this data a breakdown for different land types and for population is shown graphically in Figure II.1.
By combining this data set on soil characteristics and LGPs with a further data source on the extent of forest and woodlands in the countries (FAO Forestry Statistics Today for Tomorrow, 1995), the land area could be grouped into three major categories (favoured lands, forest and woodlands, and arid area) to derive marginal agricultural lands, the fourth category, as a residue. First, the favoured lands were defined as soil type 10 (no constraints) with LGPs above 75 days, taken as a lower limit of rainfed cultivation and grazing, and significant forest and woodland growth. Assuming an even distribution of forests and woodlands in the countries, these favoured lands were reduced by the countries' share of forests and woodlands and the balance are the favoured agricultural lands (FAL). Arid areas (LGPs below 30 days) and the countries' residue of forest and woodlands are subtracted from the data for the other soil characteristics to determine the extent of marginal agricultural lands (MAL). These MALs are the land areas above 30 days of LGP on sloping, shallow, coarse, heavy cracking, etc., lands, but exclude the forest and woodlands and the arid areas of the country. They would, however, include grazing lands. The results of this assessment at regional level are given in Table 2.3.
To show the importance of irrigation in arid regions of the world a further column was inserted in Table 2.3. The values show the extent of irrigated areas in arid lands. This data is an estimate for country level irrigated lands (data from TAC 1992, Priorities and Strategies database) which was overlaid with LGP information. The physical area of the irrigated lands would be contained in the values for the arid lands, whereas the land type itself would be classified in the category favoured agricultural lands.
Rural population data is shown at the regional aggregate level in Table 2.4. The procedure applied stemmed from the above classification for the land areas but was essentially simpler and, unfortunately, less refined. The population values on FAL were derived as the sum of the population figures for the favoured lands (soil class 10 with no soil constraints and with LGP >75 days), and the remainder was set as the country rural population on "other" land types, including marginal agricultural, forests and woodlands and desert areas. The information for forest cover could not be used to differentiate people living on good lands between agriculture and forestry, in this case the value was used as an estimate for rural population on prime lands only. Furthermore, country values for rural poverty (based on UNDP data) were used as factors to derive an estimate of rural poor living on less favoured lands.
Figure II.1: Organization of Database of Land Types for an Example Country
The focus of this study is on the role of the CGIAR in research related to "marginal lands." However, TAC has made it clear that the System should not be investing in research on marginal lands just because they currently are considered low potential areas for agriculture. Thus, as stated in the proposal for the study, "TAC favours allocating resources such that the balance between high and low potential environments emerges from the concern with poverty alleviation and resource conservation, rather than being introduced a priori."
TAC's views regarding research priorities for marginal lands are based on the premise that a balance of effort is required between the marginal lands and the non marginal or "high potential lands". It is not an issue of research investments in one land type against the other, but rather of assessing research priorities against the criteria of poverty alleviation, protection of the environment (sustainability) and efficiency (productivity enhancement). As stated in the Lucerne Declaration, the CGIAR research agenda should address problems of the poor in both less endowed and high potential areas.
From an agricultural productivity and production perspective, a further implication of the focus on poverty alleviation is that the CGIAR is concerned not only with the per hectare potential of different classes of land (i.e., their maximum potential level of production), but also with the scope for overall improvement in poverty reduction and food security through its R & D. That means that the overall levels of production that can be achieved on the farmers' fields are directly relevant to investment decisions in research. We can think of those overall levels as being a product of the average attainable sustainable yield increase per hectare (Y) and the area on which that yield increase can be achieved (A). Thus, (A × Y) becomes the relevant focus.
Thus, it may be that one million ha. of land (A) with moderate productivity, with a feasible improvement of say 0.5 tonne per hectare (Y), is a better prospect for future R & D investment than a smaller area of say 50,000 ha. of already improved high productivity irrigated land, where the product of A × Y is lower. In other words, in this case, additional CGIAR research related to marginal lands can make a greater difference than spending the resources in a favored area.
We can put these thoughts in more expanded form as follows:
Targeting research investment to a given area of marginal land, A.
