SITE-SPECIFIC CROP MANAGEMENT: FILLING CRITICAL GAPS

Crop Production/Industries,

The Role of Sensing Devices in Herbicide Application: Present and Future

For as long as farming has been practiced, farmers have realized that yield, soil characteristics, and pest infestations vary across fields. Until recently, farmers have dealt with this variation by manually adjusting fertilizer applications according to fertility test results, and have spot sprayed pesticides to control localized pest populations. The technology to increase the ease and precision of such applications has increased tremendously in the past five years. Much of this progress can be attributed to increased computing capacity and speed. Several developments have occurred that will significantly impact the way in which weeds are controlled in the near future, and some of these technologies are already being used in the northern plains states. These new technologies can be broken in two groups, real-time weed detection, and what I\u27ll call historic or geographic information systems (GIS) data management of agricultural field information. The real-time sensors detect weeds and activate the sprayer based on a threshold criteria, or simply activate the sprayer every time a weed is detected. The GIS approach activates the liquid or dry bulk application based on soil physical and chemical properties, or on weed seedbank numbers

Economic Importance of Managing Spatially Heterogeneous Weed Population

Three methods of predicting the impact of weed interference on crop yield and expected economic return were compared to evaluate the economic importance of weed spatial heterogeneity. Density of three weed species was obtained using a grid sampling scheme in 11 corn and 11 soybean fields. Crop yield loss was predicted assuming densities were homogeneous, aggregated following a negative binomial with known population mean and k, or aggregated with weed densities spatially mapped. Predicted crop loss was lowest and expected returns highest when spatial location of weed density was utilized to decide whether control was justified. Location-specific weed management resulted in economic gain as well as a reduction in the quantity of herbicide applied

Economic Importance of Managing Spatially Heterogeneous Weed Population

Three methods of predicting the impact of weed interference on crop yield and expected economic return were compared to evaluate the economic importance of weed spatial heterogeneity. Density of three weed species was obtained using a grid sampling scheme in 11 corn and 11 soybean fields. Crop yield loss was predicted assuming densities were homogeneous, aggregated following a negative binomial with known population mean and k, or aggregated with weed densities spatially mapped. Predicted crop loss was lowest and expected returns highest when spatial location of weed density was utilized to decide whether control was justified. Location-specific weed management resulted in economic gain as well as a reduction in the quantity of herbicide applied

Slow Spread of the Aggressive Invader, Microstegium vimineum (Japanese Stiltgrass)

Microstegium vimineum (Japanese stiltgrass) is a non-native weed whose rapid invasion threatens native diversity and regeneration in forests. Using data from a 4 year experiment tracking new invasions in different habitats, we developed a spatial model of patch growth, using maximum likelihood techniques to estimate dispersal and population growth parameters. The patches expanded surprisingly slowly: in the final year, the majority of new seedlings were still within 1 m of the original patch. The influence of habitat was not as strong as anticipated, although patches created in roadside and wet meadow habitats tended to expand more rapidly and had greater reproductive ratios. The long-term projections of the patch growth model suggest much slower spread than has typically been observed for M. vimineum. The small scale of natural dispersal suggests that human-mediated dispersal, likely influenced by forest road management, is responsible for the rapid spread of this invasive species

Soil Electrical Conductivity Classification: A Basis For Site-Specific Management In Semiarid Cropping Systems

Site specific management (SSM) has the potential to improve both economic and ecological outcomes in agriculture. Effective SSM requires strong and temporally consistent relationships between identified management zones, underlying soil physical, chemical and biological parameters defining yield potential, and crop yield. In a farm-scale (250 ha) experiment in semiarid northeastern Colorado, each of eight 31-ha fields was individually mapped for soil apparent electrical conductivity (ECa) and classified into four management zones (ranges of ECa). Soil analyses revealed a strong negative relationship between ECa zones and soil parameters associated with innate fertility (P ≤ 0.06). The objective of the present study was to further evaluate ECa as a basis for SSM by examining its relationship to actual yield using two years of yield maps for winter wheat (Triticurn aestivum L.) and corn (Zea mays L.). Within field wheat yields were strongly related to ECa, particularly when regressing mean wheat yields within ECa classes against mean ECa within ECa classes (r2 = 0.95 to 0.99). Yield response curves revealed a boundary line of maximum yield that decreased with increasing EC . In this semiarid dryland system, ECa-based management zones can be used in the SSM of wheat for: (1) yield goal determination, (2) soil sampling to assess residual fertilizer concentrations and soil attributes affecting herbicide efficacy, and (3) prescription maps for metering fertilizer, pesticide and seed inputs. Inconsistent relationships were found between ECa and corn yields indicating that, while soil factors controlled wheat yields, corn yields were more influenced by weather

