Smallholder Agroforestry Systems For Carbon Storage

Most smallholder agroforestry systems in Southeast Asia are tree- and species-rich systems producing non-wood and wood products for both home use and market sale. Due to their high biomass, these systems contain large carbon (C) stocks. While the systems of individual farmers are of limited size, on a per area basis smallholder systems accumulate significant amounts of C, equaling the amount of C stored in some secondary forests of similar age. Their ability to simultaneously address smallholders’ livelihood needs and store large amounts of C makes smallholder systems viable project types under the Clean Development Mechanism (CDM) of the Kyoto Protocol, with its dual objective of emissions reduction and sustainable development. Smallholder systems have not developed in areas where enabling conditions do not exist. A CDM project that facilitates a minimum threshold of enabling conditions that make smallholder agroforestation possible should qualify for C credits. To secure smallholder confidence, the agroforestry systems promoted through a CDM project must be socially and economically viable independent of C payments. To assure system productivity and profitability, projects should provide farmers with technical and marketing assistance. Additionally, project sites should meet the following preconditions: areas of underutilized low-biomass landuse systems available for rehabilitation; smallholders interested in tree farming; accessible markets for tree products; supportive local governments; sufficient infrastructure; and transparent and equitable relationships between project partners. Questions of leakage and additionality should not be problematic and can be addressed through the project design, establishment of quantifiable baseline data and facilitating enabling conditions. However, smallholder-focused CDM projects would have high transaction costs. The subsequent challenge is thus to develop mechanisms that reduce the costs of: (a) making information (e.g., technology, markets) more accessible to multiple clients; (b) facilitating and enforcing smallholder agreements and (c) designing feasible monitoring systems.     

Comparison of physiological responses to oxidative and .heavymetal stress in seedlings of rice paddy, Oryza sativa L.

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Comparison between Hansen-Hurwitz and Horvitz-Thompson estimators for adaptive cluster sampling

Thompson (1990) introduced the adaptive cluster sampling design. This sampling design has been shown to be a useful sampling method for parameter estimation of a clustered and scattered population (Roesch, 1993; Smith et al., 1995; Thompson and Seber, 1996). Two estimators, the modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators, are available to estimate the mean or total of a population. Empirical results from previous researches indicate that the modified HT estimator has smaller variance than the modified HH estimator. We analytically compare the properties of these two estimators. Some results are obtained in favor of the modified HT estimator so that practitioners are strongly recommended to use the HT estimator despite easiness of computations for the HH estimator.     

Habitat association studies in conjunction with adaptive cluster samples

Habitat association studies investigate the relationships between habitat characteristics and animal usage of study regions. These studies are often conducted in conjunction with surveys designed primarily to estimate population totals. This paper shows that habitat association studies may proceed from surveys using adaptive cluster sampling. The manner in which units appear in the sample turns out not be relevant to the habitat association study, which proceeds as though the units came from a simple random sample. However, it is also shown that the information about the habitat association parameters is greater than one would expect from a simple random sample of the same general size.     

Science and Society: Marine Reserve Design for the California Channel Islands

Abstract:  We explored the interaction of science and society in attempts to restore impaired marine ecosystems in Channel Islands National Park and National Marine Sanctuary, California. Deteriorating resource conditions triggered a community's desire to change public policy. Channel Islands National Park, one of 40 marine protected areas in the U.S. National Park System, was proclaimed a national monument in 1938 and expanded substantially in 1980 by an act of Congress. Collapse of marine life populations and loss of 80% of the giant kelp (Macrocystis pyrifera) forests in the park between 1980 and 1998 showed that habitat and water quality protection alone had not secured sustainable ocean ecosystems or fisheries. The failed fishery management strategies and practices prompted formal community and agency requests in 1998 for a network of reserves protected from direct fishing impacts to serve as marine recovery areas. A 2-year attempt to build a community consensus based on science for a reserve network successfully identified recovery goals for fisheries, biodiversity, education, economics, and heritage values. Nevertheless, the community group failed to garner unanimous support for a specific reserve network to achieve those common goals. The group submitted a recommendation, supported by 14 of 16 members, to state and federal authorities in 2001 for action in their respective jurisdictions. California adopted the half of the network in state waters in 2003. This process exposed the socioeconomic factors involved in the design of marine protected areas that can be negotiated successfully among groups of people and factors determined by nature that cannot be negotiated. Understanding the differences among the factors was crucial in reaching consensus and changing public policy.     

Estimator bias and efficiency for adaptive cluster sampling with order statistics and a stopping rule

Practical problems facing adaptive cluster sampling with order statistics (acsord) are explored using Monte Carlo simulation for three simulated fish populations and two known waterfowl populations. First, properties of an unbiased Hansen-Hurwitz (HH) estimator and a biased alternative Horvitz-Thompson (HT) estimator are evaluated. An increase in the level of population aggregation or the initial sample size increases the efficiencies of the two acsord estimators. For less aggregated fish populations, the efficiencies decrease as the order statistic parameter r (the number of units about which adaptive sampling is carried out) increases; for the highly aggregated fish and waterfowl populations, they increase with r. Acsord is almost always more efficient than simple random sampling for the highly aggregated populations. Positive bias is observed for the HT estimator, with the maximum bias usually occurring at small values of r. Secondly, a stopping rule at the Sth iteration of adaptive sampling beyond the initial sampling unit was applied to the acsord design to limit the otherwise open-ended sampling effort. The stopping rule induces relatively high positive bias to the HH estimator if the level of the population aggregation is high, the stopping level S is small, and r is large. The bias of HT is not very sensitive to the stopping rule and its bias is often reduced by the stopping rule at smaller values of r. For more aggregated populations, the stopping rule often reduces the efficiencies of the estimators compared to the non-stopping-rule scheme, but acsord still remains more efficient than simple random sampling. Despite its bias and lack of theoretical grounding, the HT estimator is usually more efficient than the HH estimator. In the stopping rule case, the HT estimator is preferable, because its bias is less sensitive to the stopping level.     

Performance of adaptive cluster sampling estimators in a multivariate setting

Adaptive cluster sampling has been proven to perform well in a univariate setting, but it may not perform well when there are several parameters of interest. The efficiency of adaptive sampling when there are several variables of interest depends on the relationship of the variables with one another.     

Markov Chain Monte Carlo on optimal adaptive sampling selections

Under a Bayesian population model with a given prior distribution, the optimal sampling strategy with a fixed sample size n is an n-phase adaptive one. That is, the selection of the next sampling units should sequentially depend on the information obtained from the previously selected units, including the observed values of interest. Such an optimal strategy is in general not executable in practice due to its intensive computation. In many survey sampling situations, an important problem is that one would like to select a set of units in addition to a certain number of sampling units which have been observed. If the optimal strategy is an adaptive one, the selection of the additional units should take both the labels and the observed values of the already selected units into account. Hence, a simpler optimal two-phase adaptive sampling strategy under a Bayesian population model is proposed in this article for practical interest. A Markov chain Monte Carlo method is used to approximate the posterior joint distribution of the unobserved population units after the first phase sampling, for the optimal selection of the second phase sample. This approximation method is found to be successful to select the optimal second-phase sample. Finally, this optimal strategy is applied to a set of data from a study of geothermal CO₂ emissions in Yellowstone National Park as a practical illustrative example.