In a sustainable poverty alleviation context, but with a focus on production of crops (food, livestock, forest, or fish), the contribution of CGIAR research "i" to poverty alleviation can be formulated conceptually as follows:
{(Ai) × (Yi)} × (Ipop) + OFi + Si = Gi, (1)
where,
Ai = size of area that benefits from CGIAR research i.Yi = average sustainable net income (or net benefit or use value) increase per unit area due to CGIAR research i (where "sustainable" incorporates the environmental protection constraint; and "net" income means benefits actually captured by the farmer net of the associated costs of achieving the benefits; the benefits can come from policy gains as well as productivity increases; values appropriately adjusted to present value (PV) terms through use of appropriate discount rate).
Ipop = index for number of poor people gaining or benefiting from (A × Yi), weighted for: (a) degree of poverty affected (e.g., proportion "poorest of the poor"); and (b) extent to which those other than the producers of (A × Yi) gain from lower prices due to (A × Yi);
OFi = measure of net gain from off-farm activity in A due to research i, weighted for the extent to which poor people benefit from the gain (again, in PV terms);
Si = spillover impacts, or externality impacts (in PV terms);
Gi = measure of gain from research i, (which, given the left side of the equation, is a measure of production increase, or net income increase, due to the research, weighted for a poverty alleviation objective);
As mentioned, this formulation considers poverty alleviation impacts with a focus on agricultural, forestry, or fisheries production. If we limit consideration to marginal agricultural lands (MAL), then A of course would reflect the particular MAL area being considered. However, as discussed below, the formulation can be used to look (at least conceptually) at all types of land (both FAL and MAL).
In this format, we are asking the following question: given the potential area for crop x or y, what kind of per hectare income increase could be generated by research related to this crop? (Obviously, in order to identify a relevant A, we have to have some particular crop(s) in mind.)
Targeting research investment to a given marginalized population
We also can change the formulation to make the primary objective be poverty alleviation. This then would be the primary determinant of Gi for a given CGIAR research investment, i, in terms of a given target population of poor or marginalized people (some farmers, some perhaps not). If one takes a poor people focus and looks at research investment from that perspective, then the following formulation might be more appropriate:
(Popi × Ypop) + Si = Gi, (2)
where,
Popi = population of poor people targeted by research i; (the population could be associated with any number of characteristics that relate to poverty)Ypop = average per capita net benefit flow gain for Popi due to CGIAR research i (such research could be focused on crops and other things that could generate benefits)
Si and Gi = as before, except G now is expressed in terms of poverty alleviation measure;
Given the above formulations, which apply equally to "marginal" as to "non-marginal" lands, there is no necessary reason why, for a given (Ai × Yi) or (Popi × Ypop), the CGIAR should be interested more in marginal vs. non-marginal lands, except if the marginal lands (defined by A) have proportionally larger populations of poor people who can gain from changes due to the research1, i.e., the Ipop that applies is higher; or Popi for the marginal lands is higher, other things being equal. These Ipop and Popi conditions define "marginal areas" (MA) used in the study regardless of whether the lands on which this population lives is biophysically marginal or favoured.
1 Also, aside from this question of research efficiency, there is a question of distribution of benefits. This is taken into account by the equation above through Ipop and the discount rate used.
From a strategic public investment point of view, maximizing returns (G) per unit of scarce CGIAR resource ($R) may be regarded as a rational criterion for allocation; (where $R is the amount invested to get the response G). Thus, we have a measure of research investment efficiency as follows:
Research investment efficiency. This could be measured by Gi per $ of CGIAR and associated research expenditure, when both are appropriately discounted to the same point in time:(Gi/$Ri) = research efficiency; (3)
We would want to find that set of research opportunities that maximizes G for the research budget (i.e., we would seek to maximize the net present worth of the research investment).
We can further modify this formulation to look at the social cost-effectiveness of research investments - which should be the ultimate objective sought, once we have eliminated all those potential research investments that have Gi < $R.
Social cost-effectiveness or "impact". (Gi/$Ri) only considers the efficiency element, i.e., production of the research results. The real aim is to get research in place in the farmer's fields or in the forest or on the grazing lands. Thus, we need to introduce $Ei, or the extension and transfer costs, to come out with an array of opportunities ordered on the basis of:
Gi/($Ri + $Ei) (4)
Adding $E to the equation assumes that the CGIAR is interested in research applied on the ground as an ultimate test of success - the $E may not come from the CGIAR, but has to be considered, since it is a necessary cost of getting research in use. This also raises a question on the need for research into institutional determinants of $E.
Finally, we also have the strategic question of equity, or distribution of benefits, as a criterion for allocation. If the calculations of Gi in relation to $R and $E do not produce results that are acceptable, then the decision-makers need to go back and discuss and possibly readjust I or the discount rate.