Strategies for the Diversity-Oriented Synthesis of Macrocycles.

Macrocycles have long been recognized as useful chemical entities for medicine, with naturally occurring and synthetic macrocycles clinically approved for use as prescription drugs. Despite this promise, the synthesis of collections of macrocycles has been historically challenging due to difficulties in the formation of large rings. Diversity-Oriented Synthesis (DOS) emerged in the early 2000s as a powerful strategic solution to the construction of diverse molecular libraries. This review details the various strategies developed within the field of DOS for the synthesis of macrocycle libraries, utilizing modern synthetic methodology to deliver structurally diverse collections of macrocyclic molecules, and the exploration of their therapeutic potential. Section 1 of this work details the use of algorithmic strategies and is divided into Build/Couple/Pair, Advanced Build/Couple/Pair, Initiate/Propagate/Terminate, Fragment-Based Domain Shuffling, Two-Directional Synthesis, and Successive Ring Expansion. Section 2 covers strategies based on ring distortion reactions, including Sequential Cycloaddition/Fragmentation, Ring Expansions, and Miscellaneous

A Unifying Gravity Framework for Dispersal

Most organisms disperse at some life-history stage, but different research traditions to study dispersal have evolved in botany, zoology, and epidemiology. In this paper, we synthesize concepts, principles, patterns, and processes in dispersal across organisms. We suggest a consistent conceptual framework for dispersal, which utilizes generalized gravity models. This framework will facilitate communication among research traditions, guide the development of dispersal models for theoretical and applied ecology, and enable common representation across taxonomic groups, encapsulating processes at the source and destination of movement, as well as during the intervening relocation process, while allowing each of these stages in the dispersal process to be addressed separately and in relevant detail. For different research traditions, certain parts of the dispersal process are less studied than others (e.g., seed release processes in plants and termination of dispersal in terrestrial and aquatic animals). The generalized gravity model can serve as a unifying framework for such processes, because it captures the general conceptual and formal components of any dispersal process, no matter what the relevant biological timescale involved. We illustrate the use of the framework with examples of passive (a plant), active (an animal), and vectored (a fungus) dispersal, and point out promising applications, including studies of dispersal mechanisms, total dispersal kernels, and spatial population dynamics

A Unifying Gravity Framework for Dispersal

Most organisms disperse at some life-history stage, but different research traditions to study dispersal have evolved in botany, zoology, and epidemiology. In this paper, we synthesize concepts, principles, patterns, and processes in dispersal across organisms. We suggest a consistent conceptual framework for dispersal, which utilizes generalized gravity models. This framework will facilitate communication among research traditions, guide the development of dispersal models for theoretical and applied ecology, and enable common representation across taxonomic groups, encapsulating processes at the source and destination of movement, as well as during the intervening relocation process, while allowing each of these stages in the dispersal process to be addressed separately and in relevant detail. For different research traditions, certain parts of the dispersal process are less studied than others (e.g., seed release processes in plants and termination of dispersal in terrestrial and aquatic animals). The generalized gravity model can serve as a unifying framework for such processes, because it captures the general conceptual and formal components of any dispersal process, no matter what the relevant biological timescale involved. We illustrate the use of the framework with examples of passive (a plant), active (an animal), and vectored (a fungus) dispersal, and point out promising applications, including studies of dispersal mechanisms, total dispersal kernels, and spatial population dynamics