Mulches on the soil surface are known to suppress weed emergence, but the quantitative relationships between emergence and mulch properties have not been clearly defined. A theoretical framework for describing the relationships among mulch mass, area index, height, cover, light extinction, and weed emergence is introduced. This theory is applied to data from experiments on emergence of four annual weed species through mulches of selected materials applied at six rates. Mulch materials, in order from lowest to highest surface-area-to-mass ratio, were bark chips, Zea mays stalks, Secale cereale, Trifolium incarnatum, Vicia villosa, Quercus leaves, and landscape fabric strips. The order of weed species' sensitivity to mulches was Amaranthus retroflexus > Chenopodium album > Setaria faberi > Abutilon theophrasti, regardless of mulch material. The success of emergence through mulches was related to the capacity of seedlings to grow around obstructing mulch elements under limiting light conditions. Mul...

The quantitative relationship between weed emergence and the physical properties of mulches

9 – Many Little Hammers: Ecological Management of Crop-Weed Interactions

The species composition and density of weed seed in the soil vary greatly and are closely linked to the cropping history of the land. Altering tillage practices changes weed seed depth in the soil, which plays a role in weed species shifts and affects efficacy of control practices. Crop rotation and weed control practices also affect the weed seedbank. Information on the influence of cropping practices on the weed seedbank should be a useful tool for integrated weed management. Decision aid models use information on the weed seedbank to estimate weed populations, crop yield loss, and recommend weed control tactics. Understanding the light requirements of weed seed may provide new approaches to weed management. Improving and applying our understanding of weed seedbank dynamics is essential to developing improved weed management systems. The principles of plant ecology must be integrated with the science of weed management to develop strategies that take advantage of basic plant responses in weed management systems for agronomic crops.

Implications of weed seedbank dynamics to weed management

In the effort of developing precision agriculture tools, remote sensing has been commonly considered as an effective technique for weed patch delineation, where weed infestations are detected based on variations in the plant canopy spectral response. Because the canopy spectral response is important for weed detection, discussions on the irradiative interaction of light in plant canopies and the effect of variable soil background on the canopy spectral response are presented in this review. Also, a presentation of the current techniques for removal of soil effects, including vegetation indices and spectral mixture analysis, shows that these techniques have not been adequately developed for use in remote sensing-based weed detection applications. Given the nature of light interaction in a plant canopy, this review proposes that the spectral response of a plant canopy depends on both the species and the biomass density. Remote detection of weeds from ground-, aircraft-, and satellite-based platforms has been accomplished on a wide scale, yet the use of these weed detection methods to make variable-rate herbicide applications has not occurred as often. By judging success based on variable-rate herbicide applications rather than precise weed localization, some of the current problems in weed sensing may be skirted.

A Review on Remote Sensing of Weeds in Agriculture

Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps—the third research contribution—which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

Bioeconomic Modeling to Simulate Weed Control Strategies for Continuous Corn (Zea mays)

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0043174500057982

Weed management decisions in corn based on bioeconomic modeling.

GWM: General Weed Management Model

An economic analysis of four weed management systems employed on four crop sequences in a barley-corn- pinto bean-sugarbeet rotation in eastern Colorado was com- puted. Weeds were controlled in each crop with only conven- tional tillage or conventional tillage plus minimum levels of herbicides (systems 3 and 4), moderate levels of herbicides (system 1), or intensive levels of herbicides (system 2). Ad- justed gross returns were higher for systems 3 and 4 where herbicide use was less/year and decreased over 4 yr than for systems 1 and 2 where herbicide use was higher/year and constant. When the four crop sequences were aggre- gated using yield and sucrose indices, the least herbicide- intensive weed management system had $440/ha/4 yr higher indexed adjusted gross return than the most herbicide- intensive weed management system. An income risk analysis showed that the herbicide-intensive weed management system was not risk efficient and that producers would select one of the other three less herbicide-intensive weed management systems depending upon their risk preferences. Nomenclature: Barley, Hordeum vulgare L. 'Steptoe'; corn, Zea mays L. 'Pioneer 3565'; pinto bean, Phaseolus vulgaris L. 'Olathe'; sugarbeet, Beta vulgaris L. 'Mono Hy A4'. Additional index words. Interdisciplinary weed research, Beta vulgaris, Hordeum vulgare, Phaseolus vulgaris, Zea mays.

Economic Analysis of Four Weed Management Systems

Bioeconomic models describe biological processes and predict the effects management decisions have on those processes. Bioeconomic models are used to improve understanding of complex production systems, to assess the effects of policies and new technologies, and to support farm-level decisions. This article examines design objectives for bioeconomic models of crop production and the implications of design objectives for model structure and function, technical challenges associated with bioeconomic model development, and problems and opportunities related to the use of bioeconomic models for direct support of crop production management decisions.

Bioeconomic Models of Crop Production Systems: Design, Development, and Use

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