The Panel puts forth the above formulation only as an annex, since it represents only the beginning thinking for a broader discussion of priority setting. However, the Panel felt it worth including in its report, since it does provide a conceptual perspective on the differences and similarities that exist when one focuses on a land/productivity measure of research return versus a poverty alleviation measure.
As part of the Study a desk analysis of current allocation of CGIAR research resources to the different land types identified in Annex II was undertaken. In addition to estimating total research investments in the different agricultural quadrants (i.e., I to IV), the analysis was also expected to identify allocation patterns among the 12 CGIAR activity categories across land types, if any.
Available information on research expenditures by projects funded by the CGIAR System is very rarely presented in terms of targeted "land domains". Actually, a brief characterisation of the natural resource base on which projects focus their activities is not a descriptor in the standard format used for the 1997 compilation of CGIAR Research-Project Details (1997 R-PD). As a consequence, the exercise was run in two phases. The first sought to elicit from the CGIAR Centres information on project agroecological targets, in terms of relevant moisture zones, as characterized by length of growing period (LGP), and soil classes (SCs), and on activity patterns across land targets. The second phase attempted to re-calculate project resource allocations in relation to the identified land types, as well as to assess their stated poverty alleviation focus in terms of objectives, outputs and beneficiaries.
1. Methodology
Project information was taken from the CGIAR 1997 R-PD, Centres' 1997 Programme Plans and Funding Requirements (PP&FRs) and Medium Term Plans (MTPs). ICLARM, IPGRI and ISNAR and most of IFPRI's projects were not included in the analysis. Data was used to estimate project resource allocation to moisture zones - as represented by LGPs - and research activities within six geographical regions. Among-region estimates followed Regional Expenditures in Table 3 of the PP&FRs, while LGP estimates were based on the FAO's agro-ecological zones information for the different regions. Activity shares were taken from "standards" presented in Table la of the PP&FRs.
Estimates of resources allocated to CGIAR activities within regional LGPs were then submitted to the corresponding Centres for their verification. They covered 279 projects out of a total of 374 projects endorsed for 1997, accounting for 80% of the total CGIAR budget. Resources allocated to LGPs within regions were reviewed by most Centres, but only one provided information on soil domains. None indicated changes in the standard share of activities when the project target moved across different land types.
In the second phase research expenditures were allocated to land types (quadrants). The basic assumption was that Centres allocate their resources in proportion to the importance of the area and land use covered by SCs within regions and LGPs in which they operate. Project budgets were then subjected to the following allocation process:
· first, among regions and LGPs, in correspondence with resources allocated by Centres to LGPs within regions;· second, among land types within LGPs (Table 1), in proportion to soil classes - which were grouped as soils with no physical constraints (No. 10), with high production potential (Nos. 3-6), and those with low potential (Nos. 1-2 & 7-9) assuming no major land development to improve land quality;
· third, between land types I and II, according to LGP-specific proportions of lands under QI and QII in relevant countries of the region.
|
Table 1: Rules to Allocate Research Resources to Land Types | ||||
|
|
Soil Classes1 | |||
|
Moisture |
Any of |
No Constraint |
Hi Potential |
Lo Potential |
|
Irrigation |
Q I/Q II |
|
|
|
|
LGP 75-120 days |
|
Q III |
Q III |
Q IV |
|
LGP > 120 days |
|
Q I/Q II |
Q III |
Q IV |
1 See Annex II for description of soil classes
An unbiased application of this method requires that the actual soil classes in the project land areas are known. Unfortunately, as no information could be provided by Centres on the project soil domains, we were compelled to apply the proportions among SCs within LGPs from the FAO/Land and Water Division database. As these are the same as those used to calculate land shares among quadrants in Annex II, proportions of land types (quadrants) and estimated resource allocations are bound to be correlated.
