The objective of this paper is to assess the possible influence of the 1994 In Trust Agreements (ITAs) on acquisition and distribution of germplasm held by the International Research Rice Institute (IRRI) genebank. The agreements, legally affirmed the ‘public good’ status of the collections that were placed ‘In Trust’ for the benefit of the world community under agreements with FAO. They initiated a formal system of multilateral access to CGIAR-held

The objective of this paper is to test the hypothesis that the consequences of the 1994 In-Trust Agreements lead to an enhancement of CGIAR germplasm utilization. In doing so formal investigations on factors affecting germplasm acquisition and distribution are conducted and a theoretical framework is developed through which it is possible to derive the demand of genetic resources (i.e. germplasm distribution) and explain how it is affected by its factors.

CGIAR germplasm collections were established with the intention of compiling genetic material of major staple crops in order to make it freely available for breeding. Subsequently germplasm availability for research and plant improvement to address problems of food security and productivity were also ensured. In this context the significance of the CGIAR collections is potentially enormous. The centres hold ∼ 600,000 accessions, out of the 6 million accessions stored in over 1300 genebanks around the world (

With the entry into force of the Convention on Biological Diversity (CBD) (Nairobi final Act) countries could begin to exercise their national sovereignty by increasing restrictions on access to Plant Genetic Resources (PGR). Therefore, CGIAR centres would have been compelled to comply with international law, if an ad hoc legal framework for the CGIAR material would not have been established. This means that without the ITAs countries that had contributed germplasm to CGIAR collections could demand its return or stipulate that CGIAR Centres holding the plant genetic resource material must limit its further distribution and use. Also countries hosting CGIAR Centre genebanks could consider germplasm held in those genebanks to fall under their sovereign rights because the material was technically located within the borders of that country (

The ‘public good’ status of the collections was legally affirmed in 1994, when the germplasm collections were placed ‘In Trust’ for the benefit of the world community under agreements with FAO. These agreements initiated a formal system of multilateral access to CGIAR-held

In this study, an empirical application will be provided, in order to give statistical evidence of the

The Chib’s changepoints framework (1998) with latent state variable is selected to investigate the hypothesis because it is explicitly formulated to assess count data changepoints, and so particularly qualified in estimation process without the inclusion of other covariates. The estimations are conducted including data on genebanks utilizations and acquisitions provided by IRRI genebank. This study attempts to add some further empirical evidence to the studies conducted by

The

The model we propose describes the salient features of the

Let _{1}, x_{2}, … x_{N})′ denote the vector of quantities (i.e. resistance score) obtained from N successive accessions or searches for a trait. We assume that these quantities are random variables emanating from a given probability distribution, that the trials are independent of one another, and that the draws can therefore, be modelled as ^{*}
_{N} ≡ max{x_{1}, x_{2}, …, x_{N}}, we can consider the selection of the optimal level, N^{*}, as the solution to the problem

where U[·] denotes utility derived from the search process. We assume that benefits obtained from locating the maximum value among the N accessions is completely described by the benefit function Benefit(y_{N}) and also that the costs incurred in locating y_{N} are completely described by the cost function Cost(y_{N}). Let us suppose that for each successful realization of the N trials – each y_{N} – the investigator receives a benefit of the amount α > 0, so that the benefit function assumes the form Benefit(y_{N}) ≡ α y_{N}. How the quantity α is calculated, revealed or determined we are unable to answer at this point. In addition, if we assume that each search-and-screening exercise incurs a constant per unit cost κ > 0, then the cost of N such trials – the cost of realizing y_{N} – is Cost(y_{N}) ≡ κ N. It follows that the utility derived from transacting N accessions is U[·] ≡ α y_{N}–κ N. In order to make further progress we note that if we also knew the form of the distribution _{1}, x_{2}, … x_{N} are drawn, we could assess the actual form of the objective function in equation (1). Consequently, we could proceed to model the demand for accessions, namely the optimal value N^{*} that is chosen by the investigator. Indeed such an assumption should be supported by an appropriate empirical exploration, and the empirics to follow provide some indications. In this context, and the desire at this point purely to simplify we adopt Stigler’s (1961) assumption that the distribution

Using this assumption the first-order required condition, which is also sufficient for a maximum, is

It is trivial to establish that the solution to this equation, N^{*}, increases with increasing α and declines when increasing κ, as one would expect in a realistic search situation with increasing value of the benefits derived and costs incurred. Using this simple idea we can motivate changes in the demand for accessions – that is, changes in the value N^{*} – as a result of changes arising in the perceived costs and benefits of sourcing accessions. In this way, we are able to motivate changes in the counts data associated with the trend of accessions.

