Spatial and temporal patterns of Lolium rigidum–Avena sterilis mixed populations in a cereal field

Through a detailed case study of a two‐species (Lolium rigidum and Avena sterilis) weed community at contrasting scales, this paper examined factors that affect weed distribution across space and time in a commercial wheat field in north‐east Spain. A. sterilis showed relatively stable spatial distribution and spatial structure of its population over time at large scale, with well‐defined patches, although weed density rose quickly. L. rigidum showed poorly defined patches that were not stable across time. Interaction between species could explain to some degree the spatial distribution at large scale: a negative relationship was detected between the spatial structures of both weed populations. At fine scale, both species showed a clear interaction effect from primary dispersal (more important in A. sterilis) and secondary dispersal from combine harvesting (more important in L. rigidum).     

Boundary-fitted grid in landscape modeling

Landscape modeling requires the delineation of system boundaries and interior features. Quite often, these components are complex and difficult to accurately represent. A rectangular grid is used to represent the study and adjacent non-study areas in most cases. When the non-study area occupies a large portion of the grid, computer memory is wasted, and computational time increases. An elliptical grid generator for non-orthogonal curvilinear coordinates is used to generate a boundary-fitted grid for a landscape model. In a boundary-fitted grid coordinate system, one coordinate axis follows the landscape domain boundary and is non-orthogonal to the second axis. The boundary-fitted grid uses elliptic partial differential equations to distribute grid points inside the landscape domain. Although the boundary-fitted grid follows the domain boundary, the grid pattern and point allocation remain structured. Thus, a landscape model can use a boundary-fitted grid without changing the model’s data structure or the computational scheme. In this study, a boundary-fitted grid and a raster-based grid were applied to the Everglades Landscape Fire Model. Use of the boundary-fitted grid decreased model simulation time by about one fifth and computer storage by 58% relative to the raster-based grid. Also, the linear characteristics of interior geographical features such as rivers and airboat trails were preserved by the boundary-fitted grid, but not by the raster-based grid. This preservation provided a more reasonable base map for simulating ecological processes, such as fire across heterogenous landscapes.     

新疆农业水土资源时空匹配研究

水土资源是农业可持续发展的核心,水资源的合理利用会影响到土地的利用方式。为了分析新疆农业水土资源匹配状况,本文采用农业水土资源匹配系数对2008-2017年新疆14个地、州、市的水土资源时空匹配程度进行研究。研究结果表明:从时间上看,2008年到2017年,新疆农业水土资源匹配系数呈现出增加的趋势。从空间分布上看,北疆地区的农业水土资源匹配系数整体较低,南疆地区的农业水土资源匹配系数较高。通过对2008年和2017年新疆农业水土资源匹配系数的差值对比发现,克孜勒苏柯尔克孜自治州、阿勒泰地区、伊犁哈萨克自治州(直属)、乌鲁木齐市和和田地区的农业水土资源匹配系数的变化较明显;博尔塔拉蒙古自治州和喀什地区农业水土资源匹配系数降低最明显。     

How does industrial agglomeration affect regional innovation? A spatial econometric analysis

This study empirically investigates the direct and indirect (spillover) effects of three types of industrial agglomeration with different cognitive distances, namely, industrial specialization, industrial related variety, and industrial unrelated variety, on regional innovation. Accordingly, we applied the concepts of related and unrelated variety and used spatial econometric analysis. Based on panel data of 283 Chinese cities from 2004 to 2016, the empirical results reveal that both industrial specialization and industrial related variety significantly improve regional innovation and produce positive spatial spillover effects on the surrounding regions. However, industrial unrelated variety has a significant negative impact on regional innovation and produces a negative spatial spillover effect on the surrounding regions. Moreover, the direct and indirect effects of the three types of industrial agglomeration on regional innovation between China's coastal and inland cities. The findings have important theoretical and policy implications.     

Optimizing Phosphorus and Potassium Fertilizer Recommendations using Spatial Uncertainty Assessments

Abstract

Variable‐rate nutrient management relies on soil fertility maps, yet the associated uncertainty is typically ignored in developing recommendations. In this article, ordinary kriging (OK) and several alternatives that rely on local estimates of uncertainty derived via probability kriging (PK) are evaluated for developing phosphorus (P) and potassium (K) fertilizer recommendations, using soil data collected at two different intensities. A decision support framework that incorporates economic and agronomic criteria to derive block‐average optimal quantile estimates (Opt Q) maximized net return for an 8‐year fertilization program. The economic results show an increase of $7.31 ha^?1 and $1.04 ha^?1 for P and K fertilization, respectively, using Opt Q rather than OK with the 0.2‐ha sampling grid and $14.79 ha^?1 and $8.93 ha^?1 for P and K fertilization, respectively, using the 0.5‐ha sampling grid. These results illustrate the importance of accounting for estimation uncertainty in developing variable‐rate fertilizer recommendations.