This study scrutinizes spatial econometric models and specifications of crop yield response functions to provide a robust evaluation of empirical alternatives available to researchers. We specify 14 competing panel regression models of crop yield response to weather and site characteristics. Using county corn yields in the US, this study implements in-sample, out-of-sample, and bootstrapped out-of-sample prediction performance comparisons. Descriptive propositions and empirical results demonstrate the importance of spatial correlation and empirically support the fixed effects model with spatially dependent error structures. This study also emphasizes the importance of extensive model specification testing and evaluation of selection criteria for prediction.

Comparing distributions among fields, species, and management practices will help us understand the spatial dynamics of weed seed banks, but analyzing observational data requires nontraditional statistical methods. We used cluster analysis and classification and regression tree analysis (CART) to investigate factors that influence spatial distributions of seed banks. CART is a method for developing predictive models, but it is also used to explain variation in a response variable from a set of possible explanatory variables. With cluster analysis, we identified patterns of variation with direction of the distance over which seed bank density was correlated (range of spatial dependence) with single-species seed banks in corn. Then we predicted patterns of the seed banks with CART using field and species characteristics and seed bank density as explanatory variables. Patterns differed by magnitude of variation in the range of spatial dependence (strength of anisotropy) and direction of the maximum range. Density and type of irrigation explained the most variation in pattern. Long ranges were associated with large seed banks and stronger anisotropy with furrow than center pivot irrigation. Pattern was also explained by seed size and longevity, characteristics for natural dispersal, species, soil texture, and whether the weed was a grass or broadleaf. Significance of these factors depended on density or type of irrigation, and some patterns were predicted for more than one combination of factors. Dispersal was identified as a primary process of spatial dynamics and pattern varied for seed spread by tillage, wind, or natural dispersal. However, demographic characteristics and density were more important in this research than in previous research. Impact of these factors may have been clearer because interactions were modeled. Lack of data will be the greatest obstacle to using comparative studies and CART to understand the spatial dynamics of weed seed banks.

Growers need affordable methods to sample weed populations to reduce herbicide use with site-specific weed management. Sampling programs and methods of developing sampling programs for integrated pest management are not sufficient for site-specific weed management because more and different information is needed to make treatment maps than simply estimate average pest density. Sampling plans for site-specific weed management must provide information to map the weeds in the field but should be developed for the objective of prescribing spatially variable management. Weed scientists will be most successful at designing plans for site-specific weed management if they focus on this objective throughout the process of designing a sampling plan. They must also learn more about the spatial distribution and dynamics of weed populations and use that knowledge to identify cost-effective plans, recommend methods to make maps as well as collect data, and find ways to evaluate maps that reflect management to be prescribed from the map. Foremost, sampling must be thought of as an ongoing process over time that uses many types of information rather than a single event of collecting one type of information. Specifically, scientists will need to identify common characteristics rather than just differences of the spatial distribution of weeds among fields and species, recognize that map accuracy may be a poor indicator of the value of a sampling plan, and develop methods to use growers' knowledge of the distribution of weeds and past spatially variable management within a field for both making a map and recommending a sampling plan. The value of proposed methods for sampling and mapping must also be demonstrated or adoption of site-specific weed management might be limited to growers who enjoy using sophisticated technology.