Prior to considering the results we present a broad overview of some of the data made available to us by the IRRI genebank. The International Rice GeneBank Collection (IRGC) at IRRI comprises the largest collection of rice germplasm held In-Trust for the world community (

Overall,

Studies by

The case of India provides evidence on how countries that rejected the multilateral system of germplasm exchange, did not want to lose their sole proprietary rights to their indigenous germplasm resources, denying thus the free exchange of their material.

Germplasm distributed for restoration and for other purposes could well have been affected by, among other factors, political uncertainty since its free distribution is depending upon the legal status attached to the accessions. Special attention to the germplasm distributed for restoration, which is understood to be germplasm distributed to country of origin for restoration purposes is posed here.

The purpose of this section is to develop more formally the notion that the 1994 In-Trust agreements had an impact on the flow of genetic resource materials, specifically the germplasm distributed for restoration. In this respect, an ideal ‘laboratory’ would enable us to determine formally, statistically, the complete trajectory and patterns of genetic material exchange had the agreements not materialized. Unfortunately, such an objective, despite its considerable merits, is simply not possible in the current state of science. What we do have available are the patterns of exchanges both prior to and immediately following the agreements. The question upon which we focus our attention is whether, given this pattern of stock exchange, the 1994 period brought any discernible change in its movement, either in the upwards or in the downwards directions.

This impact, we posit must have been a decidedly favourable one. For example, one crucial impact of the In-Trust Agreements is lowering transaction costs of germplasm’s accessions exchange (

In order to assess whether a structural change occurred in the demand of germplasm a wide range of models exists; change-point models in time series analysis to test for the presence of a structural break are widely used. A change-point framework due to

The count in year t, y_{t}, is modelled via a Poisson relation:

The estimation is executed imposing only one change-point in the time-series for the distribution of samples for restoration. We focus on this sub-category because according to

The posterior means for λ_{1} and λ_{2} (the Poisson parameters of the two identified distributions), are respectively 3275 and 2828. The model performs efficiently, identifying as the change-point the period t = 13, occurring at the point of intersection of the two probabilities of _{t}

Of course, attributing such a switch to a single event would not reflect the reality of the situation, where many issues related to both policy and other factors would affect requests for and actual distribution of germplasm. However, as

As a result of the increased demand for germplasm material for restoration combined with the overall reduction in acquisitions, the actual size of IRRI genebank would have diminished considerably posing the likely scenario of not having germplasm exchange and even if germplasm collections would not have fully dismantled, surely would not have been found anymore (

The In-Trust Agreements, signed in 1994 between FAO and 12 CGIAR Centres, were the result of a lengthy process of protracted negotiations that had the single objective of regulating CGIAR germplasm, its acquisition and its distribution. A considerable challenge existed. This challenge was to find an agreement that could accommodate the needs of a heterogeneous set of key stakeholders. These stakeholders involved as many as twelve heterogeneous research centres, with distinct boards of trustees, distinct directorships and distinct internal infrastructures; and twelve distinct states, each with their own idiosyncratic regulations and legal infrastructures. The feasible solution that emerged was to apply to CGIAR collections the concept of ‘trusteeship.’ The key contribution of the In-Trust Agreements is that there was an internationally recognized accord for the multilateral exchange of PGR, which in turn has prepared the ground for further multilateral agreements on PGR.

The ITAs represent one stage of a continuing, dynamic process in implementing and adapting the CBD regime to the characteristics of the agricultural sector. The reduction of transaction costs should be analyzed further within its dimension of reducing bargaining costs associated with the monitoring and enforcement costs. In fact the adoption in 1998 of a Second Joint Statement of FAO and the CGIAR Centres on the “Agreement Placing CGIAR Germplasm Collections under the Auspices of FAO” includes some provisions regarding monitoring and enforcement of the terms of the Material Transfer Agreement (MTA). Under the Statement, CGIAR centres and FAO agree to share responsibilities to monitor compliance with the provisions of MTA and to take legal action against possible infringers. Finally, the signature of the Agreement under article 15 of the International Treaty is of course the last stage reached toward this progress but it is still too early to draw any conclusion with existing data.

There is discernible ‘change’ evident in the statistical analysis of distribution data from the IRRI rice collection that would support a significant drop in germplasm distribution followed by a new rising trend around the establishment of the ITAs. This had followed a period beginning around 1989 and leading up to the establishment of the ITAs of a large number of requests for restoration of germplasm back to countries of origin and a reduction in acquisitions. As a result, the number of accessions held by IRRI reached a low point around 1994. Without the establishment of the stable policy environment that was provided by the In Trust Agreement the number of accessions might not have been built up again.

The authors wish to thank Ruaraidh Sackville Hamilton, Jan Engels, Garth Holloway, Chittur S. Srinivasan, Jamie Watts and Adelaida Alcantara for contributing data and comments. However, any views or opinions presented in this study are solely those of the authors. A qualitative analysis of the Agreements can be found in:

IRRI Germplasm acquisitions 1961–2006.