2. Results
Table 2 shows how CGIAR resources are being allocated across land types within the six geographical regions. Tentative shares are based on estimates of resources allocated by each of 279 projects to the agroecological focus (i) of their activities.
|
Table 2: SHARE OF CGIAR-WIDE PROJECT RESOURCES AMONG LAND TYPES WITHIN REGIONS (as percentages of total within region) |
|||||
|
Regions |
Land Types (Quadrants) |
Globally |
|||
|
I |
II |
III |
IV |
||
|
E&S-AFRICA |
26.2% |
|
57.4% |
16.3% |
18.1% |
|
W&C-AFRICA |
29.9% |
|
57.2% |
12.9% |
23.1% |
|
S&SE-ASIA |
27.5% |
14.6% |
34.1% |
23.7% |
32.3% |
|
WANA |
21.2% |
25.3% |
27.0% |
26.4% |
11.0% |
|
LAC (MesoAmerica) |
32.5% |
14.1% |
27.7% |
25.7% |
4.7% |
|
LAC (SouthAmerica) |
12.6% |
15.8% |
59.1% |
12.4% |
10.8% |
|
GLOBALLY |
26.0% |
10.0% |
45.0% |
19.0% |
100.0% |
Notwithstanding the methodological reservations, shares indicate that about one third of CGIAR resources are invested on the favourable agricultural lands of Qs I and II (FALs), and the remaining two thirds to the so-called marginal agricultural lands (MALs) of Qs III (45%) and IV (19%). This means that 70% of the resources allocated to MALs are directed towards those MALs that have a high productivity potential if the biophysical and socioeconomic constraints are removed. Considering, however, that Q III MALs in which Centres operate include productive areas such as the cracking "black cotton" soils of India and eastern Africa, the poorly drained "inland valleys" of West Africa and the infertile "Cerrados" of Brazil, it could also be concluded that three quarters of CGIAR resources are being applied to increase the sustainable productivity of lands with high agroecological potential (Qs I-II, and tracts of Q III).
Table 3 presents regional estimates on how resources are shared among CGIAR research activities within favourable and marginal lands. Given the opportunities for CGIAR's research on the marginalized poor identified in Chapter 4, the 12 categories are clustered into four ad-hoc groups of activities addressing such opportunities.
Activities on Biodiversity Conservation and Enhancement (grouping Act. 1 & 7) represent resources allocated to improve biological alternatives, while those under Sustainable Production Systems (grouping Act. 2-6) address requirements for "intensification through diversification". Policy activities (Act. 8) deal with institutional constraints, Collaboration with NARS (Act. 9-12) contribute to the requirement for new partnership mechanisms advocated in recommendations 3-4.
As no information was provided by the Centres to indicate whether proportions among activities changed with regions and LGPs, estimates in Table 3 are based on the standard values across land types. This would explain why global activity shares remain essentially the same between land types. Should proportions be different at operational levels, shares of projects covering more than one agroecology would not represent actual resource allocation.
The same picture appears to emerge from the regional data, the exception being that of WANA. Estimates for this region show a substantial increase in Biodiversity and Collaborative activities for research on MALs, while activities on Sustainable Systems and Policy decrease. Despite this, reflections on the preliminary analysis would suggest that current activity categories may not be sensitive enough to indicate actual resources allocated to alleviating constraints affecting poverty processes. Thus, activities in biodiversity enhancement to improve nutrient utilization by plants could be aimed at either nutrient-rich (FALs) or nutrient-poor lands (MALs). In the same vein, Sustainable Systems research could be aimed at increasing the long-term economic efficiency of monocropping enterprises, or at identifying opportunities for diversified systems.
An assessment of CGIAR resources focused on the marginalized rural poor would require, therefore, information on the extent to which projects are explicitly targeted at poverty alleviation in MALs. An examination of the 374 projects endorsed for 1997 shows that only 92 projects appear to be targeted directly at poverty alleviation on MALs (25%), and 37 of them are partially targeted (10%). Of the remaining 245 projects, 27 are targeted at poverty alleviation in FALs (7%), and 218 projects are not explicitly targeted to poverty alleviation (58%).