Weed management could be more efficient and require less herbicide if growers could afford to estimate the composition, density, and distribution of weed seed banks. Spatial distribution of a weed seed bank will affect the accuracy of both mean estimates and interpolated maps of density. Consequently, information about the general characteristics of spatial distributions of seeds in a seed bank is needed to identify the most efficient strategies for sampling. Seed banks were sampled on 8.4-m square grids in eight irrigated corn fields to identify the common features of distributions of seed banks of annual weeds. Spatial dependence was described with correlograms for four to eight species in each field. Spatial dependence was detected for 36 of 45 distributions, and seed counts were correlated to an average distance of 25 to 150 m for a distribution. Seed banks of different species and fields had common features of spatial correlation: spatial pattern accounted for less than half of the total variability of seed counts, spatial correlation decreased rapidly over short distances, and ranges of spatial dependence varied with direction. For half of the distributions, the maximum range of spatial dependence was at least twice as long as the minimum range. Seed counts were correlated for the longest distances in the direction of the crop row for 16 distributions, and the distance was longer in the direction of the crop row than across rows for 26 of the 36 samples. Researchers should be able to design more efficient sampling plans for growers if the common features of spatial dependence are considered. For seed banks like these, the accuracy of maps and estimates of seed bank density may be improved by collecting multiple cores around each sampling location to mitigate the effect of short-scale spatial variability. In addition, sampling may be more efficient with grids and interpolation methods that account for ranges that are 1.5 to 2 times longer in the direction of the crop row than perpendicular to the row. With a 55- by 30-m sampling grid, adjacent observations would be correlated, and maps could be made for 80% of these seed banks. More closely spaced observations would be needed to describe the rapid decline in spatial correlation with distance for a more accurate or finer-scale map. Whether sampling seed banks for making management decisions will be cost-effective is not clear. However, potential methods to sample and map seed bank distributions more efficiently have not been exhausted.

We investigated the effect of focused ion beam (FIB) imaging on the crystallinity of InAs using Raman scattering. A spatial correlation model was used to fit the broad band induced by FIB imaging. The fitting gives a correlation length of ~42 Å for the noisiest image condition (with an ion fluence of 7.4×1010 cm−2), implying severe damage in the surface layer of InAs. However, further increasing the fluence by several orders of magnitude only decreases the correlation length from 42 to 35 Å. We attribute the severe damage to the high beam current density and the low scanning speed of the FIB imaging process. These process conditions, along with low InAs thermal conductivity, also leads to a high local temperature in the exposed region that largely annihilated the defects and resulted in the nearly fluence-independent behavior.

Payments for environmental services (PES) have been recognized as a promising mechanism for conservation, with the potential to contribute to social objectives such as poverty reduction. This paper outlines a simple framework for assessing the potential for synergies in the implementation of PES programmes, used to analyse the new watershed conservation funding (WCF) channelled through Costa Rica's national PES programme, Pago por Servicios Ambientales (PSA). The WCF financing can only be used in a limited number of watersheds. Given this constraint, the paper examines the mechanisms by which the WCF may potentially contribute to biodiversity conservation and to reducing social development gaps. Although there is significant spatial correlation among the priority areas targeted for the objectives of watershed conservation, biodiversity conservation and social development, the availability of the WCF per unit of land in most watersheds is limited compared to the PSA programme's prevailing payment rate of US$ 64 ha−1, potentially hindering the impact of the WCF on conservation and social development. The analysis helps guide the allocation of the PSA budget in a way that complements the WCF and improves the cost-effectiveness of the PSA budget.

Assessing the abundance and distribution of mammalian carnivores is vital for understanding their ecology and providing for their long-term conservation. Because of the difficulty of trapping and handling carnivores many studies have relied on abundance indices that may not accurately reflect real abundance and distribution patterns. We developed statistical analyses that detect spatial correlation in visitation data from combined scent station and camera-trap surveys, and we illustrate how to use such data to make inferences about changes in carnivore assemblages. As a case study we compared the carnivore communities of adjacent communal and freehold rangelands in central Namibia. We used an index of overdispersion to test for repeat visits to individual camera-trap scent stations and a bootstrap simulation to test for correlations in visits to camera neighbourhoods. After distilling our presence-absence data to the most defensible spatial scale, we assessed overall carnivore visitation using logistic regression. Our analyses confirmed the expected pattern of a depauparate fauna on the communal rangelands compared to the freehold rangelands. Additionally, the species that were not detected on communal sites were the larger-bodied carnivores. By modelling these rare visits as a Poisson process we illustrate a method of inferring whether or not such patterns are because of local extinction of species or are simply a result of low sample effort. Our Namibian case study indicates that these field methods and analyses can detect meaningful differences in the carnivore communities brought about by anthropogenic influences.