IRRI Germplasm distribution 1983–2006.

Posterior marginal densities of λ_{1} and λ_{2}.

Pr(st=k|Yn), Germplasm demand dataset.

Germplasm acquired by IRRI from top 10 contributing countries (1961–2006).

Country | Total Acquired | Acquired pre 94* | Acquired post 94* |

India | 16,770 | 507 | 2 |

Laos | 15,362 | 61 | 1027 |

Indonesia | 9099 | 507 | 2 |

China | 8105 | 232 | 34 |

Philippines | 6651 | 133 | 173 |

Thailand | 6348 | 180 | 32 |

Bangladesh | 6166 | 179 | 21 |

Cambodia | 4875 | 56 | 233 |

Malaysia | 4269 | 95 | 88 |

*Average per year.

Source: IRRI genebank database.

Chib change-point results.

Parameters | Mean | Standard deviation | Quintiles for each variable | ||||

5% | 25% | 50% | 75% | 95% | |||

λ_{1} |
3275 | 49.1 | 3196 | 3247 | 3275 | 3314 | 3356 |

λ_{2} |
2828 | 44.3 | 2753 | 2797 | 2827 | 2857 | 2900 |

Source: Authors calculations.

For full details of our assumptions and calculations see

Where: y_{t} denotes the count in the year t and the density of y_{t} is function of the parameter ξ_{t}. The
value of ξ_{t} changes at unknown time points, Γ_{m}={τ_{1}, …, τ_{m}}. In a two- change-points model, for example, Γ_{m} = {τ_{1}, τ_{2}}, ξ_{t} is subjected to two breaks, one at time τ_{1} and another at time τ_{2} such that
ξ_{t}=λ_{1} for t≤τ_{1}, ξ_{t}=λ_{2} for τ_{1}<t≤τ_{2} and ξ_{t}=λ_{3} for τ_{2}<t≤n, where τ_{1}>1 and τ_{2}<n. The estimation effort is focused on the vector of the parameters λ, and on the unknown change points Γ_{m} (see

Associated with the probability distribution function _{yN}(y)=℘[y_{N}≤y]=℘[x_{1}≤y; x_{2}≤y; … ; x_{N}≤y]. Now, if the screenings are, in fact, independent draws we have F_{yN}(y) = ∏_{i} ℘[x_{i}≤y] = ∏_{i} F_{xi}(y). If the draws are made from the same distribution – a reasonable assumption – we then have F_{yN}(y) = F_{x}(y)^{N}. Consequently, the pdf associated with y_{N}, which is obtained by differentiating, is _{yN}(y) = N[F_{x}(y)]^{N–1}_{x}(y). Finally, using the fact that the originating distribution _{x}(·) is standard uniform, we obtain _{yN}(y) = N y^{N–1}, which is clearly a function of N, as well as y. An explicit solution to the optimization problem in equation (1), which now reduces to

where, we note, y_{min} and y_{max} are, respectively, the minima and maxima available across the support of the standard uniform distribution, that is, y_{min}= 0 and y_{max}= 1, respectively. Using this fact, the first-order necessary condition, which is also sufficient for a maximum, is equation (2).

Within each period Chib introduces a latent class predictor, ‘s_{t},’ referred to as the ‘state’ of system at time t, corresponding to the m+1 phases in which the samples can be split. This latent state variable is formulated in a way to evolve according to a discrete-time, discrete-state Markov process with the transition probability matrix, ℘, forced so that s_{t} either remains at the current value or jumps to the next highest value. The count in year t, y_{t}, is modelled via a hierarchical Poisson relation (3). Specifically, his variable ‘s_{t}’ is an integer defined on the set of integers {1, 2, …, m+1} corresponding to the m+1 possible phases into which the time series can be subdivided. Thus, a realization s_{t} = k signifies that observation ‘t’ emanates from state ‘k’ of the system. In other words, that observation y_{t} evolves from the distribution _{t}|_{t–1}, θ_{k}), where _{t-1} ≡ (y_{1}, y_{2}, …, y_{t–1})′ denotes observations up to time t–1 and θ_{k} denotes the parameters determining state _{t} can either remain at the current value or jump to the next highest value. Thus, the one-step-ahead transition probability matrix is represented as

where p_{ij} = Prob(s_{t} = j|s_{t–1} = i) denotes the probability of moving to regime j at time t given that the regime at time t–1 resides in regime i. Now, defining _{1}, s_{2}, …, s_{N})′ as the unknown or latent class of states; and defining _{1}, _{2}, … , _{m+1}}, attentions focus on the posterior defined over the quantities