|
Table 3: REGIONAL CGIAR RESEARCH ACTIVITIES ACROSS LAND TYPES (as percentage of totals within region) | |||||
|
|
CGIAR Research Activities | ||||
|
Land Types |
Biodiversity Conservation/ Enhancement |
Sustainable Production Systems |
Policy |
Collaboration with NARS |
GLOBAL |
|
|
AFRICA |
| |||
|
Qs I&II - FALs |
29 |
45 |
9 |
17 |
34 |
|
Qs III&IV - MALs |
30 |
46 |
7 |
17 |
46 |
|
|
ASIA |
| |||
|
Qs I&II - FALs |
34 |
46 |
8 |
12 |
38 |
|
Qs III&IV - MALs |
35 |
42 |
9 |
14 |
29 |
|
|
WANA |
| |||
|
Qs I&II - FALs |
23 |
53 |
11 |
13 |
14 |
|
Qs III&IV - MALs |
30 |
42 |
4 |
25 |
9 |
|
|
LAC |
| |||
|
Qs I&II - FALs |
28 |
41 |
17 |
14 |
14 |
|
Qs III&IV - MALs |
29 |
47 |
8 |
16 |
16 |
|
|
GLOBAL |
| |||
|
Qs I&II - FALs |
30 |
46 |
10 |
14 |
100 |
|
Qs III&IV - MALs |
31 |
44 |
8 |
17 |
100 |
Chair:
Dr. Michael Nelson (New Zealand)
Casilla 209-12
Santiago
Chile.
Members:
Dr. Hans Gregersen (USA)
College of Forestry
University of Minnesota
Room 110, Green Hall
1530 N. Cleveland Avenue
St. Paul
MN 55108
USA.
Dr. Narpat Jodha (India)
4701 Willard Avenue #1504
Chevy Chase
MD 20815
USA.
Dr. Rudy Dudal (Belgium)
Professor, Faculty of Agricultural Sciences
Institute for Land and Water Management
Katholieke Universiteit Leuven
Vital Decosterstraat 102
3000 Leuven
Belgium.
Dr. Daniel Nyamai (Kenya)
Principal Research Scientist
Kenya Forestry Research Institute
P.O. Box 20412
Nairobi
Kenya.
Consultants:
Dr. Filemon Torres (Argentina)
Via della Farnesina 218
00194 Rome
Italy.
Mr. Jan Peter Groenewold (Germany)
Via Barraco 2
00182 Rome
Italy.
Resource Person:
Dr. Amir Kassam (Panel Secretary)
Senior Agricultural Research Officer
TAC Secretariat
FAO
Via delle Terme di Caracalla
00100 Rome
Italy.
|
ASB |
Alternatives to Slash-and-Burn |
|
CGIAR |
Consultative Group on International Agricultural Research |
|
CIAT |
Centro Internacional de Agricultura Tropical |
|
CIFOR |
Centre for International Forestry Research |
|
CPR |
Common Property Resources |
|
E & S Africa |
Eastern and Southern Africa |
|
FAL |
Favoured Agricultural Lands |
|
FAO |
Food and Agriculture Organization of the United Nations |
|
GIS |
Geographical Information System |
|
HPEPR |
High Potential for Expansion Based on Research |
|
HPUV |
High Present Agricultural Use Values |
|
ICARDA |
International Centre for Agricultural Research in the Dry Areas |
|
ICLARM |
International Centre for Living Aquatic Resources Management |
|
ICRAF |
International Centre for Research in Agroforestry |
|
ICRISAT |
International Crop Research Institute for the Semi-Arid Tropics |
|
IFPRI |
International Food Policy Research Institute |
|
IITA |
International Institute of Tropical Agriculture |
|
ILRI |
International Livestock Research Institute |
|
INRM |
Integrated Natural Resource Management |
|
IPGRI |
International Plant Genetic Resources Institute |
|
IPM |
Integrated Pest Management |
|
ISNAR |
International Service for National Agricultural Research |
|
LAC |
Latin America and Caribbean |
|
LGP |
Length of Growing Period |
|
LPEPR |
Low Potential for Expansion Based on Research |
|
LPUV |
Low Present Agricultural Use Values |
|
LZI |
Low or Zero Intensity of Agricultural Use |
|
MA |
Marginal Area |
|
MAL |
Marginal Agricultural Lands |
|
ML |
Marginal Land |
|
MTP |
Medium Term Plan |
|
NARS |
National Agricultural Research System |
|
NGO |
Non-Governmental Organization |
|
NRM |
Natural Resource Management |
|
PP & FR |
Programme Plans and Funding Requirement |
|
PPR |
Private Property Resources |
|
R&D |
Research and Development |
|
R - PD |
Research-Project Details |
|
SC |
Soil Classes |
|
SWNM |
Systemwide Nutrient Management Initiative |
|
S & SE Asia |
South and South-east Asia |
|
TAC |
Technical Advisory Committee to the CGIAR |
|
UNDP |
United Nations Development Programme |
|
WANA |
West Asia and North Africa |
|
W & C Africa |
Western and Central Africa |
